Euclid Quick Data Release (Q1). A systematic study of the intracluster light fraction at 0.2 < z < 1.5

Introduction

The intracluster light (ICL) is the diffuse stellar component of galaxy clusters that is gravitationally bound to the cluster potential but not to individual galaxies [133, 95]. Observationally, it is characterised by low-surface-brightness (LSB) emission extending beyond the central brightest cluster galaxy (BCG). ICL is of particular interest because it acts as a fossil record of cluster assembly and the redistribution of stellar mass during hierarchical growth [100, 72] and because its spatial distribution closely traces the shape of the underlying dark matter halo in a cold dark matter (CDM) universe [103, 5, 129, 65]. As such, the properties of ICL may be used as a proxy for the cluster mass [4], dynamical state [77, 81], and orientation [65], and its presence or absence may help refine cluster catalogues by flagging misidentified clusters.

A commonly used summary statistic to quantify the amount of ICL in a cluster is the ICL fraction, which is defined as the ratio of ICL light (or stellar mass) to the total cluster stellar light (or stellar mass). Measurements of the ICL fraction span a wide range, from as low as a few per cent [23, 77] to 50% [87, 94, 72], potentially depending on cluster mass, observational depth, the adopted ICL definition, and the radial extent over which ICL is measured. However, the dependence on these factors remains uncertain. For example, there is ongoing debate regarding the dependence of the ICL fraction on cluster mass, with some observation and simulation-based studies finding a correlation between the ICL fraction and cluster mass (e.g., [26, 2, 80]), while others report weak or no clear trend (e.g., [36, 111, 21]). Differences in the methodology adopted to define and isolate ICL, as well as the radial extent over which it is measured, can also contribute to variations between reported ICL fractions (e.g., [20]).

Comparisons of ICL fractions between different studies are therefore challenging. This becomes particularly prevalent when measuring the evolution of the ICL fraction with redshift, which provides a direct observational link between ICL formation mechanisms and the overall growth of clusters (e.g., [79]). However, current studies focus on measurements in discrete redshift intervals: either low redshifts [132, 74, 120, 111], intermediate redshifts [102, 69, 72], or high redshifts [23, 79, 78], with little overlap between different epochs. Even between studies at similar epochs, there are major discrepancies. For example, as discussed by Montes [100], measurements at $z < 0.5$ show inconsistent trends, with some studies reporting a strong increase in the ICL fraction towards lower redshift (e.g., [24, 69, 27]), while others find substantially weaker trends (e.g., [85, 102]), or no trend at all ([77, 130]).

In this work, we focus on aperture-based measurements of the ICL fraction, which provide a relatively simple and reproducible method that is readily applicable to large cluster samples and commonly adopted in observational studies. This approach also enables a controlled exploration of how specific methodological choices influence the inferred ICL fraction. Such an analysis requires a well-characterised cluster sample spanning a broad redshift range, together with deep, homogeneous imaging. The Euclid mission (Euclid Collaboration: Mellier et al. [60]) provides a unique opportunity in this regard. Its combination of a stable point-spread function (PSF) and uniform depth over large contiguous areas enables robust LSB measurements (Euclid Collaboration: Borlaff et al. [54]), while its near-infrared imaging allows ICL to be measured in the rest-frame optical beyond $z = 1$.

Using Euclid images, we measured the ICL fraction for a sample of approximately 300 galaxy clusters spanning $0.2 < z < 1.5$. We applied a homogeneous fiducial methodology across the full redshift range in order to place improved constraints on the evolution of the ICL fraction and to compare it with previously reported trends. In particular, our analysis enabled a direct test of the conclusion of Joo & Jee [79] that the ICL fraction shows little or no evolution out to $z > 1$, by extending this measurement to a larger sample, a broader redshift range, and a consistent observational approach. We also systematically investigated the impact of several methodological choices within the aperture-based approach, including the adopted photometric band, radial aperture, and stacking estimator, thereby quantifying the sensitivity of the inferred ICL fraction and its redshift evolution to the adopted measurement methodology.

This paper is organised as follows. In Sect. 2 we describe the Euclid imaging, catalogues, and the different cluster samples used in this analysis. In Sect. 3, we present the methodology used to measure the ICL fraction. We present our results in Sect. 4, and discuss their implications for ICL formation and evolution in Sect. 5.

For this analysis, we assumed a flat $\Lambda$CDM universe, with $\Omega_{\mathrm{m}} = 0.30$, $\Omega_{\Lambda} = 0.70$, and $H_0 = 70\ \mathrm{km\ s^{-1}\ Mpc^{-1}}$. We primarily adopted halo masses defined within a spherical overdensity of 500 times the critical density of the Universe, $M_{500}$, with corresponding radius $r_{500}$ and projected radius $R_{500}$, but we also referred to $M_{200}$ and $R_{200}$ where relevant.

Data

Euclid Quick Data Release 1

The Euclid Quick Data Release 1 (Q1; Euclid Quick Release Q1 2025) is the first public data set from the Euclid mission. It consists of a single reference observing sequence (ROS; Euclid Collaboration: Scaramella et al. [62]) targeting the three Euclid Deep Fields: North (EDF-N), South (EDF-S), and Fornax (EDF-F). These span areas of approximately 23 deg$^2$, 28 deg$^2$, and 12 deg$^2$, respectively (Euclid Collaboration: Mellier et al. [60]; Euclid Collaboration: Aussel et al. [50]). This release provides calibrated single exposures in the $I_{\mathrm{E}}$ band from the VIS instrument (Euclid Collaboration: McCracken et al. [59]; Euclid Collaboration: Cropper et al. [55]), and $Y_{\mathrm{E}}$, $J_{\mathrm{E}}$, and $H_{\mathrm{E}}$ bands from the NISP instrument (Euclid Collaboration: Jahnke et al. [57]; Euclid Collaboration: Polenta et al. [61]). It also provides photometric redshift estimates and galaxy physical properties delivered by OU-PHZ (Euclid Collaboration: Tucci et al. [63]).

To optimise the images for LSB science, we used the custom pipeline described in Euclid Collaboration: Bamford et al. [51] to further reprocess the calibrated frames. Starting from the standard LE2 data products, we applied an atemporal background correction and removed a ‘tartan’ pattern and persistence features from the NIR images. We then resampled the images to a common $0.3$ pixel grid, matched the background level on each detector, and finally combined the individual exposures into a weighted-mean combined frame. Full details of each of these processes are given in the appendices of B26. The use of this custom pipeline enables reliable detection of surface-brightness levels of around 27.5 mag arcsec$^{-2}$ in $Y_{\mathrm{E}}$, $J_{\mathrm{E}}$, $H_{\mathrm{E}}$, and 29 mag arcsec$^{-2}$ in $I_{\mathrm{E}}$, on scales of several arcminutes.

Cluster samples

We make use of two complementary cluster samples drawn from millimetre and optical/near-infrared surveys. The first is based on observations with the South Pole Telescope (SPT; Carlstrom et al. [28]). The second comes from the Euclid mission itself. Additionally, we make use of a simulated sample of clusters from the Hydrangea cosmological hydrodynamic simulations (Bahé et al. [9]) to provide a theoretical reference for interpreting the observed ICL properties.

SPT clusters

The third generation camera of the South Pole Telescope (SPT-3G; Benson et al. [12]; Sobrin et al. [119]) is capable of detecting galaxy clusters via the thermal Sunyaev–Zeldovich (SZ) effect (Sunyaev & Zeldovich [122]). Unlike optical/infrared and X-ray detection methods, the SZ signal does not suffer from cosmological dimming, making it particularly powerful for detecting clusters out to high redshifts ($z > 1$).

Archipley et al. [8] carried out observations of a 57 deg$^2$ region overlapping the EDF-S, totalling 20 days, and they provided a catalogue of 217 SZ-detected clusters, 121 of them within the EDF-S footprint. Although Archipley et al. [8] estimate cluster redshifts and masses, we opt to re-estimate these ourselves in Appendix A, using the Euclid Q1 data to ensure homogeneity between our cluster samples. Appendix A also details the identification of BCGs and the removal of problematic clusters, yielding a final sample of 66 SPT clusters for the analysis presented in this work.

Euclid clusters

Euclid Collaboration: Bhargava et al. [53] present the first cluster detections of the Euclid mission that cover the 63 deg$^2$ of the three EDFs, using two algorithms: AMICO (Bellagamba et al. [11]; Maturi et al. [91]); and PZWav (Thongkham et al. [123]). The joint cluster catalogue, requiring detection with both algorithms, contains 426 clusters across the three fields. In order to ensure homogeneity with the SPT sample of clusters, we opt to estimate cluster redshifts ourselves in Appendix A. Following the identification of BCGs and removal of problematic clusters, as described in Appendix A, the final Euclid sample used for the analysis in the work consists of 269 clusters.

As discussed by Archipley et al. [8], a number of clusters in the SPT and Euclid samples are the same. In this work, we retain all clusters from both samples and carry out our BCG identification procedure (Appendix A) independently for each. Duplicates are naturally revealed through this process, showing 36 clusters that exist in both samples. These systems are retained in both samples throughout the analysis, since removing them from either catalogue would alter the sample selection and potentially bias comparisons between the SPT and Euclid cluster populations.

Simulated clusters

The Hydrangea simulations (Bahé et al. [9]) are a suite of zoom-in cosmological hydrodynamical simulations of galaxy clusters built on the EAGLE model of galaxy formation (Schaye et al. [117]). These clusters were drawn from a 3.2 Gpc parent simulation (Barnes et al. [10]) and chosen to span a similar redshift range as the observed clusters analysed in this work ($0.2 < z < 1.5$; Figure 1). To enable a direct comparison with the SPT sample, we defined a mass-matched simulated sample by applying a redshift-dependent upper and lower mass threshold that traces the observed SPT locus in the mass–redshift plane. The resulting mass-matched sample contains 181 simulated clusters spanning the redshift range $0.2 < z < 1.5$, corresponding to 12 unique Hydrangea clusters observed at multiple simulation snapshots. Figure 1 shows cluster mass versus redshift for the SPT cluster sample and the mass-matched simulated sample. We also show the redshift and mass distributions of each of the cluster samples used in this work as probability density functions (PDFs) above and to the right of the main axes.

To provide a controlled reference for our measurements, we constructed background-free mock images for each projected cluster. These images represent the intrinsic light distribution of each system and are used as a baseline for comparison with observational measurements. Two versions are generated in each case: one containing only the combined BCG+ICL stellar component; and a second that additionally includes the contribution from satellite galaxies. This decomposition is performed directly at the particle level in three dimensions in the simulations, within $3\,r_{200}$ of the most bound particle. Satellite systems are first identified using Subfind (Dolag et al. [46]), which detects subhaloes within friends-of-friends structures and isolates gravitationally bound overdensities. These identifications are subsequently refined using Cantor (Bahé et al. in prep), which improves the assignment of stellar particles to individual subhaloes. Particles associated with these substructures are classified as satellites, while all remaining stellar particles are attributed to the BCG+ICL component. Flux from poorly resolved satellites (with stellar mass $\lesssim10^{8}M_{\odot}$; Brown et al. [21]) may mildly contaminate the non-satellite components, and it is known that Subfind can truncate satellite galaxies by transferring unbound material in their outskirts to the cluster central structure, though Cantor is designed to improve on this issue (Brown et al. [21]; Bahé et al. in prep).

Mock Euclid-like observations were then constructed for each cluster along the three principal Cartesian projections ($xy$, $xz$, and $yz$), following the methodology described by Martin et al. [88], resulting in 181 $\times$ 3 mock observations for the mass-matched sample. The images were generated with a pixel scale of 0.3 arcsec pixel$^{-1}$, matching the resolution of our LSB Euclid images. Stellar particles were assigned spectral energy distributions (SEDs) based on their individual ages and metallicities. These SEDs were integrated through the Euclid $I_{\mathrm{E}}$-band

transmission curve to compute apparent fluxes. Prior to projection, particles were adaptively smoothed using a Gaussian kernel with width set by the 3D distance to their $k$th nearest neighbour. This was done to produce a more physically realistic representation of the stellar mass distribution. The resulting images were then convolved with the large-scale Euclid $I_{\mathrm{E}}$-band PSF (Cuillandre et al. [43]). We produced these mock observations in the rest-frame $I_{\mathrm{E}}$ band in order to provide a direct comparison to the real observations, for which we standardise to rest-frame $I_{\mathrm{E}}$ in Sect. 3.4.

Method

BCG and ICL

We first estimated cluster redshifts and identified the likely BCG for each cluster. To ensure that the analysis was applied consistently to both the SPT and Euclid samples, we derived cluster association scores, richnesses, and BCG identifications using a common procedure, rather than adopting the Euclid cluster catalogue values, which are not available for the SPT clusters. The method for this is presented in Appendix A. The resulting quality cuts and BCG identification procedure reduced both samples relative to their parent catalogues. Consequently, the final sample is not expected to be fully representative of the parent cluster populations, and the results presented here should be interpreted as applying to the subset of clusters for which robust BCG identification and ICL measurements are possible. Following the BCG identification, we used the LSB Euclid $I_{\mathrm{E}}$, $Y_{\mathrm{E}}$, $J_{\mathrm{E}}$, and $H_{\mathrm{E}}$ images of each cluster (processed with the pipeline presented in B26) to measure the ICL, BCG and galaxy light of each cluster.

