FEAST: Spatially Resolved Star Formation History of the NGC 4485/NGC 4490 System and its Tidal Bridge from JWST/NIRCam Color Magnitude Diagrams

Corresponding author: Giacomo Bortolini

Email: [email protected]

• ARC DECRA Fellow

Introduction

Understanding how galaxy interactions and mergers regulate the conversion of gas into stars remains a fundamental challenge in the field of galaxy formation and evolution. Within the hierarchical framework of the Lambda-cold dark matter ($\Lambda$CDM) cosmological model, galaxies are predicted to grow through accretion of smaller satellites (S. D. M. White & M. J. Rees 1978; P. J. E. Peebles 1982; S. D. M. White & C. S. Frenk 1991; J. S. Bullock & K. V. Johnston 2005; J. S. Bullock & M. Boylan-Kolchin 2017). Interactions and mergers are therefore expected to play a key role in shaping galaxy evolution across a wide range of masses. They can alter galaxy morphology and kinematics, redistribute angular momentum, drive gas inflows, increase gas densities, and trigger enhanced episodes of star formation (see e.g., J. E. Barnes & L. E. Hernquist 1991; J. E. Barnes & L. Hernquist 1996; G. Östlin et al. 2001; C. Li et al. 2008; F. Bournaud 2011; S. L. Ellison et al. 2013; S. Pearson et al. 2016; S. Paudel et al. 2018; I. D. Karachentsev et al. 2018; M. D. Thorp et al. 2019; H.-A. Pan et al. 2019; G. Bortolini et al. 2024a, among others). While the impact of interactions and mergers has been extensively studied in the Milky Way and other massive galaxies [14] (R. Ibata et al. 2001; A. W. McConnachie et al. 2009; D. Martínez-Delgado et al. 2010; D. Crnojević et al. 2016; A. Merritt et al. 2016), the role of these processes in the low-mass dwarf galaxy regime ($\mathrm{M}_{\star} \lesssim10^{9}\,\mathrm{M}_{\odot}$) has historically been investigated primarily through individual systems (see e.g., G. Östlin et al. 2001), while only in the past decade have dedicated observational surveys enabled a systematic exploration of interacting dwarf galaxy populations (see, e.g., S. Stierwalt et al. 2015; J. L. Carlin et al. 2016; C. R. Higgs et al. 2016; S. Stierwalt et al. 2017; S. Paudel et al. 2018; F. Annibali et al. 2020; E. Sacchi et al. 2024; M. Correnti et al. 2025a; S. Stierwalt et al. 2026; J. Li et al. 2026).

Observations of isolated low-mass galaxy pairs have revealed systematically elevated star formation rates relative to those of isolated dwarfs, with the enhancement becoming stronger as the separation between the galaxies decreases (S. Stierwalt et al. 2015). Unlike mergers involving massive galaxies, where star formation is often concentrated in the nuclear regions as tidal torques funnel gas toward the center [6, 10] (J. C. Mihos & L. Hernquist 1994; S. L. Ellison et al. 2008; P. F. Hopkins et al. 2008; P. F. Hopkins et al. 2013; M. D. Thorp et al. 2019; M. H. Hani et al. 2020), interacting dwarf galaxies frequently exhibit spatially extended and clumpy star formation distributed throughout their disks and tidal structures (S. Stierwalt et al. 2015, 2017; G. C. Privon et al. 2017; S. Paudel et al. 2018; S. Mandal & C. R. Kerton 2024; M. Correnti et al. 2025b; G. Bortolini et al. 2025). This suggests that the response of the interstellar medium to tidal interactions depends strongly on galaxy mass, with low-mass systems being highly susceptible to efficient gas stripping and structural transformation, while higher-mass galaxies predominantly exhibit gas redistribution and tidally induced morphological features rather than large-scale gas removal (A. Fattahi et al. 2018a; A. R. Pettitt & J. W. Wadsley 2018; C. Henkel et al. 2022).

Recent hydrodynamical simulations further indicate that mergers between gas-rich, low-mass galaxies can produce the high gas densities and pressures required to trigger elevated star formation efficiencies, and the formation of young massive star clusters (see e.g., K. Bekki 2008; T. Ejdetjärn et al. 2025; N. Lahén et al. 2019, 2020). N. Lahén et al. (2020) found that during the interaction between two gas-rich galaxies with the same stellar masses ($\mathrm{M}_{\star} \sim10^{7}\,\mathrm{M}_{\odot}$), the total star formation rate peaks shortly after each pericenter passage (see Figure 5 in N. Lahén et al. 2020). These bursts of star formation are predicted to be several times stronger than the average SFR, enough to leave strong and observable traces in the stellar populations of such systems.