We followed the ICL measurement workflow outlined in detail in Appendix § 10 of B26; here we provide a brief summary of the method. We first performed a global background subtraction measured using photutils.Background2D (Bradley et al. [19]), with a background box size corresponding to 1 Mpc at the cluster redshift, followed by a $3 \times3$ median filter. To prevent contamination of the background by ICL, background estimation was performed on masked images in which sources (including the BCG and ICL) were excluded.¹ The background was subtracted from each image, producing the measurement images used for all analyses hereafter. We then constructed three masks: a bad-pixel mask, a regular-objects mask (sources outside the ICL region, grown iteratively to 50% larger than their detected size), and a central-objects mask (compact sources within the ICL region, grown to 25% larger). Masks were created separately for the $I_{\mathrm{E}}$ and coadded $YJH_{\mathrm{E}}$ images.

To measure BCG+ICL photometry, we fitted elliptical isophotes to the masked $H_{\mathrm{E}}$-band images² using the nonparametric light profile extraction tool AutoProf (Stone et al. [121]), taking the BCG coordinates identified in Appendix A as the fixed centres. AutoProf derives the position angle and ellipticity of the isophotes by performing a Fourier analysis of the flux within apertures, iteratively adjusting the position angle and ellipticity for each isophote until convergence is achieved. The isophote shapes are fit out to where the surface brightness per pixel drops to the $1\sigma$ background noise level (as measured within AutoProf). To define isophotes beyond this, where the measurements become unreliable, the shapes of the isophotes are fixed at the final reliable values.

Using these isophotes, measured on the $H_{\mathrm{E}}$-band images, we extracted surface brightness profiles in the masked images for all filters by computing the sigma-clipped median flux within each annulus. The use of the median reduces sensitivity to residual contamination from imperfect masking. The median surface brightness was then multiplied by the annular area to obtain the total flux in each annulus. We constructed cumulative flux profiles and interpolated them to estimate the enclosed flux within elliptical apertures. The BCG light was defined as the total flux within an elliptical aperture that has semi-major axis $a = 50$ kpc, and the ICL was measured as the flux enclosed by elliptical apertures with semi-major axes of 50 and 200 kpc. These apertures were chosen following the optimisation of ICL measurements from Euclid Collaboration: Bellhouse et al. [52].

To estimate the uncertainty on integrated fluxes measured within either apertures or annuli, we adopted an empirical approach based on measurements in blank-sky regions. For each EDF, we selected $2.5 \times2.5$ sky patches chosen to contain minimal cluster emission, and placed sets of concentric elliptical apertures at 1000 random pointings (all with ellipticity set to 0.5). Using the same masking and background-subtraction procedure as applied to the cluster measurements, we measured the median surface brightness within each annulus for each random pointing.

The sigma-clipped median surface brightness in each annulus was converted to an integrated flux by multiplying by the corresponding annular area, and these annular fluxes were cumulatively summed to construct aperture flux profiles for each random sky pointing. The flux between any pair of inner and outer radii (e.g., 0–200 kpc and 50–200 kpc) was then computed by differencing the interpolated aperture fluxes at those radii. The corresponding $1\sigma$ uncertainty was taken as the sigma-clipped mean across all random pointings. For measurements involving multiple apertures or annuli (e.g., the ICL fraction), the covariance matrix was estimated from the ensemble of random realisations, allowing us to propagate the noise correctly and account for correlations between apertures.

To measure the BCG and ICL fluxes in the simulated clusters, we applied the same measurement procedure described above. However, no masking or background subtraction was required because the simulated images contain only stars belonging to the BCG and ICL.

The galaxy population

Cluster redshifts were estimated from photometric redshift overdensities, and cluster association scores were then derived using these redshift distributions together with projected spatial information. These were used to identify likely cluster members and to select the BCG. The cluster association scores are also used to exclude unlikely members in the galaxy selection and contamination correction steps described below. Full details of the method are described in Appendix A.

We measured the galaxy light within clusters by summing the flux from all galaxies except definite non-members within the same 200 kpc elliptical aperture as we do for the BCG+ICL, followed by a contamination correction. We defined definite non-members as sources with association scores $M_i < 1\%$. This step removes contaminants such as stars and galaxies with vastly different redshifts to the cluster. The fluxes used were measured from single-Sérsic model fits in all four Euclid bands, as provided in the Euclid source catalogues. We performed the contamination correction to account for residual contamination from foreground and background galaxies. While cluster association scores down-weight unlikely members, the choice of threshold is inherently arbitrary and may vary with redshift. A contamination correction provides an empirical correction that ensures that the inferred cluster galaxy light is not biased by a specific choice of membership cut.

To estimate the contribution of foreground and background galaxies, we constructed a per-cluster analogue sample by applying the same redshift and spatial selection used for the cluster members (Appendix A for details) to galaxies drawn from the field. Specifically, we selected galaxies consistent with the cluster redshift and applied the same spatial weighting to identify associated sources around these randomly chosen galaxies. Repeating this procedure for 1000 such analogues per cluster yields a distribution of flux measurements that represents the expected contribution from unrelated galaxies. For each of these analogues, we measured galaxy fluxes within the same elliptical apertures as used to measure the BCG+ICL. The medians of these distributions were adopted as the galaxy contamination fluxes, while the 16th–84th percentile range provided an estimate of the galaxy flux uncertainty. This contamination estimate is specific to each cluster, accounting for its redshift, selection criteria, and aperture geometry.

For the simulated clusters, we measured the galaxy light differently because the mock observations do not provide Euclid-like photometric redshifts, and therefore do not allow us to apply the same definite non-member selection or statistical contamination correction. Instead, we summed the pixel values in the satellite-only mock images within the $a = 200$ kpc isophote fitted to the corresponding BCG+ICL-only images.

Stacking

We constructed stacked cluster images to characterise the average ICL properties of our sample. The SZ-selected clusters from SPT are approximately mass-limited, whereas the optically selected clusters from Euclid are not. For this reason, we treat the SPT and Euclid clusters as separate populations throughout the stacking analysis. The majority of the SPT clusters span a relatively narrow range in halo mass, with most systems lying within a fairly narrow interval of $14.2 \lesssim\log_{10}(M_{500}/M_{\odot}) \lesssim14.6$. As a result, further binning by mass was not required for this subsample, although we do exclude five clusters with high masses, $\log_{10}(M_{500}/M_{\odot}) > 14.6$. The Euclid clusters span a broad range of richness. Since richness can act as a proxy for halo mass (e.g., Andreon & Bergé [7]; Simet et al. [118]; McClintock et al. [93]), we excluded the small number of clusters forming a distinct high-richness tail of the distribution to prevent them from disproportionately influencing the stacked signal. Specifically, we excluded clusters with richness greater than 150, removing 13 clusters. We estimated cluster richnesses by summing the number of galaxies with cluster association scores $M_i > 50\%$. For the SPT clusters, we created three bins in redshift, chosen to select a similar number of clusters in each bin. These bins have redshifts of $0.20 \leq z < 0.58$, $0.58 \leq z < 0.95$, and $0.95 \leq z < 1.46$, and contain 20, 20, and 21 clusters, respectively. For the Euclid clusters, we created five bins, which have redshifts of $0.22 \leq z < 0.45$, $0.45 \leq z < 0.58$, $0.58 \leq z < 0.71$, $0.71 \leq z < 0.93$, and $0.93 \leq z < 1.43$, and contain 51, 51, 51, 51, and 52 clusters, respectively.

For each cluster, we began with the background-subtracted, masked images centred on the BCG. All images were rotated so that the cluster major axis is aligned before stacking. The position angle used for this rotation was derived from the isophotal analysis of the $H_{\mathrm{E}}$ images. Specifically, we interpolated the measured position angle profile to a physical radius of 100 kpc and rotated each image so that this angle was aligned horizontally. We chose 100 kpc as a representative inner-cluster radius where the isophotal position angle can be measured robustly, while remaining comfortably within the ICL region. To account for the impact of the PSF, we additionally performed an image deconvolution, as described in Appendix § 10.

To estimate uncertainties on the stacked measurements and to reduce sensitivity to individual clusters, we adopted a bootstrap resampling approach. For each bootstrap realisation, clusters were randomly resampled with replacement, and their rotated images were stacked. We did not perform rescaling of the images when stacking, as $R_{500}$ measurements between clusters span a relatively narrow range due to our selection in redshift and mass. This process was repeated 100 times per bin, allowing us to capture the statistical uncertainty associated with sample variance. Two complementary stacking estimators were computed: a pixel-wise median, which is less sensitive to extreme values; and a $3\sigma$-clipped mean, which averages the signal after removing outlying pixels. Using both estimators allows us to assess the robustness of the results to the choice of stacking method. We find that our results and conclusions are insensitive to the choice of stacking estimator, and therefore present only the results computed using the pixel-wise median hereafter. To account for varying coverage and masking across individual images, we constructed a weight map for each stack, defined as the fraction of contributing images with valid (unmasked) pixels at each position. Pixels with a weight below 25% were excluded from the final stacked images to ensure that the measured signal was not dominated by a small subset of clusters. The resulting stacked images therefore represent a statistically robust estimate of the average cluster light distribution within each bin.

We followed the same procedure described in Sect. 3.1 to measure the BCG and ICL in the stacked images. For each stack, we fitted elliptical isophotes to define the aperture geometry, from which the 50 kpc and 200 kpc apertures used to measure the BCG and ICL components were determined. These steps were performed for all bootstrap realisations. To ensure we are not biased by outliers, the final measurements for the stacks were taken as the median of all bootstrap realisations, with uncertainties estimated from the 16th–84th percentile range.

To measure the galaxy light in the stacks, we adopted the 200 kpc aperture from the corresponding stacked image and applied it to each individual cluster contributing to that stack. The flux was then measured following the procedure outlined in Sect. 3.2, including the galaxy contamination correction. We took the median across the individual clusters to obtain the galaxy light for the whole stack. This was done for each bootstrap realisation. The properties of the stacks are summarised in Table D.1.

Rest-frame standardisation

To compare ICL fractions consistently across clusters at different redshifts, we interpolated the integrated fluxes to a common rest-frame reference wavelength corresponding to the $I_{\mathrm{E}}$ band ($0.71\ \mu\mathrm{m}$). For each cluster, we first extracted the total flux within the BCG ($a < 50$ kpc), ICL ($50\ \mathrm{kpc} < a < 200$ kpc), and member-galaxy ($a < 200$ kpc) components in all available observed bands. We linearly interpolated the flux of each component separately to the target rest-frame $I_{\mathrm{E}}$ wavelength, and then converted to rest-frame luminosity, following Hogg et al. [75].

Results

Cluster-component luminosities

The rest-frame $I_{\mathrm{E}}$ luminosities for each cluster component are shown in Figure 2, and summarised in Table D.1. We find that the luminosities of the BCG and ICL components remain relatively constant with redshift, with the ICL luminosities in good agreement with Kluge et al. [82]. In contrast, the galaxy light becomes fainter towards lower redshifts, broadly consistent with passive stellar-population evolution, whereas the BCG and ICL do not show comparable fading.

The simulations reproduce this overall behaviour, exhibiting a similar evolution in the luminosities of the BCG, ICL, and satellite galaxy components across the full redshift range. Since we show the mass-matched simulated sample here, the clearest and most direct comparison to the observations is with the SPT stacks. For the BCGs, we find very good agreement between the SPT stacks and simulations, with the largest offset around 0.1 dex at the highest redshift. We find a larger, systematic offset between the SPT stacks and simulations for ICL, with the simulations exhibiting around 0.2–0.3 dex brighter ICL than the SPT stacks at all redshifts. We find that the satellite galaxies in the simulations are systematically fainter than in the SPT stacks, by around 0.3 dex at the lowest redshift, with this offset decreasing to 0.1 dex at the highest redshift.³ The inferred luminosities of each component depend not only on the underlying stellar mass distributions but also on assumptions regarding stellar population synthesis models, stellar ages and metallicities, the initial mass function, dust attenuation, and the construction of the mock observations themselves (e.g., Conroy et al. [34]; Conroy [33]). This therefore prevents us from determining whether the normalisation offsets arise from the simulations, observations, or mock-observation assumptions.

Figure 2 also shows that the cluster-to-cluster scatter differs significantly between the three components. The BCG luminosities exhibit relatively small scatter, consistent with their use as standard candles (Postman & Lauer [109]), while the ICL and satellite galaxy luminosities show substantially broader distributions. Furthermore, the observed scatter is generally larger than that seen in the simulations. This could reflect a combination of greater intrinsic variation in the assembly histories of real clusters and additional observational uncertainties, particularly for the more challenging ICL measurements.

Detectability of ICL

The detectability of ICL in our sample is summarised in Figure 3, which shows the signal-to-noise ratio (S/N) of the measured ICL flux as a function of redshift for both individual clusters and stacked measurements. A substantial fraction of clusters have ICL measurements at S/N $> 3$ (within the 50–200 kpc isophotes), confirming the capability of the Euclid Wide Survey imaging and our reprocessing pipeline (B26) to recover ICL in individual clusters. In all redshift bins, the stacked measurements yield high-S/N detections of ICL, demonstrating that the signal is robustly recovered when combining clusters.⁴

We now use these measurements to also address a more fundamental question: the prevalence of ICL in clusters across the observed redshift range. The most robust test for this is provided by the 36 clusters that are independently detected in both the SPT and Euclid catalogues. These systems are confirmed massive clusters selected through two entirely different techniques and therefore represent the highest-purity subset of our sample. Of these clusters, 30 (83%) exhibit ICL detections with S/N $> 3$ and 33 (92%) with S/N $> 2$. ICL is therefore extremely common within confirmed clusters across the redshift and mass range studied here. However, it is not equally prominent in all systems. Six of these clusters have ICL detections below S/N $= 3$. These systems span a range of redshifts and, based on a visual inspection, appear to be genuine clusters with clearly identifiable BCGs. Their low ICL S/N therefore cannot be explained solely by obvious misidentification or by their location at the highest redshifts. Instead, they suggest that some clusters host intrinsically weak diffuse light components that fall below the detection threshold.

The full SPT sample supports this picture. Across all redshifts, 71% of clusters exhibit ICL detections with S/N $> 3$ (88% with S/N $> 2$). The Euclid-selected sample exhibits a substantially lower detection rate. Only 54% of clusters have ICL detections with S/N $> 3$ (68% with S/N $> 2$). Furthermore, the bottom panel of Figure 3 shows that the fraction of clusters with S/N $< 2$, which do not have even a marginal detection, increases strongly with redshift for the Euclid sample, from 5% at $z < 0.5$ to 56% at $z > 0.8$. At face value, this could be interpreted either as evidence for a strong decrease in ICL prevalence with redshift, or that the detection of ICL becomes increasingly difficult towards higher redshifts. However, such an interpretation is difficult to reconcile with the behaviour of the independently selected SPT sample, which spans a similar redshift range and shows no comparable decline.

To investigate this further, we constructed stacks using only Euclid clusters with individual ICL detections below S/N = 2.5. Four stacks were created, each containing 19 clusters, comparable to the number of clusters contributing to the SPT stacks. If these systems hosted genuine but individually undetectable ICL, stacking should recover a significant signal. Figure 3 shows that the three highest-redshift stacks remain below S/N = 2, indicating no significant recovery of diffuse light even after stacking. The lowest-redshift stack has S/N ∼ 5. However, this remains substantially weaker than the two SPT stacks at similar redshifts, which achieve ICL detections of S/N = 25–70 despite containing a similar number of clusters. This result suggests that a small fraction of the low-S/N Euclid systems may indeed host genuine but weak ICL components. Nevertheless, the overall behaviour of the low-S/N stacks indicates that a substantial fraction of these systems are not simply ordinary clusters with ICL lying marginally below the detection threshold.

While a visual inspection found no obvious evidence that these systems are spurious, the population is likely heterogeneous and may include a combination of genuinely weak ICL, incorrect BCG identifications, lower-mass systems such as galaxy groups, and projection-driven cluster candidates that do not correspond to a single massive virialised halo. Since there are no individual cluster mass estimates for the Euclid sample, we are unable to perform a direct comparison of the mass distributions between the SPT and Euclid samples. We therefore cannot exclude differences in the underlying mass distributions of the Euclid and SPT samples as a contributor to the differences observed between them.

Motivated by the analysis on the low-S/N stacks, we defined an alternative high-S/N Euclid subsample that excludes clusters with individual ICL detections below S/N = 2, while still including marginal detections with 2 < S/N < 3. We created five bins, which have redshifts of 0.22 ≤ $z$ < 0.41, 0.41 ≤ $z$ < 0.52, 0.52 ≤ $z$ < 0.64, 0.64 ≤ $z$ < 0.84, and 0.84 ≤ $z$ < 1.43, and contain 35, 35, 35, 36, and 36 clusters, respectively. Such a selection may introduce biases, since the probability of obtaining an individual ICL detection depends on redshift, cluster properties, and data quality. Excluding low-S/N systems therefore risks preferentially removing clusters from particular regions of parameter space. For this reason, we present results for both the full Euclid sample and the high-S/N subsample, allowing the impact of this selection to be assessed directly. Figure 3 shows that the high-S/N Euclid stacks exhibit similar S/N values as the full Euclid stacks at $z$ < 0.6, and higher S/N values at $z$ > 0.6, despite containing substantially fewer clusters.

Diffuse-component ICL fraction

A key quantity for understanding the build-up of diffuse light in clusters is the fraction of stellar light contained within ICL relative to the central galaxy, which we term the diffuse-component ICL fraction, $f_{\mathrm{diff}}$, but can also be thought of as a concentration. It is calculated from the fluxes of each component ($F_{\mathrm{BCG}}$ and $F_{\mathrm{ICL}}$) as

and is shown as a function of redshift in Figure 4. Individual clusters exhibit significant cluster-to-cluster variation in their measured fractions. Although a minority of clusters have weak or undetected ICL (Sect. 4.2), the majority show significant, non-zero values of $f_{\mathrm{diff}}$, demonstrating that meaningful measurements are achievable on a cluster-by-cluster basis, even at the highest redshifts in our sample.

The stacked measurements provide a clearer view of the underlying trend (see also Table D.1). In each redshift bin, $f_{\mathrm{diff}}$ is robustly constrained, with uncertainties derived from bootstrap realisations. For the SPT stacks, $f_{\mathrm{diff}}$ remains constant across the full redshift range, with values ranging from 37% to 40%. The full Euclid stacks have slightly lower fractions, ranging from 32% to 36%, although the uncertainties overlap with the SPT measurements, indicating no statistically significant offset between the two samples. The high-S/N Euclid stacks exhibit systematically larger values of $f_{\mathrm{diff}}$ than the full Euclid stacks, reaching values of 38% to 45%, and also appear to show a modest increase towards higher redshift.

To quantify the significance of these trends, we compared the stacked measurements to a null hypothesis in which $f_{\mathrm{diff}}$ remains constant over the observed redshift range. We fitted a constant fraction to the stacked measurements and evaluated the goodness-of-fit of this model using the resulting $\chi^2$ statistic. The corresponding probability for the constant-fraction hypothesis was converted into an equivalent Gaussian significance, providing a measure of the extent to which a constant $f_{\mathrm{diff}}$ model is disfavoured. The SPT stacks are fully consistent with a constant value of $f_{\mathrm{diff}}$, while the full Euclid stacks show a very small deviation from the constant model (0.4 $\sigma$). The high-S/N Euclid sample exhibits the largest deviation (1.8 $\sigma$), but it does not reach statistical significance. We therefore consider the observational results consistent with values of $f_{\mathrm{diff}}$ that remain constant with redshift. Any evolution is small compared to the cluster-to-cluster scatter.

The simulations exhibit systematically higher values of $f_{\mathrm{diff}}$ than the observations at 45% to 55% across the full redshift range. However, as discussed in Sect. 3.4, modest offsets in the relative luminosities of the BCG and ICL components naturally propagate into $f_{\mathrm{diff}}$. Despite this difference in normalisation, the simulations and observations show broadly similar behaviour with redshift, with neither exhibiting strong evidence for rapid evolution over the redshift range probed. Applying the same constant-model test to the redshift-binned simulated medians, we find that they are fully consistent with a constant value of $f_{\mathrm{diff}}$, with the observed variation in the medians small compared to the intrinsic cluster-to-cluster scatter.

Taken together, these results show that $f_{\mathrm{diff}}$ is consistently significant across the entire redshift range. For the SPT sample, the near-constant value of $f_{\mathrm{diff}}$ indicates that clusters of comparable mass exhibit a similar relative distribution of stellar light between the BCG and ICL across $z \simeq0.2$–1.5. The broadly similar behaviour of the Euclid sample, whose effective mass threshold is also expected to vary only weakly with redshift (Euclid Collaboration: Adam et al. [49]), supports this picture. Overall, this suggests that the relative partitioning of stellar light between the BCG and ICL evolves only weakly at fixed cluster mass over the redshift range probed.

Total ICL fraction

Figure 5 shows the total ICL fraction, $f_{\mathrm{tot}}$, as a function of redshift, where $f_{\mathrm{tot}}$ includes the flux contribution from satellite galaxies ($F_{\mathrm{Galaxies}}$), and is defined as

Similarly to Sect. 4.3, individual cluster measurements exhibit substantial scatter between clusters, reflecting both the stochastic nature of ICL assembly and the uncertainties introduced through the measurements. Differences in the dynamical state of clusters may also contribute to this scatter, as Kimmig et al. [81] show that the BCG+ICL fraction is an excellent tracer of dynamical state. We therefore explored whether the ICL fraction correlates with the projected offset between the BCG and cluster-centre (as a proxy for dynamical state), but found no significant relationship.

The stacked measurements provide a clearer view of the underlying population behaviour as the impact of cluster-to-cluster scatter is reduced. The SPT and high-S/N Euclid stacks yield values of $f_{\mathrm{tot}}$ ranging from 16% to 22% and 19% to 22%, respectively, and appear approximately constant over the redshift range probed. We applied the same constant-model test described in Sect. 4.3 to quantify possible evolution in $f_{\mathrm{tot}}$. The SPT and high-S/N Euclid samples show no significant deviation from a constant value, with the constant model disfavoured at only $\sim1.5\,\sigma$ and $\sim1.0\,\sigma$, respectively. The full Euclid sample exhibits lower fractions at higher redshift, declining from about 20% in the lowest-redshift bin to 12% in the highest-redshift bin, with the constant model disfavoured at $3.3\,\sigma$. The difference between the full and high-S/N Euclid stacks suggests that the apparent decline in $f_{\mathrm{tot}}$ with redshift is driven primarily by the low-S/N systems. As discussed in Sect. 4.2, these objects may include clusters with incorrect BCG identifications or a higher fraction of spurious detections, which can bias the recovered ICL measurements. While we do not show $f_{\mathrm{tot}}$ for these low-S/N stacks in Figure 5, they support this conclusion since they only range from 1% to 7% across the whole redshift range (Table D.1). The consistency between the SPT stacks and the high-S/N Euclid stacks therefore provides evidence that $f_{\mathrm{tot}}$ remains approximately constant at around 20% across the redshift range probed by our observations.

The simulations also predict a largely constant value of $f_{\mathrm{tot}}$ with redshift. The redshift-binned simulated medians are fully consistent with a constant model, with their variation remaining small compared to the intrinsic cluster-to-cluster scatter, although the normalisation is systematically higher than that measured in the observations.

Radial dependence

We investigated the sensitivity of $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ to the choice of our outer integration radius, which is used for both ICL and galaxy light. Figure 6 shows $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ as a function of outer radius for the lowest and highest redshift stacks in both the SPT and Euclid samples. We note that the background was estimated on scales of 1 Mpc, so measurements approaching this scale become increasingly sensitive to the background subtraction procedure and should be interpreted with appropriate caution. We therefore truncate this analysis at 600 kpc.

For $f_{\mathrm{diff}}$, we find a monotonic increase with outer radius that gradually flattens around $\sim300$ kpc. This behaviour is expected as, by construction, increasing the outer boundary includes additional diffuse light, while the BCG contribution remains fixed. The flattening at larger radii indicates that the bulk of ICL is contained within $\sim300$ kpc, with only modest additional contributions at larger distances, at least for the cluster masses probed in this work. This is broadly consistent with previous observational studies, which found that the ICL surface brightness drops steeply at large radii, implying only a modest additional contribution to the total ICL beyond a few hundred kpc (e.g., Kluge et al. [83]). This is also consistent with Golden-Marx et al. [71, 72], who found that the stellar mass of the BCG+ICL exhibits minimal change between 150–300 kpc.

The bottom panel of Figure 6 shows that $f_{\mathrm{tot}}$ exhibits a rise with increasing outer ICL boundary out to $\sim200$–300 kpc, beyond which it remains broadly constant within the uncertainties, with a possible slight decrease at larger radii. In practice, we find no statistically significant change in $f_{\mathrm{tot}}$ between 200 and 600 kpc. As such, our primary results are not strongly sensitive to the exact choice of outer ICL boundary (at least out to 600 kpc). Ultimately, this suggests that our adopted aperture of 200 kpc captures the bulk of the diffuse light while avoiding the regime where measurements become increasingly noise-dominated.

Taking the highest redshift SPT stack at $\bar{z} = 1.11$, a 200 kpc aperture corresponds to $\sim0.35\,\bar{R}_{500}$. For the lower two redshift stacks at $\bar{z} = 0.72$ and $\bar{z} = 0.40$, this corresponds to apertures of size $\sim240$ kpc and $\sim280$ kpc, respectively. With such small changes in the aperture sizes across redshift, evaluating $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ at these scaled radii yields consistent results with those obtained using a fixed physical aperture. This indicates that our conclusions are not sensitive to whether the outer boundary is defined in absolute or cluster-scaled units, and suggests that the bulk of the diffuse light is similarly captured within $\sim0.35\,R_{500}$ across the mass and redshift range probed.