In this scenario, interacting low-mass galaxies in the nearby Universe (i.e., within $\sim20$ Mpc from us) provide a unique opportunity to investigate these processes at high spatial resolutions. At such distances, individual stars and star clusters can be resolved with modern space-based facilities [3] (E. Tolstoy et al. 2009; K. B. W. McQuinn et al. 2010; D. R. Weisz et al. 2014; D. R. Weisz et al. 2024; A. Smercina et al. 2025), enabling the reconstruction of spatially resolved star formation histories (SFHs) from deep color–magnitude diagrams (CMDs, see M. Cignoni & M. Tosi 2010 and references therein). Because resolved stellar populations retain a fossil record of the past star formation activity, they provide a direct means of probing how interactions influence star formation across the entire merger history. These nearby systems therefore serve as fundamental laboratories for understanding galaxy assembly in the early Universe, when interactions and mergers were more common and hierarchical growth proceeded more rapidly (C. J. Conselice 2014), but where comparable spatial resolution cannot be achieved.

One of the best nearby laboratories for studying low-mass, gas-rich galaxy interaction is the NGC 4485/4490 system (Arp 269). NGC 4485 and NGC 4490 form a pair of interacting galaxies with stellar masses of $\sim3.2 \times10^{8}\,\mathrm{M}_{\odot}$ and $\sim2.5 \times10^{9}\,\mathrm{M}_{\odot}$, respectively. The system is located at a distance $\sim7.4$ Mpc (D. Calzetti et al. 2015; E. Sabbi et al. 2018), and the galaxy centers are separated by $\sim7.5$ kpc in projection. They exhibit a prograde motion and a velocity difference of approximately $30\,\mathrm{km\,s^{-1}}$ (S. Pearson et al. 2018). NGC 4490 has an oxygen abundance of 12 + $\log(\mathrm{O}/\mathrm{H}) = 8.37$ derived from HII regions (L. S. Pilyugin & T. X. Thuan 2007). The pair is also relatively isolated, with the nearest massive galaxy, NGC 4369, located at a projected distance of $\sim300$ kpc and differing in radial velocity by more than 400 km s$^{-1}$ (S. Pearson et al. 2016, 2018). These properties make NGC 4485/NGC 4490 particularly interesting, as it is one of the nearby systems in which individual stars can be resolved across a tidally induced, actively star-forming bridge (G. Bortolini et al. 2025, see also Figure 1). Indeed, although a similar bridge can be studied in greater detail between the Large and Small Magellanic Clouds, disentangling the effects of their mutual interaction from those induced by the gravitational influence of the Milky Way remains challenging [13] (G. Besla et al. 2012; F. Ficara et al. 2026; C. Burhenne et al. 2026).

The tidal bridge connecting NGC 4485 and NGC 4490 exhibits a non-uniform morphology (see Figure 1 and Figure 2): star formation is strongly enhanced near the two parent galaxies (T. P. Roberts et al. 2002; B. J. Smith et al. 2010; S. Mandal & C. R. Kerton 2024; G. Bortolini et al. 2025), while the central region contains abundant atomic and molecular gas (J. Cannon, S. Linden, private communication), but comparatively little recent star formation. Some studies have suggested that NGC 4490 is a relatively young galaxy, approximately 2 Gyr old, that has sustained nearly continuous star formation over at least the past 100 Myr (D. M. Elmegreen et al. 1998; M. S. Clemens et al. 1999, 2000). In this context, N-body and test-particle simulations by S. Pearson et al. (2018) provide further insight into the dynamical history of the system, suggesting two pericenter passages occurring approximately 1.3 Gyr and 200 Myr ago. If this scenario is correct, such encounters should have left observable signatures in the SFHs of both galaxies and their tidal bridge (N. Lahén et al. 2019, 2020; T. Ejdetjärn et al. 2025).

In G. Bortolini et al. (2025), we presented new JWST/NIRCam observations of NGC 4485/NGC 4490, obtained as part of the Cycle 1 program Feedback in Emerging extrAgalactic Star clusTers (FEAST; PI: A. Adamo). Leveraging the unprecedented sensitivity and spatial resolution of JWST in the near-infrared, we characterized the resolved stellar populations of NGC 4485, NGC 4490, and their tidal bridge down to several magnitudes below the tip of the red giant branch. Building on that work, in this follow-up study we derive the spatially resolved SFHs of the system using the JWST/NIRCam CMDs and our CMD-fitting code SFERA 2.0 (G. Bortolini et al. 2024b). Our main goal is to determine whether distinct bursts of star formation can be recovered and associated with the interaction history of the system, and to investigate how star formation has proceeded across the galaxies and their tidal bridge over cosmic time.

The paper is organized as follows. In § 2, we describe the observations, data reduction, and point-spread function (PSF) fitting photometry. The observed CMDs and main stellar evolutionary phases are presented in § 3. In § 4 we outline SFERA 2.0 CMD-fitting procedure and methods. In § 5 we present the derived star formation histories, while in § 6 we discuss the implications of our results and present a summary of our findings and conclusions.

Data reduction

The observations analyzed in this study have been performed with the NIRCam instrument (M. J. Rieke et al. 2023) onboard the JWST, as part of the cycle 1 GO 1783 program FEAST (PI: A. Adamo) ¹. The observational strategy and all data products of the FEAST collaboration are presented in detail in (B. Gregg et al. 2024; A. Pedrini et al. 2024; A. Knutsen et al. 2025; M. Correnti et al. 2025b; G. Bortolini et al. 2025). Here we will give a brief recap of the strategy and reduction of the observations used in this article.

The NIRCam observations (footprint shown in Figure 1) were taken with a FULLBOX 4TIGHT primary dither and SHALLOW4 readout pattern, along with 2 small dithers resulting in 8 single exposures covering 6′ × 2.2′ ($\sim13 \times5$ kpc) when combined. This resulted in deeper coverage in some regions of the mosaic with respect to others. Of all the NIRCam filters available for this target in FEAST, the two short wavelength filters F115W and F200W, with a total exposure time of 2920 s, represent the deepest and best combination to study the resolved stellar populations (G. Bortolini et al. 2025). In this paper we used both the drizzled, distortion-corrected mosaics (i.e., _i2d.fits), which result from stage three of the JWST pipeline, and the calibrated science images (i.e., _cal.fits), which are the output of the stage two JWST pipeline. Figure 1 shows our NIRCam mosaic in the F115W (blue channel), F200W (green channel) and F444W (red channel) filters and its field of view compared to the SDSS (D. G. York et al. 2000) optical-infrared image of the same target. The positions of the two dwarf galaxies are labeled. The two shorter wavelength filters probe mainly the stellar continuum, while the F444W is sensitive to the hot dust component of the interstellar medium (ISM) present around young star forming regions.

PSF-fitting photometry

A detailed description of the FEAST PSF-fitting photometric reduction pipeline is provided in M. Correnti et al. (2025b) and G. Bortolini et al. (2025). Here, we briefly summarize the main steps. To speed up the photometry reduction we divided the field of view into 18 equally sized ($\sim60^{\prime\prime} \times48^{\prime\prime}$, or $\sim2.1 \times1.7$ kpc) boxes (hereafter ‘tiles’, see Figure 2). In this paper, we adopt the same naming convention for the different tiles introduced in G. Bortolini et al. (2025). We used the 2.1 version of the photometry package DOLPHOT (A. E. Dolphin 2000; A. Dolphin 2016), and its JWST/NIRCam module (D. R. Weisz et al. 2024) to reduce simultaneously the F115W and F200W filters to reach the maximum depth possible in each tile. The photometry was run with DOLPHOT parameters set to values optimized for JWST/NIRCam crowded images (D. R. Weisz et al. 2024; M. Correnti et al. 2025b; G. Bortolini et al. 2025). The output catalogs contain the measured positions, apparent magnitude in the F115W and F200W filters, signal to noise ratio of each source, plus some diagnostic parameters such as sharpness, crowding, roundness, chi2, quality_flag, and object_type (we refer the reader to the DOLPHOT manual for an in-depth description of the meaning of these quantities).

To maximize the number of good candidate stars, while at the same time reduce the number of spurious detections (i.e. background galaxies, blends) we culled all 18 tile catalogs separately. We required all retained sources in each catalog to have: object_type = 1, quality_flag $\le2$, SNR $> 2$ in both the F115W and F200W filters. In each tile, the sharpness cuts have been derived from the sharpness vs. magnitude distribution of the detected sources, keeping only the sources with $\lvert\mathrm{sharpness}\rvert< 0.075$ or within $\pm0.5\sigma$ of their running mean sampled on 0.5 mag intervals. For the crowding we selected only sources with crowding $< 0.1$ or within $1\sigma$ from the running mean of their distribution. Figure 3 shows an example of the sharpness and crowding cuts we applied for the sources detected in tile 1. The sources that passed the initial object_type, quality_flag, and SNR cuts are shown with gray dots, while the sources that pass the sharpness and crowding selections for each filter are marked in black. The sharpness and crowding limits are marked with green and cyan dashed lines, while the running means of the distributions are shown in red.