The choice of the inner BCG/ICL separation is inherently less well defined than that of the outer integration radius, since the extended envelope of the BCG transitions gradually into ICL and there is no unique physical radius at which the two components can be separated observationally (Zhang et al. [131]; Kluge et al. [82]). Consequently, a range of fixed physical separations have been adopted in the literature. For example, some studies define the BCG within 30 kpc (e.g., Montenegro-Taborda et al. [99]; Golden-Marx et al. [72]), while others adopt 50 kpc (e.g., Whiley et al. 2008; Furnell et al. [69]; Contreras-Santos et al. [39]). Separations of roughly 80 kpc have also been motivated by the observed transition between the BCG-dominated and ICL-dominated regimes (e.g., Iodice et al. [76]; Montes et al. [101]; Golden-Marx et al. [72]). Our choice of 50 kpc follows the optimisation presented by Euclid Collaboration: Bellhouse et al. [52], but to assess the sensitivity of our results to this choice, we have also repeated our analysis using inner separations of 30 and 80 kpc. While the absolute normalisation of both $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ changes systematically, as expected from the increasing or decreasing contribution of the extended BCG to the ICL component, we find the same qualitative redshift dependence for all three separation radii. This indicates that our conclusions regarding the evolution of $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ are not driven by the particular choice of 50 kpc adopted here.

Wavelength dependence

In addition to the wide range of apertures, ICL definitions, observational depths, and measurement methodologies adopted across the literature, the interpretation of reported ICL fractions is also complicated by the wide range of observational filters that are used, which inherently probe different parts of the spectrum (e.g., Burke et al. [24]; DeMaio et al. [44]; Morishita et al. [104]; Montes & Trujillo [102]; Jiménez-Teja et al. [77]), especially when also considering the range of cluster redshifts. As a result, variations in reported ICL fractions may partly reflect differences in wavelength rather than intrinsic physical differences between cluster samples.

We therefore investigated the wavelength dependence of our ICL measurements by comparing $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ values measured in each of the Euclid filters relative to the values measured at the $\sim0.71\,\mu\mathrm{m}$ reference wavelength adopted throughout this work. The full analysis is presented in Appendix § 11, but we summarise the main results here.

We find that the wavelength dependence is relatively weak over the rest-frame optical range probed by our measurements. Relative to the $\sim0.71\,\mu\mathrm{m}$ reference point, both $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ only vary by approximately 15% towards $\sim1.2\,\mu\mathrm{m}$, while remaining approximately constant down to $\sim0.5\,\mu\mathrm{m}$. Nevertheless, the dependence is systematic, with the inferred ICL fractions generally increasing towards shorter rest-frame wavelengths and decreasing towards longer wavelengths. We find that this behaviour is not driven by differences in the satellite galaxy contribution, but instead reflects differences between the stellar populations of the BCG and ICL. Consequently, ICL fractions measured at substantially different rest-frame wavelengths may not be directly comparable. We mitigate this by converting all measurements to the rest-frame $I_{\mathrm{E}}$ band, which is possible because of Euclid's broad wavelength coverage.

Discussion

Our measured values for the individual clusters of 20% to 60% for $f_{\mathrm{diff}}$ are broadly consistent with values reported previously in both observations (e.g., Zibetti et al. [132]; Gonzalez et al. [73]; Donzelli et al. [47]; Kluge et al. [82]) and simulations (e.g., Cui et al. [42]; Contini et al. [36]; Pillepich et al. [108]). Similarly, our measurements for individual clusters of 10% to 30% for $f_{\mathrm{tot}}$ are also broadly consistent with values reported previously in both observations (e.g., Zibetti et al. [132]; Mihos et al. [96]; Spavone et al. [120]; Kluge et al. [82]; Ragusa et al. [111]) and simulations (e.g., Rudick et al. [114]; Cui et al. [42]; Contini et al. [36]; Cooper et al. [41]; Pillepich et al. [108]; Fu et al. 2024). However, these broad agreements in the absolute values do not necessarily imply agreement in their redshift dependence, since the studies considered span different redshift ranges and employ different methodologies.

We find that the SPT and high-S/N Euclid clusters exhibit no significant evolution in either $f_{\mathrm{diff}}$ or $f_{\mathrm{tot}}$ across the redshift range probed, consistent with the simulations. This is also consistent with some previous observational (e.g., Joo & Jee [79]; Coogan et al. [40]; Jiménez-Teja et al. [78]; Zhang et al. [130]) and theoretical (e.g., Contini et al. [37]; Joo et al. [80]; Kimmig et al. [81]; Brown et al. [21]) studies.

The approximately constant values of $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ observed across the full redshift range place important constraints on the physical mechanisms responsible for the formation of ICL. At face value, the lack of significant evolution may appear inconsistent with a scenario in which ICL is built predominantly through the gradual tidal stripping of satellite galaxies over time (e.g., Rudick et al. [113]; Contini et al. [36]; DeMaio et al. [45]). Since the clusters in the SPT sample span a similar mass range at all redshifts, lower-redshift systems have had substantially longer to experience stripping events than their higher-redshift counterparts. If tidal stripping were the dominant process governing changes in the global ICL mass budget, one might therefore expect the fraction of stellar light contained within ICL to increase towards lower redshift (Contini et al. [38]). We do not observe such a trend. Our results therefore disfavour a scenario in which the BCG forms first, followed by the gradual build-up of ICL through the cumulative stripping of satellite galaxies, and instead favour a picture in which the BCG and ICL are established concurrently.

It is important to note, however, that a constant $f_{\mathrm{tot}}$ does not imply that ICL itself does not grow. Rather, our measurements constrain the relative partitioning of stellar light between the BCG, ICL, and satellite populations. The key observational result is therefore that clusters of a given mass (at least for the SPT sample) appear to contain similar fractions of their stellar light in these components across the full redshift range probed. Such behaviour is naturally produced by processes that build the BCG and ICL simultaneously. For example, in merger-driven scenarios, some stars remain bound to the central galaxy while others are liberated into the diffuse component, leading to concurrent growth of both the BCG and ICL (Monaco et al. [98]; Murante et al. [105]; Montenegro-Taborda et al. [99]; Joo & Jee [79]; Jiménez-Teja et al. [78]). However, since our analysis does not track progenitor-descendant systems, we cannot directly constrain how this relative partitioning changes as individual clusters subsequently grow, nor whether the fraction of newly accreted stars that contributes to the BCG and ICL changes with redshift.

Brown et al. [22, 21] show that the majority of ICL stars in $z = 0$ simulated clusters originate from a relatively small number of massive galaxies, with typically more than half of the ICL stellar mass contributed by progenitor galaxies with stellar masses above $10^{10}\,M_{\odot}$. They also show that low-mass galaxies contribute a larger fraction of their stellar mass to the ICL through gradual tidal stripping, owing to their relatively shallow potential wells (Read et al. [112]; Martin et al. [90]), compared to massive galaxies that contribute a smaller fraction of their stellar mass to the ICL, but nevertheless dominate the total ICL mass budget because of their much larger stellar masses (see also Alvarez et al. 2024; Chun et al. [32]; Contreras-Santos et al. [39]). In fact, Martin et al. [89] show that under any plausible cluster satellite population, high-mass galaxies always dominate ICL production. These massive galaxies experience strong dynamical friction (Boylan-Kolchin et al. [18]; Amorisco [6]) and are therefore expected to sink rapidly towards the cluster centre, eventually merging with the BCG (Contini [35]; Butler et al. [25]; Martin et al. [89]). However, they are also subject to tidal stripping during infall. Consequently, the distinction between merger-origin and stripping-origin ICL may be somewhat artificial. The same galaxy can contribute stars to the diffuse component through stripping long before its final merger event, while ultimately contributing much of its remaining stellar mass to the BCG.

This scenario can also reconcile our results with observations of colour and metallicity gradients in the ICL. Numerous studies have found that the outer regions of ICL are bluer and more metal-poor than the inner regions, implying ongoing contributions from stripped stellar populations (e.g., Montes & Trujillo [102]; DeMaio et al. [45]; Jiménez-Teja et al. [77]; Euclid Collaboration: Golden-Marx et al. [56]). Such gradients are often interpreted as evidence for tidal stripping, but they do not necessarily imply that stripping dominates the overall ICL mass budget. Massive galaxies themselves exhibit strong internal age and metallicity gradients, with their centres generally older, redder, and more metal-rich than their outskirts (e.g., Franx & Illingworth [67]; Liao & Cooper [86]; Miller et al. [97]; Cheng et al. [30]). Because tidal stripping preferentially removes the least-bound stars first (Aguerri & González-García [1]; Blaña et al. 2025), the outermost stellar populations of these galaxies are expected to be deposited into the ICL before their dense central regions merge with the BCG. Consequently, the same galaxy can contribute stripped stars to the diffuse component while later contributing its central stellar mass to the BCG. Colour and metallicity gradients may therefore primarily trace where stars are deposited within the cluster rather than which physical process dominates the global ICL mass budget. Massive galaxies may be responsible for a large fraction of the ICL mass, while tidal stripping remains responsible for many of the observed stellar-population gradients.

Conclusions

We have presented a homogenous analysis of the ICL fraction using Euclid observations in a sample of around 300 galaxy clusters spanning $0.2 < z < 1.5$, combining SZ-selected clusters from SPT with optically selected clusters from Euclid, and comparing them to simulated clusters from Hydrangea. By applying a consistent methodology across all datasets, this work provides new constraints on the evolution of the ICL fraction and the physical processes that govern its assembly. Our main results can be summarised as follows:

• We show that the Euclid Wide Survey imaging is capable of detecting ICL in individual massive clusters ($M_{500} \gtrsim10^{14.2}\,M_{\odot}$) out to at least $z \sim1.5$. The high detection rates (70–80%) in confirmed clusters indicate that ICL is a common component of massive cluster assembly throughout most of the age of the Universe, but not ubiquitous, since a minority (less than 10%) of systems appear to host intrinsically weak diffuse stellar envelopes. Generally, the presence of ICL can be used to identify and confirm optically selected clusters (e.g., [70]), even at $z > 1$, although a less than marginal detection does not necessarily imply that a candidate is not a genuine cluster.

• We measure $F_{\mathrm{ICL}}/(F_{\mathrm{BCG}} + F_{\mathrm{ICL}})$ fractions at rest-frame $0.71\,\mu\mathrm{m}$ of approximately 40% from $z = 0.2$ to $z = 1.5$, within elliptical apertures of semi-major axis 200 kpc, and a BCG–ICL separation at 50 kpc, implying significant levels of ICL even at high redshifts. The near-constant balance between the BCG and ICL (for clusters of similar mass) suggests that their assembly is closely linked. This is consistent with a picture in which both components are built from the same population of accreted massive galaxies, which contribute stars to the ICL through stripping before eventually merging with the central galaxy.

• ICL comprises approximately 20% of the total stellar light in clusters with masses $10^{14.2} \lesssim M_{500}/M_{\odot} \lesssim10^{14.6}$, and we find no evolution in this fraction across redshift. It is therefore plausible that the amount of ICL may provide a complementary proxy for cluster mass alongside galaxy richness. However, the large scatter in the ICL fraction among individual clusters would hinder this, and highlights the need for further investigation of the physical processes driving this variation across a wider range of cluster masses.

• We find that the inferred ICL fraction exhibits a modest but systematic dependence on rest-frame wavelength, with lower fractions at longer wavelengths and some evidence for enhanced fractions at shorter wavelengths. We show that this behaviour is not caused by the satellite galaxies, and instead reflects differences in the stellar populations of the BCG and ICL. This is consistent with the preferential stripping of the outer regions of accreted galaxies, which can contribute relatively younger and/or more metal-poor stars to the ICL, while their central regions are incorporated into the BCG. This result demonstrates that measurements made at substantially different rest-frame wavelengths are not directly comparable.

Taken together, the results presented in this work suggest that the relative partitioning of stellar mass between the BCG, ICL, and satellite populations is already largely established by $z \sim1.5$, and is consistent with a picture in which the BCG and ICL are assembled together through the accretion, stripping, and eventual merging of the same population of massive galaxies. More broadly, this work highlights the importance of homogeneous analyses, careful treatment of selection effects, and large statistical samples in disentangling the physical mechanisms of ICL formation.

Acknowledgements

H. Gully, N.A.H., H. Gao, J.B.G.M., T.K., and M. U. gratefully acknowledge financial support from The Leverhulme Trust. Y.M.B. acknowledges support from UK Research and Innovation through a Future Leaders Fellowship (grant agreement MR/X035166/1) and financial support from the Swiss National Science Foundation (SNSF) under project 200021_213076. This work used the DiRAC@Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The equipment was funded by BEIS capital funding via STFC capital grants ST/K00042X/1, ST/P002293/1, ST/R002371/1 and ST/S002502/1, Durham University and STFC operations grant ST/R000832/1. DiRAC is part of the National e-Infrastructure. This work has made use of the Euclid Quick Release Q1 data from the Euclid mission of the European Space Agency (ESA), 2025, https://doi.org/10.57780/esa-2853fb3. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Agenzia Spaziale Italiana, the Austrian Forschungsförderungsgesellschaft funded through BMIMI, the Belgian Science Policy, the Canadian Euclid Consortium, the Deutsches Zentrum für Luft- und Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark, the French Centre National d’Etudes Spatiales, the Fundação para a Ciência e a Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia, Innovación y Universidades, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Nederlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research Council of Finland, the Romanian Space Agency, the State Secretariat for Education, Research, and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (www.euclid-ec.org).

Authors and affiliations

Euclid Collaboration: H. Gully⋆$^{1}$, N. A. Hatch$^{1}$, H. Gao$^{1}$, S. P. Bamford$^{1}$, J. B. Golden-Marx$^{1}$, A. Fernandez$^{1}$, T. Kolcu$^{1}$, Y. M. Bahé$^{1,2}$, A. H. Gonzalez$^{3}$, M. Montes$^{4}$, M. Urbano$^{1,5}$, H. J. Brown$^{1}$, G. Martin$^{1}$, L. Moscardini$^{6,7,8}$, F. Durret$^{9}$, S. L. Ahad$^{10,11}$, J. G. Sorce$^{12,13}$, B. Altieri$^{14}$, S. Andreon$^{15}$, N. Auricchio$^{7}$, C. Baccigalupi$^{16,17,18,19}$, M. Baldi$^{20,7,8}$, S. Bardelli$^{7}$, P. Battaglia$^{7}$, A. Biviano$^{17,16}$, M. Bolzonella$^{7}$, E. Branchini$^{21,22,15}$, M. Brescia$^{23,24}$, S. Camera$^{25,26,27}$, V. Capobianco$^{27}$, C. Carbone$^{28}$, V. F. Cardone$^{29,30}$, J. Carretero$^{31,32}$, M. Castellano$^{29}$, G. Castignani$^{7}$, S. Cavuoti$^{24,33}$, K. C. Chambers$^{34}$, A. Cimatti$^{35}$, C. Colodro-Conde$^{36}$, G. Congedo$^{37}$, C. J. Conselice$^{38}$, L. Conversi$^{39,14}$, Y. Copin$^{40}$, A. Costille$^{41}$, F. Courbin$^{42,43,44}$, H. M. Courtois$^{45}$, M. Cropper$^{46}$, H. Degaudenzi$^{47}$, G. De Lucia$^{17}$, H. Dole$^{13}$, F. Dubath$^{47}$, X. Dupac$^{14}$, A. Ealet$^{40}$, S. Escoffier$^{48}$, M. Farina$^{49}$, R. Farinelli$^{7}$, F. Faustini$^{29}$, S. Ferriol$^{40}$, F. Finelli$^{7,50}$, P. Fosalba$^{51,4}$, S. Fotopoulou$^{52}$, N. Fourmanoit$^{48}$, M. Frailis$^{17}$, E. Franceschi$^{7}$, M. Fumana$^{28}$, L. Gabarra$^{53}$, S. Galeotta$^{17}$, K. George$^{54}$, B. Gillis$^{37}$, C. Giocoli$^{7,8}$, J. Gracia-Carpio$^{55}$, A. Grazian$^{56}$, F. Grupp$^{55,57}$, S. Gwyn$^{58}$, S. V. H. Haugan$^{59}$, S. Hemmati$^{60}$, H. Hoekstra$^{61}$, W. Holmes$^{62}$, F. Hormuth$^{63}$, A. Hornstrup$^{64,65}$, K. Jahnke$^{66}$, M. Jhabvala$^{67}$, B. Joachimi$^{68}$, S. Kermiche$^{48}$, A. Kiessling$^{62}$, B. Kubik$^{40}$, M. Kümmel$^{57}$, M. Kunz$^{69}$, H. Kurki-Suonio$^{70,71}$, A. M. C. Le Brun$^{72}$, S. Ligori$^{27}$, P. B. Lilje$^{59}$, V. Lindholm$^{70,71}$, I. Lloro$^{73}$, M. Magliocchetti$^{49}$, G. Mainetti$^{74}$, D. Maino$^{75,28,76}$, O. Mansutti$^{17}$, O. Marggraf$^{77}$, M. Martinelli$^{29,30}$, N. Martinet$^{41}$, F. Marulli$^{6,7,8}$, R. J. Massey$^{78}$, N. Mauri$^{35,8}$, E. Medinaceli$^{7}$, S. Mei$^{79,80}$, M. Melchior$^{81}$, M. Meneghetti$^{7,8}$, E. Merlin$^{56}$, G. Meylan$^{2}$, P. Monaco$^{82,17,18,16}$, A. Mora$^{83}$, M. Moresco$^{6,7}$, C. Moretti$^{17,16,18}$, R. Nakajima$^{77}$, C. Neissner$^{84,32}$, R. C. Nichol$^{85}$, S.-M. Niemi$^{86}$, C. Padilla$^{84}$, S. Paltani$^{47}$, F. Pasian$^{17}$, W. J. Percival$^{10,11,87}$, V. Pettorino$^{86}$, A. Pezzotta$^{15}$, S. Pires$^{88}$, G. Polenta$^{89}$, M. Poncet$^{90}$, L. A. Popa$^{91}$, L. Pozzetti$^{7}$, F. Raison$^{55}$, A. Renzi$^{92,93,7}$, J. Rhodes$^{62}$, G. Riccio$^{24}$, I. Risso$^{21,22,15}$, F. Rizzo$^{17}$, E. Romelli$^{17}$, M. Roncarelli$^{7}$, B. Rusholme$^{60}$, R. Saglia$^{57,55}$, Z. Sakr$^{94,95,96}$, D. Sapone$^{97}$, B. Sartoris$^{55,17}$, P. Schneider$^{77}$, T. Schrabback$^{98}$, A. Secroun$^{48}$, E. Sihvola$^{99}$, P. Simon$^{77}$, C. Sirignano$^{92,93}$, G. Sirri$^{8}$, L. Stanco$^{93}$, P. Tallada-Crespí$^{31,32}$, A. N. Taylor$^{37}$, I. Tereno$^{100,101}$, S. Toft$^{102,103}$, R. Toledo-Moreo$^{104,105}$, F. Torradeflot$^{32,31}$, I. Tutusaus$^{4,51,95}$, J. Valiviita$^{70,71}$, T. Vassallo$^{17,54}$, Y. Wang$^{60}$, J. Weller$^{57,55}$, A. Zacchei$^{17,16}$, G. Zamorani$^{7}$, F. M. Zerbini$^{15}$, E. Zucca$^{7}$, M. Ballardini$^{106,107}$, P. Bergamini$^{7}$, S. Borgani$^{82,16,17,18,108}$, E. Bozzo$^{47}$, C. Burigana$^{109,50}$, R. Cabanac$^{29}$, F. Caro$^{110}$, T. Castro$^{17,16}$, D. Ferdinando$^{55}$, J. A. Escartin Vigo$^{94}$, J. García-Bellido$^{94}$, T. Gasparetto$^{29}$, V. Gautard$^{111}$, C. M. Gutierréz$^{36,112}$, J. Macias-Perez$^{113}$, R. Maoli$^{114,29}$, R. B. Metcalf$^{6,7}$, A. A. Nucita$^{115,116,117}$, E. Sarpa$^{17}$, V. Scottez$^{118}$, M. Sereno$^{7,8}$, M. Tenti$^{8}$, M. Tucci$^{47}$, M. Viel$^{16,17,19,18,108}$, M. Wiesmann$^{59}$, J. A. Acevedo Barroso$^{62}$, R. Adam$^{119}$, Y. Akrami$^{94,120}$, I. T. Andika$^{57}$, S. Anselmi$^{93,92,121}$, M. Archidiacono$^{75,76}$, G. Aricò$^{8}$, F. Atrio-Barandela$^{122}$, M. Baes$^{123}$, L. Bazzanini$^{106,7}$, D. Bertacca$^{92,56,93}$, M. Bethermin$^{5}$, F. Beutler$^{37}$, A. Blanchard$^{95}$, L. Blot$^{124,72}$, H. Böhringer$^{55,54,125}$, M. L. Brown$^{38}$, S. Bruton$^{126}$, B. Camacho Quevedo$^{16,19,17}$, C. S. Carvalho$^{101}$, F. Cogato$^{6,7}$, T. E. Collett$^{127}$, S. Consel$^{128,40}$, A. R. Cooray$^{129}$, P. Corcho-Caballero$^{130}$, C. Csizsi$^{98}$, O. Cucciati$^{7}$, H. Dannerbauer$^{36,112}$, S. D. Dayal$^{22}$, T. De Boer$^{34}$, F. De Paolis$^{115,116,117}$, G. Desprez$^{130}$, A. Díaz-Sánchez$^{131}$, S. Di Domizio$^{21,22}$, J. M. Diego$^{132}$, P. Dimauro$^{133,29}$, P.-A. Duc$^{5}$, V. Duret$^{48}$, Y. Fang$^{57}$, A. Farina$^{15,22}$, A. Finoguenov$^{70}$, F. Fontanot$^{17,16}$, A. Franco$^{115,116,117}$, Y. Fu$^{61,130}$, K. Ganga$^{79}$, R. Gavazzi$^{41,134}$, E. Gaztanaga$^{4,51,127}$, Z. Ghaffari$^{17,16}$, F. Giacomini$^{8}$, F. Gianotti$^{7}$, G. Gozaliasl$^{135,70}$, A. Gruppuso$^{7,8}$, M. Guidi$^{20,7}$, A. Hall$^{37}$, C. Hernández-Monteagudo$^{112,36}$, T. Heuberger$^{136}$, H. Hildebrandt$^{137}$, J. Hjorth$^{138}$, J. J. E. Kajava$^{139,140}$, Y. Kang$^{98}$, V. Kansal$^{142,143}$, D. Karagiannis$^{106,144}$, J. Kim$^{3}$, C. C. Kirkpatrick$^{99}$, A. Kovács$^{145,146}$, I. Kovačić$^{123}$, K. Koyama$^{127}$, S. Kruk$^{14}$, M. C. Lam$^{37}$, L. Legrand$^{147,13}$, G. Leroy$^{148,78}$, J. Lesgourgues$^{149}$, L. Linke$^{98}$, S. J. Liu$^{4}$, F. Mannucci$^{150}$, F. R. Marleau$^{98}$, C. J. A. P. Martins$^{151,152}$, M. Migliaccio$^{153,154}$, M. Miluzio$^{14,155}$, G. Morgante$^{7}$, K. Naidoo$^{127,66}$, A. Navarro-Alsina$^{77}$, S. Nesseris$^{94}$, L. Nicastro$^{7}$, F. Oppizzi$^{22,93,92}$, F. Pace$^{25,26,27,156}$, D. Paoletti$^{7,50}$, F. Passalacqua$^{93}$, K. Paterson$^{66}$, L. Patrizii$^{8}$, C. Pattison$^{127}$, A. Pisani$^{48}$, D. Potter$^{157}$, G. W. Pratt$^{88}$, S. Quai$^{6,7}$, M. Radovich$^{56}$, G. Rodighiero$^{92,56}$, W. Roster$^{55}$, S. Sacquegna$^{158}$, M. Sahlén$^{159}$, D. B. Sanders$^{34}$, A. Schneider$^{157}$, D. Sciotti$^{29,30}$, E. Sellentin$^{160,61}$, S. Serjeant$^{161}$, F. Shankar$^{162}$, A. Shulevski$^{163,130,61,164}$, L. C. Smith$^{2}$, S. A. Stanford$^{165}$, I. Szapudi$^{166}$, K. Tanidis$^{167}$, F. Tarsitano$^{168,169,47}$, G. Testera$^{22}$, R. Tessier$^{170}$, S. Tosti$^{15,22}$, A. Troja$^{17}$, I. Uhlemann$^{171,172}$, C. Valieri$^{8}$, A. Venhola$^{173}$, D. Vergani$^{7}$, P. Vielzeuf$^{48}$, S. Vinciguerra$^{41}$, N. A. Walton$^{165}$, L. Wang$^{174,130}$, and A. H. Wright$^{137}$

footer: Article number, page 13 Italy

{sup}7` INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Piero Gobetti 93/3, 40129 Bologna, Italy

{sup}8` INFN-Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy

{sup}9` Institut d’Astrophysique de Paris, 98bis Boulevard Arago, 75014, Paris, France

{sup}10` Waterloo Centre for Astrophysics, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

{sup}11` Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

{sup}12` Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, 59000 Lille, France

{sup}13` Université Paris-Saclay, CNRS, Institut d’astrophysique spatiale, 91405, Orsay, France

{sup}14` ESAC/ESA, Camino Bajo del Castillo, s/n., Urb. Villafranca del Castillo, 28692 Villanueva de la Cañada, Madrid, Spain

{sup}15` INAF-Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano, Italy

{sup}16` IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, 34151 Trieste, Italy

{sup}17` INAF-Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, 34143 Trieste, Italy

{sup}18` INFN, Sezione di Trieste, Via Valerio 2, 34127 Trieste TS, Italy

{sup}19` SISSA, International School for Advanced Studies, Via Bonomea 265, 34136 Trieste TS, Italy

{sup}20` Dipartimento di Fisica e Astronomia, Università di Bologna, Via Gobetti 93/2, 40129 Bologna, Italy