Artificial star tests

A careful estimation of the photometric errors and depth of the photometry is an essential step for a correct analysis and application of the synthetic CMD method (M. Cignoni & M. Tosi 2010) that we will describe in § 4. To estimate the photometric errors and completeness of our catalogs, we simulated 1 million artificial stars for each tile (for a total 18 million artificial stars), following the light profile of the image and carefully sampling all the color and magnitude range covered by our sources. Each artificial star was injected in each tile separately using the DOLPHOT built-in routine for artificial star tests, and the reduction was performed following the same steps and using the same parameters of the original photometry. The final artificial stars catalog for each tile is obtained after culling it with the same cuts that have been applied to the original photometry, and contains the original and recovered positions as well as the magnitudes in each filter. For instance in tile 1, $\sim550{,}000$ artificial stars have passed the quality cuts, out of the $\sim1$ million injected. The difference between the recovered (i.e., output) and the original (i.e. input) magnitude gives us an estimation of the photometric errors as a function of magnitude and color. The number of recovered vs. the number of injected artificial stars gives us an estimation of the completeness of the photometry, that takes into account the natural varying crowding conditions of the galaxy. As an example of the artificial star test results, Figure 4 shows the output–input vs. input magnitude distribution of the artificial stars in tile 1 that passed the quality cuts for both the F115W and F200W filters. Due to the high concentration of sources the distribution is binned and color-coded according to the number of artificial stars that fall in each bin. The red dashed lines represent the running 16th, 50th, and 84th percentile of the distributions.

The artificial stars allow us to probe how the photometric errors and completeness of our data vary not only in the color-magnitude space, but also (and crucially) spatially across the image. Using our $\sim18$ million artificial stars we can construct a spatially resolved completeness map of our observation field of view, presented in Figure 5. Each pixel of the map covers an area of $1.7^{\prime\prime} \times1.4^{\prime\prime}$ ($\sim60 \times50$ pc), and is color coded according to the magnitude in the F200W filter at which the 50% completeness limit is reached (see colorbar on the right hand side). Figure 6 shows some zoom-ins of the regions covered by tile 1 (i.e., encompassing the center of NGC 4485), tile 5 (i.e., NGC 4485’s side of the tidal bridge), and tile 9 (i.e., NGC 4490’s side of the tidal bridge) at the same pixel scale of Figure 5. As can be seen, the completeness maps have a complex morphology, going from very incomplete regions ($m_{50\% \mathrm{comp}} \sim22$–$24$), that nicely match with regions of high star density and star-forming regions in the F200W image, to more complete ones ($m_{50\% \mathrm{comp}} \sim26$–$28$), as we move towards less crowded regions of the galaxy. Such a complex spatial morphology makes the CMD fitting process very challenging. To ensure an optimal fit of the CMD, and therefore a reliable recovery of the SFH, we divided each of the 18 tiles into several sub-regions, delimited by the solid lines in Figure 6. The sub-regions are defined such that the magnitude corresponding to the 50% completeness level varies by no more than $\sim1.5$ mag within each region, thereby ensuring a relatively uniform completeness within each region. For example, tile 5 and tile 9 are divided into four sub-regions because they contain both highly crowded (and therefore more incomplete) star-forming regions and more sparsely populated areas. Region 1 (R1) is enclosed by the solid black contours; Region 2 (R2) lies between the black and magenta contours; Region 3 (R3) is located between the magenta and yellow contours; and Region 4 (R4) corresponds to the remaining area outside the yellow contour. In contrast, tile 1 is divided into only three sub-regions (R1, R2, and R3), reflecting the more gradual variation in completeness across the tile. The subdivision scheme adopted for the remaining tiles is presented in Section B of the Appendix. To ensure a consistent and robust analysis, we derive the SFH of each sub-region independently with SFERA 2.0, and then combine the results to recover the total SFH of the corresponding tile.

Resolved Stellar Populations

In this Section, we briefly describe the main features of the stellar populations in NGC 4485 and NGC 4490 revealed by the near-infrared CMDs. For a detailed description of the NIRCam CMDs and the different stellar populations present in each tile of our NGC 4485/NGC 4490 mosaic we refer to G. Bortolini et al. (2025).