{sup}21` Dipartimento di Fisica, Università di Genova, Via Dodecaneso 33, 16146, Genova, Italy

{sup}22` INFN-Sezione di Genova, Via Dodecaneso 33, 16146, Genova, Italy

{sup}23` Department of Physics "E. Pancini", University Federico II, Via Cinthia 6, 80126, Napoli, Italy

{sup}24` INAF-Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy

{sup}25` Dipartimento di Fisica, Università degli Studi di Torino, Via P. Giuria 1, 10125 Torino, Italy

{sup}26` INFN-Sezione di Torino, Via P. Giuria 1, 10125 Torino, Italy

{sup}27` INAF-Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese (TO), Italy

{sup}28` INAF-IASF Milano, Via Alfonso Corti 12, 20133 Milano, Italy

{sup}29` INAF-Osservatorio Astronomico di Roma, Via Frascati 33, 00078 Monteporzio Catone, Italy

{sup}30` INFN-Sezione di Roma, Piazzale Aldo Moro, 2 - c/o Dipartimento di Fisica, Edificio G. Marconi, 00185 Roma, Italy

{sup}31` Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Avenida Complutense 40, 28040 Madrid, Spain

{sup}32` Port d’Informació Científica, Campus UAB, C. Albareda s/n, 08193 Bellaterra (Barcelona), Spain

{sup}33` INFN – Sezione di Napoli, Via Cinthia 6, 80126, Napoli, Italy

{sup}34` Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA

{sup}35` Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Università di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy

{sup}36` Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife, Spain

{sup}37` Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK

{sup}38` Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK

{sup}39` European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044 Frascati, Roma, Italy

{sup}40` Université Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne, F-69100, France

{sup}41` Aix-Marseille Université, CNRS, CNES, LAM, Marseille, France

{sup}42` Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona (IEEC-UB), Martí i Franquès 1, 08028 Barcelona, Spain

{sup}43` Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig de Lluís Companys 23, 08010 Barcelona, Spain

{sup}44` Institut de Ciències de l’Espai (IEEC-CSIC), Campus UAB, Carrer de Can Magrans, s/n Cerdanyola del Vallès, 08193 Barcelona, Spain

{sup}45` UCB Lyon 1, CNRS/IN2P3, IP2I Lyon, 4 rue Enrico Fermi, 69622 Villeurbanne, France

{sup}46` Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, UK

{sup}47` Department of Astronomy, University of Geneva, ch. d’Ecogia 16, 1290 Versoix, Switzerland

{sup}48` Aix-Marseille Université, CNRS/IN2P3, CPPM, Marseille, France

{sup}49` INAF-Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere, 100, 00100 Roma, Italy

{sup}50` INFN-Bologna, Via Irnerio 46, 40126 Bologna, Italy

{sup}51` Institut d’Estudis Espacials de Catalunya (IEEC), Edifici RDIT, Campus UPC, 08860 Castelldefels, Barcelona, Spain

{sup}52` School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol, BS8 1TL, UK

{sup}53` Department of Physics, University of Oxford, Keble Road, Oxford OX1 3RH, UK

{sup}54` University Observatory, LMU Faculty of Physics, Scheinerstr. 1, 81679 Munich, Germany

{sup}55` Max Planck Institute for Extraterrestrial Physics, Giessenbachstr. 1, 85748 Garching, Germany

{sup}56` INAF-Osservatorio Astronomico di Padova, Via dell’Osservatorio 5, 35122 Padova, Italy

{sup}57` Universitäts-Sternwarte München, Fakultät für Physik, Ludwig-Maximilians-Universität München, Scheinerstr. 1, 81679 München, Germany

{sup}58` National Research Council, Herzberg Astronomy and Astrophysics Research Centre, 5071 W. Saanich Rd. Victoria, BC, V9E 2E7, Canada

{sup}59` Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, 0315 Oslo, Norway

{sup}60` Caltech/IPAC, 1200 E. California Blvd., Pasadena, CA 91125, USA

{sup}61` Leiden Observatory, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands

{sup}62` Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA

{sup}63` Felix Hormuth Engineering, Goethestr. 17, 69181 Leimen, Germany

{sup}64` Technical University of Denmark, Elektrovej 327, 2800 Kgs. Lyngby, Denmark

{sup}65` Cosmic Dawn Center (DAWN), Denmark

{sup}66` Max-Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany

{sup}67` NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

{sup}68` Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK

{sup}69` Université de Genève, Département de Physique Théorique and Centre for Astroparticle Physics, 24 quai Ernest-Ansermet, CH-1211 Genève 4, Switzerland

{sup}70` Department of Physics, P.O. Box 64, University of Helsinki, 00014 Helsinki, Finland

{sup}71` Helsinki Institute of Physics, Gustaf Hällström katu 2, University of Helsinki, 00014 Helsinki, Finland

{sup}72` Laboratoire d’étude de l’Univers et des phenomenes eXtremes, Observatoire de Paris, Université PSL, Sorbonne Université, CNRS, 92190 Meudon, France

73 SKAO, Jodrell Bank, Lower Withington, Macclesfield SK11 9FT, UK

74 Centre de Calcul de l’IN2P3/CNRS, 21 avenue Pierre de Coubertin 69627 Villeurbanne Cedex, France

75 Dipartimento di Fisica "Aldo Pontremoli", Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy

76 INFN-Sezione di Milano, Via Celoria 16, 20133 Milano, Italy

77 Universität Bonn, Argelander-Institut für Astronomie, Auf dem Hügel 71, 53121 Bonn, Germany

78 Department of Physics, Institute for Computational Cosmology, Durham University, South Road, Durham, DH1 3LE, UK

79 Université Paris Cité, CNRS, Astroparticule et Cosmologie, 75013 Paris, France

80 CNRS-UCB International Research Laboratory, Centre Pierre Binétruy, IRL2007, CPB-IN2P3, Berkeley, USA

81 University of Applied Sciences and Arts of Northwestern Switzerland, School of Engineering, 5210 Windisch, Switzerland

82 Dipartimento di Fisica - Sezione di Astronomia, Università di Trieste, Via Tiepolo 11, 34131 Trieste, Italy

83 Telespazio UK S.L. for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

84 Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain

85 School of Mathematics and Physics, University of Surrey, Guildford, Surrey, GU2 7XH, UK

86 European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands

87 Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada

88 Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France

89 Space Science Data Center, Italian Space Agency, via del Politecnico snc, 00133 Roma, Italy

90 Centre National d’Etudes Spatiales – Centre spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France

91 Institute of Space Science, Str. Atomistilor, nr. 409 Magurele, Ilfov, 077125, Romania

92 Dipartimento di Fisica e Astronomia "G. Galilei", Università di Padova, Via Marzolo 8, 35131 Padova, Italy

93 INFN-Padova, Via Marzolo 8, 35131 Padova, Italy

94 Instituto de Física Teórica UAM-CSIC, Campus de Cantoblanco, 28049 Madrid, Spain

95 Institut de Recherche en Astrophysique et Planétologie (IRAP), Université de Toulouse, CNRS, UPS, CNES, 14 Av. Edouard Belin, 31400 Toulouse, France

96 Université St Joseph; Faculty of Sciences, Beirut, Lebanon

97 Departamento de Física, FCFM, Universidad de Chile, Blanco Encalada 2008, Santiago, Chile

98 Universität Innsbruck, Institut für Astro- und Teilchenphysik, Technikerstr. 25/8, 6020 Innsbruck, Austria

99 Department of Physics and Helsinki Institute of Physics, Gustaf Hällströmin katu 2, University of Helsinki, 00014 Helsinki, Finland

100 Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Edifício C8, Campo Grande, PT1749-016 Lisboa, Portugal

101 Instituto de Astrofísica e Ciências do Espaço, Faculdade de Ciências, Universidade de Lisboa, Tapada da Ajuda, 1349-018 Lisboa, Portugal

102 Cosmic Dawn Center (DAWN)

103 Niels Bohr Institute, University of Copenhagen, Jagtvej 128, 2200 Copenhagen, Denmark

104 Universidad Politécnica de Cartagena, Departamento de Electrónica y Tecnología de Computadoras, Plaza del Hospital 1, 30202 Cartagena, Spain

105 European University of Technology EUt+, European Union

106 Dipartimento di Fisica e Scienze della Terra, Università degli Studi di Ferrara, Via Giuseppe Saragat 1, 44122 Ferrara, Italy

107 Istituto Nazionale di Fisica Nucleare, Sezione di Ferrara, Via Giuseppe Saragat 1, 44122 Ferrara, Italy

108 ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing, Via Magnanelli 2, Bologna, Italy

109 INAF, Istituto di Radioastronomia, Via Piero Gobetti 101, 40129 Bologna, Italy

110 Department of Mathematical Physics, Institute of Physics, University of São Paulo, R. do Matão 1371, 05508-090, São Paulo, SP, Brazil

111 CEA Saclay, DFR/IRFU, Service d’Astrophysique, Bat. 709, 91191 Gif-sur-Yvette, France

112 Universidad de La Laguna, Dpto. Astrofísica, E-38206 La Laguna, Tenerife, Spain

113 Univ. Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 53, Avenue des Martyrs, 38000, Grenoble, France

114 Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 2, 00185 Roma, Italy

115 Department of Mathematics and Physics E. De Giorgi, University of Salento, Via per Arnesano, CP-193, 73100, Lecce, Italy

116 INFN, Sezione di Lecce, Via per Arnesano, CP-193, 73100, Lecce, Italy

117 INAF-Sezione di Lecce, c/o Dipartimento Matematica e Fisica, Via per Arnesano, 73100, Lecce, Italy

118 ICL, Junia, Université Catholique de Lille, LITL, 59000 Lille, France

119 Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Bd de l’Observatoire, CS 34229, 06304 Nice cedex 4, France

120 CERCA/ISO, Department of Physics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA

121 Laboratoire Univers et Théorie, Observatoire de Paris, Université PSL, Université Paris Cité, CNRS, 92190 Meudon, France

122 Departamento de Física Fundamental. Universidad de Salamanca. Plaza de la Merced s/n. 37008 Salamanca, Spain

123 Universiteit Gent, Department of Physics and Astronomy, Proeftuinsraat 86 N3, 9000 Ghent, Belgium

124 Center for Data-Driven Discovery, Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan

125 Max-Planck-Institut für Physik, Boltzmannstr. 8, 85748 Garching, Germany

126 California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA

127 Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK

128 Centre de Recherche Astrophysique de Lyon, UMR5574, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, 69230, Saint-Genis-Laval, France

129 Department of Physics & Astronomy, University of California Irvine, Irvine CA 92697, USA

130 Kapteyn Astronomical Institute, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands

131 Departamento Física Aplicada, Universidad Politécnica de Cartagena, Campus Muralla del Mar, 30202 Cartagena, Murcia, Spain

132 Instituto de Física de Cantabria, Edificio Juan Jordá, Avenida de los Castros, 39005 Santander, Spain

133 Observatorio Nacional, Rua General Jose Cristino, 77-Bairro Imperial de Sao Cristovao, Rio de Janeiro, 20921-400, Brazil

134 Institut d’Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université, 98 bis boulevard Arago, 75014 Paris, France

135 Department of Computer Science, Aalto University, PO Box 15400, Espoo, FI-00 076, Finland 136 University of Applied Sciences and Arts of Northwestern Switzerland, School of Computer Science, 5210 Windisch, Switzerland

137 Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing (GCCL), 44780 Bochum, Germany

138 DARK, Niels Bohr Institute, University of Copenhagen, Jagtvej 155, 2200 Copenhagen, Denmark

139 Department of Physics and Astronomy, Vesilinnantie 5, University of Turku, 20014 Turku, Finland

140 Finnish Centre for Astronomy with ESO (FINCA), Quantum, Vesilinnantie 5, University of Turku, 20014 Turku, Finland

141 Serco for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

142 ARC Centre of Excellence for Dark Matter Particle Physics, Melbourne, Australia

143 Centre for Astrophysics & Supercomputing, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia

144 Department of Physics and Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa

145 MTA-CSFK Lendület Large-Scale Structure Research Group, Konkoly-Thege Miklós út 15-17, H-1121 Budapest, Hungary

146 Konkoly Observatory, HUN-REN CSFK, MTA Centre of Excellence, Budapest, Konkoly Thege Miklós út 15-17. H-1121, Hungary

147 Brazilian Center for Research in Physics (CBPF), Dr. Xavier Sigaud st. 150, zip 22290-180, Rio de Janeiro, RJ, Brazil

148 Department of Physics, Centre for Extragalactic Astronomy, Durham University, South Road, Durham, DH1 3LE, UK

149 Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, 52056 Aachen, Germany

150 INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125, Firenze, Italy

151 Centro de Astrofísica da Universidade do Porto, Rua das Estrelas, 4150-762 Porto, Portugal

152 Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal

153 Dipartimento di Fisica, Università di Roma Tor Vergata, Via della Ricerca Scientifica 1, Roma, Italy

154 INFN, Sezione di Roma 2, Via della Ricerca Scientifica 1, Roma, Italy

155 HE Space for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

156 Instituto de Astrofísica e Ciências do Espaço, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal

157 Department of Astrophysics, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland

158 INAF - Osservatorio Astronomico d’Abruzzo, Via Maggini, 64100, Teramo, Italy

159 Theoretical astrophysics, Department of Physics and Astronomy, Uppsala University, Box 516, 751 37 Uppsala, Sweden

160 Mathematical Institute, University of Leiden, Einsteinweg 55, 2333 CA Leiden, The Netherlands

161 School of Physical Sciences, The Open University, Milton Keynes, MK7 6AA, UK

162 School of Physics & Astronomy, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK

163 ASTRON, the Netherlands Institute for Radio Astronomy, Postbus 2, 7990 AA, Dwingeloo, The Netherlands

164 Center for Advanced Interdisciplinary Research, Ss. Cyril and Methodius University in Skopje, Macedonia

165 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK

166 Department of Physics and Astronomy, University of California, Davis, CA 95616, USA

167 Center for Astrophysics and Cosmology, University of Nova Gorica, Nova Gorica, Slovenia

168 Kobayashi-Maskawa Institute for the Origin of Particles and the Universe, Nagoya University, Chikusa-ku, Nagoya, 464-8602, Japan

169 Institute for Particle Physics and Astrophysics, Dept. of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 27, 8093 Zurich, Switzerland

170 Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA

171 Fakultät für Physik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld, Germany

172 School of Mathematics, Statistics and Physics, Newcastle University, Herschel Building, Newcastle-upon-Tyne, NE1 7RU, UK

173 Space physics and astronomy research unit, University of Oulu, Pentti Kaiteran katu 1, FI-90014 Oulu, Finland

174 SRON Netherlands Institute for Space Research, Landleven 12, 9747 AD, Groningen, The Netherlands

Cluster properties and identification of the BCG

In order to measure the ICL fraction, we first identified the BCG. The BCG provides a central reference point for fitting elliptical isophotes and extracting surface brightness profiles of both the BCG+ICL and total cluster light. We used a probabilistic approach to estimate cluster association scores and then selected BCGs, combining photometric redshift information, projected spatial distributions of galaxies, and Euclid NIR photometry, similar in spirit to methods such as RedMaPPer Rykoff et al. [115] and the approach of Castignani & Benoist [29]. Cluster association scores were derived from photometric redshift overdensities, and the BCG was then selected as the brightest NIR galaxy among the likely members. More sophisticated BCG identification methods have also been developed, combining additional information such as colours, stellar masses, and cluster richness (e.g., Radovich et al. [110]; Toni et al. [124]). However, our implementation is intentionally simpler, as the association scores are used only to identify likely cluster members for BCG selection, while satellite galaxy contribution is measured independently through statistical contamination correction.

Photometric redshift overdensities

To search for redshift overdensities, we used the entire photo-$z$ PDF for each galaxy, derived from OU-PHZ (Euclid Collaboration: Tucci et al. [63]). We did this by stacking the photo-$z$ PDF of each galaxy, where each galaxy’s photo-$z$ PDF was weighted (using a Gaussian weighting function) by its angular separation to the initial cluster centre (as given in either the Euclid or SPT catalogues). The photo-$z$ stack is given by

where $A_{\mathrm{eff}}$ normalises the stack by effective area, $N$ is the number of galaxies in the stack, $P_i(z)$ is the photo-$z$ PDF of the $i$th galaxy over the range $0 \le z \le z_{\max}$ (where $z_{\max} = 6$), and $w_i$ is the Gaussian weighting function for the $i$th galaxy, given by

where $\theta_i$ is the angular separation between the $i$th galaxy and the initial cluster centre, and $\sigma_\theta$ is the scale parameter of the weighting function, initially set to 1 arcminute. An example of one of these stacks is shown in the top panel of Fig. A.1. In the creation of these stacks, we exclude any galaxy that has mer.det_quality_flag $> 3$, or mer.spurious_flag $> 0$, or phz.phz_flags $> 0$, or mer.mumax_minus_mag $> -2.6$.⁶ As we allowed all galaxies to contribute to the photo-$z$ stack, the determination of the area to normalise by (i.e., $A_{\mathrm{eff}}$) was not trivial, especially when considering masked pixels and image edges. To estimate $A_{\mathrm{eff}}$, we first constructed a per-pixel validity mask from the RMS noise maps. Each pixel was assigned a value of unity if its RMS value was both positive and finite, and zero otherwise. This mask effectively encodes the usable image area, excluding regions affected by missing data, saturation, or invalid noise estimates. The exact position of the initial cluster centre relative to these excluded regions affects the area available to the stack. To account for this, we convolved this validity mask with a Gaussian kernel defined by the same functional form as the angular weighting function in Eq. (A.2), with the same scale parameter ($\sigma_\theta= 1'$). This convolution effectively computes, for every pixel position, the total valid area available under the weighting scheme, thereby producing an area map that encodes the effective area as a function of position. The value of $A_{\mathrm{eff}}$ for a given cluster centre was then obtained by sampling this convolved map at the cluster location. This procedure ensures that the normalisation of the stacked photo-$z$ distribution properly reflects both the weighting profile and the varying image coverage due to masks and edges.

For each cluster, this method was also applied to 500 randomly selected regions across the specific cluster’s image. By repeating the stacking analysis on these regions, we constructed a distribution of background stacks. An example of this can be seen in Fig. A.1. The mean and standard deviation of these random stacks provide an empirical estimate of the background and its variance, which were then used to determine the statistical significance of any redshift overdensities. The significance was calculated as

where $S_{\mathrm{cl}}(z)$ is the stack for the cluster, $\langle S_{\mathrm{r}}(z)\rangle$ is the mean of the random stacks, and $\sigma_{S_{\mathrm{r}}(z)}$ is the standard deviation of the random stacks. An example of this significance is shown in the bottom panel of Fig. A.1.

Once the significance distribution, $\Sigma(z)$, had been constructed, the next step was to identify and characterise the most significant redshift peak. We first located contiguous regions in redshift space where the significance exceeded a chosen threshold (here $\Sigma> 2$). Among all candidate regions, we selected the one with the highest maximum significance. For the selected region, we fitted a Gaussian function, $G(z)$, to the excess distribution (where the excess is defined as the numerator in Eq. (A.3)) using a weighted least-squares approach, where the weights are given by the inverse significance of each point. This fit provides a robust estimate of the cluster redshift, along with its uncertainty. An example of this fit is shown in the top panel of Fig. A.1.

Cluster association scores

Calculating the cluster association score for each galaxy allows us to identify likely cluster members from which to select a BCG. To assign association scores to galaxies, we compared each galaxy’s photometric redshift PDF with the cluster’s redshift distribution, as well as its projected spatial location relative to the cluster centre. Specifically, we multiplied the Gaussian fit to the cluster excess, $G(z)$, by the galaxy’s photo-$z$ PDF, $P_i(z)$, integrated over redshift, and then multiplied by the spatial weight, $w_i$, such that the cluster association score is given by

For the cluster association score calculations, we retained galaxies that were excluded from the photo-$z$ PDF stacks (with mer.det_quality_flag $> 3$, or mer.spurious_flag $> 0$, or phz.phz_flags $> 0), since visual inspections revealed that obvious BCGs were sometimes removed by these flags.

It should be noted that $M_i$ does not represent a formal membership probability in a strict statistical sense, but rather a relative measure of likelihood that a galaxy is associated with the cluster, based on its photometric redshift and projected location. These values allow us to rank galaxies by probable membership and to robustly select likely cluster members for subsequent analyses, such as BCG identification and total light measurements, while accounting for both redshift and spatial information.

BCG selection and method refinement

Once cluster association scores were assigned to all galaxies in the cluster region, the BCG was selected as the brightest galaxy among those with high association scores. Specifically, we selected probable members as galaxies with $M_i > 0.5$. From these, the BCG was identified as the brightest galaxy based on Euclid $H_{\rm E}$-band fluxes measured from single-Sérsic model fits (i.e., mer.flux_h_sersic). These represent estimates of total galaxy flux, making it ideal to identify the BCG. As an additional safeguard against the misidentification of interlopers (e.g., stars), we further required the selected BCG to have a Kron radius larger than the median Kron radius of the probable members (i.e., $M_i > 0.5$). If this condition was not satisfied, the next brightest galaxy was selected as the BCG. In all cases, the requirement was met by one of the two brightest galaxies, and no fainter candidates were considered.

For SPT clusters, the cluster mass, $M_{500}$, was estimated from the significance of the SZ signal, $\xi$. The measured detection significance is biased because it is maximised across position (RA, Dec) and filter scale. This maximisation over three independent search dimensions introduces a statistical bias, effectively equivalent to an addition of three degrees of freedom. To account for this, we followed Vanderlinde et al. [126], and estimated the unbiased detection significance as

Here, we approximated the ensemble average of $\xi$ across many noise realisations (i.e., $\langle\xi\rangle$) as the measured detection significance, $\xi$. Following Archipley et al. [8], we then used the significance–mass relation from Benson et al. [13] to estimate cluster mass, which is given by

where $A_{\rm SZ}$, $B_{\rm SZ}$, and $C_{\rm SZ}$ describe the normalisation, mass dependence, and redshift evolution of the relation, respectively, and $H(z)$ is the Hubble parameter at redshift $z$. We adopted the values $A_{\rm SZ} = 3.95$, $B_{\rm SZ} = 1.69$, and $C_{\rm SZ} = 0.87$ from Bleem et al. [16]. Following Archipley et al. [8], we rescaled $A_{\rm SZ}$ as $\gamma A_{\rm SZ}$ (with $\gamma= 4.05$).⁸ We also accounted for the scatter in the $\zeta$–mass relation by sampling $\zeta$ assuming a log-normal scatter of $\sigma_{\ln\zeta} = 0.2$, and propagating this through to a distribution of $M_{500}$ values. These samples were then weighted by a dark-matter-only Bocquet et al. [17] halo mass function at the cluster redshift, computed using the hmf Python package (Murray et al. [106]), to incorporate the expected abundance of haloes as a prior. The final cluster mass was taken as the median of this weighted distribution, and uncertainties defined by the 16th and 84th percentiles.

Given that $M_{500}$ is defined as the mass enclosed within the radius where the mean interior density equals 500 times the critical density of the Universe, we first compute $R_{500}$ for each SPT cluster. This is then converted into a characteristic scale radius, $R_{\rm scale} \sim0.5 R_{200}$, which is adopted in the weighting function used to calculate cluster association scores. For the SPT sample, $R_{\rm scale}$ is therefore derived directly from the mass estimates. For the Euclid clusters, however, we lack reliable mass measurements and cannot determine $R_{500}$ (or $R_{200}$) on an individual basis. Instead, we adopt an empirical characteristic scale by fitting $R_{\rm scale}$ from the SPT sample as a function of redshift. We stress that this relation is not intended to provide physical cluster size estimates for the Euclid sample, but rather supplies a representative scale for the weighting function used during the BCG identification and association score estimation. Although the normalisation of this relation may not be directly applicable to the Euclid sample, its redshift dependence is expected to be more robust. The SPT clusters span a relatively narrow mass range with only weak evolution in their mass across the redshift range considered here (Figure 1). At fixed mass, the characteristic radii defined relative to the critical density naturally decrease with increasing redshift as the critical density of the Universe increases. The Euclid sample is likewise expected to exhibit only weak evolution in its typical cluster mass with redshift, albeit with a broader mass distribution [49]. We therefore expect a similar qualitative redshift dependence of the characteristic scale radius for the Euclid sample, although with greater scatter and potentially a different normalisation. We use the SPT-derived relation as a practical approximation rather than as a measurement of the physical sizes of individual Euclid clusters. As the observed relation is approximately linear (Fig. A.2), we employ the random sample consensus (RANSAC) algorithm (Fischler & Bolles [66]) to perform a robust linear regression, which is less sensitive to outliers than ordinary least-squares fitting. The resulting best-fitting relation, shown in Fig. A.2, is then applied to the Euclid sample to assign $R_{\mathrm{scale}}$ values for use in the association score calculation.

Using these radii, we altered the weighting function from Eq. (A.2) to instead be physically motivated, and defined it as

where $R_i$ is the physical separation of the $i$th galaxy with the newly identified BCG, and the scale parameter is set as $\sigma_R = R_{\mathrm{scale}}$. The exact value of $R_{\mathrm{scale}}$ is not critical for our analysis. We find that reasonable variations in $R_{\mathrm{scale}}$ do not alter the identified BCGs in practice. Furthermore, the resulting cluster association scores are used only to reject obvious non-members and to obtain an approximate richness estimate for excluding extremely rich systems from the stacking analysis; the satellite galaxy light itself is measured using a statistical contamination correction. With the new weighting function physically motivated and centred on the BCG, we repeated all the steps again and measured new photo-$z$ PDF stacks for the clusters and random regions, new overdensity significance measurements, new Gaussian fits to the most significant redshift peaks, and new cluster association scores.

If the BCG identified in the second iteration is consistent with that from the initial iteration, we adopt it as the final BCG.