Figure 7 shows the CMDs for the sources that passed our photometry quality cuts (see § 2.1) for tile 1, 5, 9 and 17, probing parts of the main bodies of the two galaxies (i.e., tile 1 and 17) and their tidal bridge (i.e., tile 5 and 9, see Figure 2). The main stellar evolutionary phases present and described below are marked by red arrows and labeled in the Figure. The total number of sources recovered in each tile is also reported in the bottom-right corner of each panel. The average photometric errors, estimated from our ASTs, at $m_{\mathrm{F115W}} - m_{\mathrm{F200W}} = 1$ and for different magnitude values are shown as red error-bars.

A blue vertical sequence at $m_{\mathrm{F115W}} - m_{\mathrm{F200W}} \sim0$, composed of young upper main-sequence (MS) stars and more evolved core He-burning (He-b) stars in the blue phase of their evolutionary loops, is clearly visible in each tile. Unfortunately, the intrinsically similar colors in the near-infrared of these stars, convolved with the photometric errors and distance of the target, do not allow a confident separation between these two evolutionary phases. In tiles 1 and 17, these stars are concentrated around $m_{\mathrm{F200W}} \sim25$, whereas in tiles 5 and 9 the sequence is more uniformly populated, extending up to magnitudes as bright as $m_{\mathrm{F200W}} \sim19$.

A red vertical plume, populated by young (age $\lesssim30$ Myr), massive ($8\,M_{\odot} \lesssim M \lesssim30\,M_{\odot}$) He-b red supergiant (RSG) stars, is clearly visible in the tidal bridge (tiles 5 and 9), confirming the presence of a strong and recent starburst. In tiles 1 and 17, a vertical sequence of red He-b stars is still present, although its bright end is significantly less populated.

A red sequence parallel to, but fainter than the red He-b sequence is also distinguishable in all four tiles, around $m_{\mathrm{F115W}} - m_{\mathrm{F200W}} \sim1.25$ and $22.3 \lesssim m_{\mathrm{F200W}} \lesssim23.5$. These are intermediate-mass ($4\,M_{\odot} \lesssim M \lesssim8\,M_{\odot}$) oxygen-rich asymptotic giant branch (AGB) stars. At $m_{\mathrm{F115W}} - m_{\mathrm{F200W}} \sim2$ and $m_{\mathrm{F200W}} \sim22$ a red cloud of lower mass ($1.2\,M_{\odot} \lesssim M \lesssim4\,M_{\odot}$) AGB stars in their thermally pulsing phase (TPAGB) are also visible in all four tiles. During this phase, inner-shell convective episodes known as the third dredge-up bring a significant amount of carbon to the stellar surface. This carbon is responsible for a significant decrease of the effective temperature of the stars and is subsequently expelled into the interstellar medium through strong stellar winds, where it contributes predominantly to the formation of carbonaceous dust. As a consequence, these stars appear redder than their oxygen-rich (O-rich) counterparts. For this reason, they are also referred to as carbon-rich (C-rich) AGB stars. Thanks to the sensitivity and resolution of JWST, these stars can now be resolved with high accuracy in star-forming galaxies outside the Local Group ((A. S. Hirschauer et al. 2024; G. Bortolini et al. 2024a; M. Correnti et al. 2025b; G. Bortolini et al. 2025; M. L. Boyer et al. 2025)), and have been also used to measure distances ((A. J. Lee 2023; S. Li et al. 2024; A. J. Lee et al. 2025a; S. Li et al. 2025; C. Gavetti et al. 2026a,b)). For a more detailed description of this complex evolutionary phase we refer the reader to E. Vassiliadis & P. R. Wood (1993); F. Herwig (2005); P. Marigo et al. (2008); A. Nanni et al. (2013); P. Ventura et al. (2014); P. Marigo et al. (2017); P. Ventura et al. (2018); C. Gavetti et al. (2025) among others.

The AGB stars described above have a wide range of ages, that go from $\sim100$ Myr to $\sim5$ Gyr. We also note the presence of a gap in the distribution of AGB stars between the vertical sequence of O-rich stars and the C-rich cloud, and a vertical finger brighter than $m_{\mathrm{F200W}} \sim22$, that lies above the O-rich sequence and runs parallel but slightly redder of the sequence of more massive RSG stars. For a detailed description and interpretation of these features from a stellar evolution point of view, we refer the reader to M. Correnti et al. (2025b) and Dell’Agli et al. (in prep), in which the authors use the state-of-the-art AGB stellar evolution and dust formation code ATON (P. Ventura et al. 1998, 2014, 2018, 2020) these features in NGC 4449 (a starburst dwarf galaxy in the FEAST sample).