In cases where a different candidate is selected, the analysis is repeated, recentered on the position of the newly identified BCG. If this identifies a candidate that is still not consistent with either of the previously identified candidates, we do not adopt any as the final BCG. Genuine BCGs are expected to be prominent, typically centrally located objects ([87, 127, 14, 31]) whose identification should be robust to small changes in assumed cluster centre. Persistent changes after multiple redshifting likely indicate issues such as inaccurate photometric redshifts, unreliable photometry, or low S/N, in which case automated selection cannot be considered reliable.

We identified BCGs in 81 of the 121 SPT clusters, and in 303 of the 426 Euclid clusters. We find that 37 of the BCGs identified are in both the SPT and Euclid samples, which leaves a total cluster sample of 347 unique clusters. We visually inspected all 347 BCGs and found that 18 had been clearly misidentified. In these cases, a different galaxy was evidently the true BCG, but it had been overlooked because its cluster association score fell below 50%. Since these galaxies were located close to their respective cluster centres, the low association scores were driven entirely by the shapes of their photo-$z$ PDFs, which showed too little overlap with the Gaussian, $G(z)$, fitted to the cluster redshift overdensity. This mismatch likely arises from biases or broad uncertainties in the photometric redshift estimates ([63]), which can affect the accuracy of the association scores. We manually reselected the BCGs in these cases. Finally, we removed an additional 47 clusters from our sample for which measurement of ICL would likely be biased due to extremely bright nearby stars or poor quality imaging. The final cluster sample used for the rest of this work consists of 66 SPT clusters and 269 Euclid clusters, with 36 that are in both. We show the redshifts we measured for this sample against the redshifts previously measured by Archipley et al. [8] and Euclid Collaboration: Bhargava et al. [53] in Fig. A.3.

PSF deconvolution

Accurate measurements of ICL require a careful treatment of the PSF. The extended wings of the PSF redistribute flux from the central galaxy to large radii, where it can mimic or bias the diffuse ICL (e.g., [116, 125, 84, 130]). To mitigate this contamination, we explicitly modelled the PSF in each band and applied a deconvolution to the stacked cluster images prior to measuring surface brightness profiles.

For each filter, we constructed empirical PSFs from stacked stellar profiles (from Cuillandre et al. [43]), where we interpolated the radial surface brightness profiles onto a two-dimensional grid. To account for sub-pixel centring effects, we generated multiple PSF realisations with random offsets uniformly distributed within $\pm0.5$ pixels from the centre pixel. The resulting PSFs were normalised such that their total flux summed to unity. For each filter, we stacked the same number of PSF realisations as the number of clusters contributing to the corresponding cluster stack, using the same stacking procedure adopted for the science images.

We deconvolved the stacked cluster images using the unsupervised Wiener deconvolution algorithm (Orieux et al. [107]). This method works in the frequency domain, and provides a regularised inversion of the PSF convolution that suppresses high-frequency noise amplification. The deconvolution was performed on each bootstrap realisation of the stacked images. Figure B.1 shows how this deconvolution affects both the surface brightness profiles and the integrated fluxes measured in the stacked images. The distributions show that light is transferred back to the most central regions through deconvolution, as expected. However, there is very little difference between deconvolved and non-deconvolved integrated fluxes at the scales we are interested in (i.e., a percentage difference of $\pm1\%$ within SMAs from 50–200 kpc), which is true for all redshifts. This means that the derived ICL fractions are not materially affected by the deconvolution. It is only within the innermost regions that the deconvolution would have a non-negligible effect, and the specific scale depends on whether measurements of the flux at particular radii, or within, are desired. Since deconvolution has only a minor impact on our measurements, we do not apply it to individual clusters for which the noise is significantly larger.

Wavelength dependence

To understand how wavelength affects the ICL fractions we measure for our sample of clusters, we computed $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ in each of the observed Euclid filters for the stacked cluster images. This dependence is shown in Fig. C.1, where measurements in each band ($f_{\lambda}$) are expressed relative to the rest-frame $I_{\mathrm{E}}$ reference point at $\sim0.71\,\mu\mathrm{m}$ ($f_{\mathrm{IE}}$, i.e., the values for the stacks presented in Figure 4 and Figure 5) to remove any redshift dependence. For both $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$, we find the same trends: moving to longer wavelengths gives a mild but systematic decrease in $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$, reaching offsets of around 15% at $\sim1.2\,\mu\mathrm{m}$. In contrast, towards shorter wavelengths, $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ remain approximately stable down to $\sim0.5\,\mu\mathrm{m}$, with an increase at shorter wavelengths driven primarily by the two highest-redshift bins.

The almost identical dependence on wavelength in both $f_{\mathrm{diff}}$ and $f_{\mathrm{tot}}$ suggests that galaxies play no substantial role in the inferred trend, and it instead solely reflects the differences in the stellar populations of the BCG and ICL. One possible interpretation is that ICL contains a larger contribution from relatively younger and/or lower-metallicity stars compared to the BCG (e.g., [36, 48, 102, 2, 56]). This could arise because, in addition to the stars that form the BCG, ICL includes contributions from interactions with less massive and more metal-poor cluster members – although not necessarily younger (Mayes et al. [92]). Such populations would contribute proportionally more light at shorter wavelengths, naturally leading to enhanced ICL fractions at the blue end. Conversely, at longer wavelengths, where older and more metal-rich stars dominate, the contrast between the BCG and ICL is reduced, resulting in a lower inferred fraction.

Jiménez-Teja et al. [78] find little wavelength dependence in the ICL fractions for dynamically relaxed clusters (at least for $\simeq0.2$–$0.7\,\mu\mathrm{m}$), while for merging systems the ICL fraction increases towards longer wavelengths over $0.2$–$1.0\,\mu\mathrm{m}$, with a distinctive peak at $0.38$–$0.48\,\mu\mathrm{m}$. We do not recover this behaviour. Instead, our measurements remain approximately constant down to $\sim0.5\,\mathrm{\mu m}$ before increasing only at the shortest wavelengths, while otherwise exhibiting a mild decrease towards longer wavelengths. This discrepancy may reflect differences in cluster selection, dynamical state, or methodology, but more generally suggests that the wavelength dependence of the ICL fraction is not universal and may depend sensitively on the underlying stellar populations and assembly history of the cluster.

Overall, these results demonstrate that the ICL fraction is not strictly wavelength-independent. The dependence is relatively weak at wavelengths longer than $0.5\,\mathrm{\mu m}$, while the shortest-wavelength measurements show some evidence for an increase. Consequently, measurements made at similar rest-frame optical wavelengths are likely to be broadly comparable, whereas comparisons involving substantially different rest-frame wavelengths may be affected by systematic offsets.

Summary of stacked cluster properties

Table D.1 summarises the properties of all cluster stacks analysed in this work, including their component luminosities and the corresponding ICL fractions.

Stack	Redshift$^{a}$	$\log_{10}(M_{500}/M_{\odot})^{a}$	Number of clusters	$\log_{10}(L/L_{\odot})^{b,c,d}$ BCG	$\log_{10}(L/L_{\odot})^{b,c,d}$ ICL	$\log_{10}(L/L_{\odot})^{b,c,d}$ Galaxies	Fraction$^{c,d}$ $f_{\mathrm{diff}}$ [%]	Fraction$^{c,d}$ $f_{\mathrm{tot}}$ [%]
SPT 1	$0.40^{+0.12}_{-0.06}$	$14.33^{+0.11}_{-0.06}$	20	$11.44^{+0.06}_{-0.09}$	$11.22^{+0.07}_{-0.07}$	$11.56^{+0.05}_{-0.07}$	$38^{+3}_{-2}$	$21^{+2}_{-2}$
SPT 2	$0.72^{+0.14}_{-0.09}$	$14.29^{+0.11}_{-0.05}$	20	$11.41^{+0.02}_{-0.04}$	$11.16^{+0.06}_{-0.05}$	$11.73^{+0.08}_{-0.06}$	$36^{+3}_{-3}$	$15^{+1}_{-2}$
SPT 3	$1.11^{+0.23}_{-0.09}$	$14.24^{+0.21}_{-0.08}$	21	$11.52^{+0.05}_{-0.05}$	$11.31^{+0.06}_{-0.09}$	$11.74^{+0.08}_{-0.12}$	$38^{+3}_{-2}$	$19^{+2}_{-2}$
Euclid 1	$0.40^{+0.02}_{-0.03}$	-	51	$11.34^{+0.03}_{-0.03}$	$11.09^{+0.03}_{-0.03}$	$11.42^{+0.05}_{-0.06}$	$36^{+1}_{-1}$	$20^{+1}_{-1}$
Euclid 2	$0.51^{+0.05}_{-0.03}$	-	51	$11.28^{+0.02}_{-0.03}$	$11.02^{+0.05}_{-0.06}$	$11.44^{+0.05}_{-0.05}$	$36^{+2}_{-2}$	$18^{+2}_{-2}$
Euclid 3	$0.64^{+0.05}_{-0.04}$	-	51	$11.35^{+0.03}_{-0.03}$	$11.02^{+0.05}_{-0.07}$	$11.54^{+0.09}_{-0.09}$	$32^{+2}_{-2}$	$15^{+2}_{-2}$
Euclid 4	$0.82^{+0.07}_{-0.07}$	-	51	$11.36^{+0.03}_{-0.03}$	$11.07^{+0.05}_{-0.07}$	$11.66^{+0.10}_{-0.08}$	$34^{+2}_{-2}$	$15^{+2}_{-1}$
Euclid 5	$1.14^{+0.22}_{-0.18}$	-	52	$11.47^{+0.03}_{-0.03}$	$11.13^{+0.06}_{-0.08}$	$11.84^{+0.06}_{-0.09}$	$32^{+2}_{-3}$	$12^{+2}_{-2}$
Euclid high-S/N 1	$0.39^{+0.02}_{-0.04}$	-	35	$11.35^{+0.04}_{-0.04}$	$11.12^{+0.04}_{-0.04}$	$11.45^{+0.07}_{-0.06}$	$37^{+1}_{-2}$	$21^{+1}_{-1}$
Euclid high-S/N 2	$0.47^{+0.03}_{-0.05}$	-	35	$11.37^{+0.03}_{-0.03}$	$11.15^{+0.03}_{-0.04}$	$11.46^{+0.07}_{-0.07}$	$38^{+2}_{-2}$	$21^{+2}_{-2}$
Euclid high-S/N 3	$0.58^{+0.04}_{-0.04}$	-	35	$11.33^{+0.02}_{-0.02}$	$11.09^{+0.03}_{-0.03}$	$11.56^{+0.06}_{-0.06}$	$36^{+1}_{-2}$	$17^{+1}_{-1}$
Euclid high-S/N 4	$0.71^{+0.06}_{-0.04}$	-	36	$11.37^{+0.04}_{-0.03}$	$11.23^{+0.06}_{-0.03}$	$11.71^{+0.07}_{-0.12}$	$42^{+2}_{-2}$	$19^{+2}_{-2}$
Euclid high-S/N 5	$1.03^{+0.23}_{-0.13}$	-	36	$11.48^{+0.04}_{-0.03}$	$11.36^{+0.03}_{-0.04}$	$11.89^{+0.07}_{-0.08}$	$43^{+2}_{-2}$	$17^{+1}_{-1}$
Euclid low-S/N 1	$0.53^{+0.04}_{-0.05}$	-	19	$11.20^{+0.03}_{-0.05}$	$10.38^{+0.15}_{-0.13}$	$11.30^{+0.11}_{-0.10}$	$14^{+4}_{-3}$	$7^{+2}_{-2}$
Euclid low-S/N 2	$0.65^{+0.13}_{-0.03}$	-	19	$11.23^{+0.06}_{-0.05}$	$9.97^{+0.27}_{-0.42}$	$11.19^{+0.25}_{-0.12}$	$3^{+6}_{-6}$	$2^{+3}_{-3}$
Euclid low-S/N 3	$0.90^{+0.04}_{-0.06}$	-	19	$11.26^{+0.03}_{-0.03}$	$10.35^{+0.23}_{-0.31}$	$11.60^{+0.07}_{-0.10}$	$11^{+6}_{-8}$	$4^{+3}_{-3}$
Euclid low-S/N 4	$1.26^{+0.12}_{-0.25}$	-	19	$11.43^{+0.06}_{-0.05}$	$10.25^{+0.35}_{-0.57}$	$11.76^{+0.05}_{-0.10}$	$3^{+8}_{-7}$	$1^{+3}_{-2}$

Notes. $^{a}$Redshift and mass values are reported as the median of the clusters in each stack. Uncertainties denote the 16th and 84th percentiles of the cluster distributions within that stack.

$^{b}$The solar spectral luminosity density in the $I_{\mathrm{E}}$ band was calculated by convolving a model solar spectrum with the $I_{\mathrm{E}}$ filter transmission curve, giving $L_{\odot}=53.40$ Jy.

$^{c}$Values are reported as the median of the bootstrap realisations. Uncertainties denote the 16th and 84th percentiles of the corresponding bootstrap distributions.

$^{d}$BCG luminosities are measured within an elliptical aperture of semi-major axis 50 kpc; ICL luminosities between elliptical apertures with semi-major axes of 50 kpc and 200 kpc; galaxy luminosities within an elliptical aperture of semi-major axis 200 kpc. The fractions are computed using these same aperture definitions.