Last but not least, a population of old (age $\geq1$ Gyr) and low mass ($M \lesssim1.0\,M_{\odot}$) RGB stars dominate the faint and red region of the CMDs of all four tiles. The indicative location of the tip of the RGB is marked at $m_{\mathrm{F200W}} \sim23.5$, and seems to not change from tile to tile within the uncertainties of our photometry (see also discussion in § 4).

To illustrate how photometric completeness affects the different sub-regions within each tile, Figure 8 shows as an example the CMDs for the three different sub-regions of tile 1 and the four sub-regions of tile 5, going from most crowded (i.e., less complete) to least crowded (i.e., more complete) from left to right. Over-plotted on each CMD are PARSEC–COLIBRI stellar isochrones (A. Bressan et al. 2012; P. Marigo et al. 2017; G. Pastorelli et al. 2020), color-coded by age going from 10 Myr to 10 Gyr. The isochrones with age $\leq1$ Gyr have

a metallicity of [Fe/H] $= -0.30$, whereas the older ones are more metal-poor to match the color of the RGB ([Fe/H] $= -1.20$). The TPAGB phase tracks for stars older than 100 Myr are shown with a lower opacity to improve readability. Absolute magnitudes have been calculated from theoretical luminosities and temperatures using the bolometric corrections from Y. Chen et al. (2019), and then converted to apparent magnitudes assuming a distance modulus of $(m - M)_0 = 29.50$ and a foreground reddening of $E(B - V) = 0.10$ (D. Calzetti et al. 2015; M. Cignoni et al. 2019). The shaded region of the CMDs in each panel indicates $< 50\%$ completeness, derived from artificial stars within that specific sub-region. The R1 sub-region is the one with the shallowest CMD, being the most crowded area (see Figure 6), while R2, R3, and R4 CMDs become progressively deeper, reaching down to $m_{\mathrm{F200W}} \sim26$ and $m_{\mathrm{F200W}} \sim27$ in region 3 of tile 1 and region 4 of tile 5, respectively.

Most importantly for the analysis of the SFH of the system, this variation in the photometry completeness affects the age of the faintest He-b stars detectable in each region. Indeed, during the core He burning phase intermediate mass stars ($M \gtrsim2M_{\odot}$) exhibit large loops in colors (the so-called blue loops) and their luminosities are proportional to their mass. Because of this characteristic, the loops become fainter with increasing age between 100 and 500 Myr, which makes these loops an excellent indicator of stellar age in distant star-forming galaxies. In the most crowded regions of tile 1 (i.e., R1), @page 8/27 starts_mid=1 ends_mid=1

also note the presence of a gap in the distribution of AGB stars between the vertical sequence of O-rich stars and the C-rich cloud, and a vertical finger brighter than $m_{\mathrm{F200W}} \sim22$, that lies above the O-rich sequence and runs parallel but slightly redder of the sequence of more massive RSG stars. For a detailed description and interpretation of these features from a stellar evolution point of view, we refer the reader to M. Correnti et al. (2025b) and Dell’Agli et al. (in prep), in which the authors use the state-of-the-art AGB stellar evolution and dust formation code ATON (P. Ventura et al. 1998, 2014, 2018, 2020) these features in NGC 4449 (a starburst dwarf galaxy in the FEAST sample).

Last but not least, a population of old (age $\geq1$ Gyr) and low mass ($M \lesssim1.0\,M_{\odot}$) RGB stars dominate the faint and red region of the CMDs of all four tiles. The indicative location of the tip of the RGB is marked at $m_{\mathrm{F200W}} \sim23.5$, and seems to not change from tile to tile within the uncertainties of our photometry (see also discussion in § 4).

To illustrate how photometric completeness affects the different sub-regions within each tile, Figure 8 shows as an example the CMDs for the three different sub-regions of tile 1 and the four sub-regions of tile 5, going from most crowded (i.e., less complete) to least crowded (i.e., more complete) from left to right. Over-plotted on each CMD are PARSEC–COLIBRI stellar isochrones (A. Bressan et al. 2012; P. Marigo et al. 2017; G. Pastorelli et al. 2020), color-coded by age going from 10 Myr to 10 Gyr. The isochrones with age $\leq1$ Gyr have

a metallicity of [Fe/H] $= -0.30$, whereas the older ones are more metal-poor to match the color of the RGB ([Fe/H] $= -1.20$). The TPAGB phase tracks for stars older than 100 Myr are shown with a lower opacity to improve readability. Absolute magnitudes have been calculated from theoretical luminosities and temperatures using the bolometric corrections from Y. Chen et al. (2019), and then converted to apparent magnitudes assuming a distance modulus of $(m - M)_0 = 29.50$ and a foreground reddening of $E(B - V) = 0.10$ (D. Calzetti et al. 2015; M. Cignoni et al. 2019). The shaded region of the CMDs in each panel indicates $< 50\%$ completeness, derived from artificial stars within that specific sub-region. The R1 sub-region is the one with the shallowest CMD, being the most crowded area (see Figure 6), while R2, R3, and R4 CMDs become progressively deeper, reaching down to $m_{\mathrm{F200W}} \sim26$ and $m_{\mathrm{F200W}} \sim27$ in region 3 of tile 1 and region 4 of tile 5, respectively.

Most importantly for the analysis of the SFH of the system, this variation in the photometry completeness affects the age of the faintest He-b stars detectable in each region. Indeed, during the core He burning phase intermediate mass stars ($M \gtrsim2M_{\odot}$) exhibit large loops in colors (the so-called blue loops) and their luminosities are proportional to their mass. Because of this characteristic, the loops become fainter with increasing age between 100 and 500 Myr, which makes these loops an excellent indicator of stellar age in distant star-forming galaxies. In the most crowded regions of tile 1 (i.e., R1),

the oldest resolved He-b loop stars have ages around $\sim100$ Myr, while in the least crowded regions (i.e., R3) have ages of $\sim300$ Myr. For tile 5 we see that in the most crowded region (i.e., R1), the oldest resolved He-b loop stars have ages around $\sim200$ Myr, while in the least crowded region (i.e., R4) have ages of $\sim500$ Myr.

Where He-b stars in the 100–500 Myr age range are not resolved (i.e. under our completeness limit), we must rely on more massive stars in later evolutionary stages, like AGBs, that trace the same stellar ages. Unfortunately, despite being bright and thus easy to detect in distant galaxies, AGB stars are less sensitive to age than He-b. As shown by the isochrone in Figure 8, AGB stars of different ages are harder to disentangle due to being confined in a tight color and magnitude region of the CMD. Moreover, the TPAGB phase is still the most uncertain among all the single stars main evolutionary phases (P. Marigo et al. 2008, 2013, 2017). In this phase, stars go through a series of processes that are highly complex to model from first principles and that are mutually dependent, like stochastic mass losses, long period pulsations, hot-bottom burning, and the many third dredge-up episodes (see e.g., C. A. Frost & J. C. Lattanzio 1996; N. Mowlavi 1999; P. Ventura & F. D’Antona 2005; F. Herwig 2005; P. Marigo et al. 2013; P. Ventura et al. 2014). These uncertainties and difficulties translate into substantial differences in the predictions of different stellar evolution models (P. Marigo et al. 2017), and in turn makes the recovered SFH based only on this phase more uncertain (C. Gallart et al. 2005; M. Cignoni & M. Tosi 2010). Due to the distance of the galaxies from us, we do not resolve He-b stars older than 500 Myr in any of the sub-regions. Therefore, to probe the epoch between 500 Myr and 1 Gyr we have no other choice than to rely on AGB stars.

For epochs older than $\sim1$ Gyr, our best tracers are instead RGB stars, that can be reliably resolved below the tip in each sub-region due to their brightness. Naturally, the variations in crowding conditions affect what populations can be resolved with confidence in similar ways in all the other tiles. Particular attention should be given to the central region of NGC 4490 (i.e., tiles 11 and 14). Owing to the severe stellar crowding in this area, the average completeness limit is only $\sim23$ mag, causing only BL stars younger than $\sim60$ Myr to lie above our detection threshold, hindering our ability to safely recover the SFH at intermediate ages. In addition, RGB stars associated with older stellar populations are also largely below the completeness limit, making it impossible to reliably recover the SFH at old epochs. For these reasons, we exclude these tiles from the SFH analysis (see discussion below).

CMD-fitting routine

With both the photometric and artificial star catalogs in hand, we can recover the SFH of the interacting system NGC 4485/NGC 4490 using the synthetic CMD method. This technique, pioneered by A. Maeder (1974), C. Chiosi et al. (1988), M. Tosi et al. (1991), G. Bertelli et al. (1992) among others, has since become a widely adopted and extensively tested approach over the past three decades to study the formation history of dwarf galaxies both inside and outside the Local Group (e.g., C. Gallart et al. 1999; F. Annibali et al. [2]; A. E. Dolphin et al. 2005; A. A. Cole et al. 2007; M. Monelli et al. 2010; K. B. W. McQuinn et al. 2010; D. R. Weisz et al. 2014; E. Sacchi et al. 2016; E. D. Skillman et al. 2017; E. Sacchi et al. 2018; M. Cignoni et al. 2019; E. Sacchi et al. 2021; A. Savino et al. 2023; K. B. W. McQuinn et al. 2024; E. Fernández-Alvar et al. 2025; C. Burhenne et al. 2026, among many others).

Several codes that implement this technique have been developed over the years, including StarFISH (J. Harris & D. Zaritsky 2002), MATCH (A. E. Dolphin 2002), IAC-pop (A. Aparicio & S. L. Hidalgo [5]), SFERA (M. Cignoni et al. 2015; E. Sacchi et al. 2018), Talos (T. J. L. de Boer & M. Fraser 2016), TheStorm (E. J. Bernard et al. 2018), ChronoGal (C. Gallart et al. 2024), StarFormationHistories.jl (C. T. Garling et al. 2025), and PANCAKE (Y. Zheng et al. 2025). Although these codes differ in their specific implementations and optimization strategies, they share the same fundamental principles and overall methodology. In this work, we employ an updated version of the stellar population recovery algorithm SFERA (Star Formation Evolution Recovery Algorithm), named SFERA 2.0, which was developed and optimized to fit the bright stellar populations (e.g., upper MS, He-b and AGB stars) that can be observed in the CMDs of star-forming dwarf galaxies outside the Local Group (G. Bortolini et al. 2024a,b).

The first step of the routine is to generate a library of synthetic CMDs for simple stellar populations, covering a small range in age and metallicity, and interpolating an adopted library of stellar isochrones. We refer the interested reader to G. Bortolini et al. (2024b) for an in depth description on how the interpolation in mass and age of the isochrones and how unresolved binary systems are implemented in the code to uniformly sample all stellar evolutionary phases. In this paper we used the PARSEC-COLIBRI stellar isochrones library (A. Bressan et al. 2012; P. Marigo et al. 2013, 2017; G. Pastorelli et al. 2020), which calculates in great detail all evolutionary phases for stars in a wide range of masses and metallicities².

The “simple” stellar population synthetic CMDs are generated to cover all the possible combinations of 9 non uniform age bins, between $\log(\mathrm{age}/\mathrm{yr}) = 7.00$ and 10.13, and 13 uniform metallicity bins between $[\mathrm{Fe}/\mathrm{H}] = -1.50$ and $-0.30$ in steps of $0.1$ dex, for a total of 117 synthetic CMDs. We adopted an age resolution $\Delta\log(\mathrm{age}/\mathrm{yr}) = 0.25$ for $\log(\mathrm{age}/\mathrm{yr}) < 8.00$, $0.35$ for $8.00 \le\log(\mathrm{age}/\mathrm{yr}) < 9.05$, $0.45$ for $9.05 \le\log(\mathrm{age}/\mathrm{yr}) < 9.50$, and one last bin for older epochs. The different lengths of the age bins are carefully selected to account for the natural loss of time resolution that occurs for older epochs (see discussion in § 3), and at the same time minimize the stochastic correlation between adjacent bins (see also Appendix § 11). We chose not to include ages younger than 10 Myr because the $\mathrm{m}_{\mathrm{F115W}} - \mathrm{m}_{\mathrm{F200W}}$ color is not particularly efficient at separating stars with those ages, plus we expect to miss most of such young stars due to them being still associated in dense, unresolved young star clusters. The synthetic CMDs are then populated assuming a Kroupa IMF (P. Kroupa 2001) from 0.2 and 300 $M_{\odot}$, while assuming a 30% binary fraction (M. Cignoni et al. 2009), and a set of bolometric corrections to go from theoretical luminosities and temperatures to absolute magnitudes in the F115W and F200W filters (Y. Chen et al. 2019).

The second, and crucial step is to simulate the observational conditions. First the absolute magnitudes are converted to apparent ones by placing the synthetic stellar populations at the distance of the target galaxy, and correcting for reddening due to different star to star line of sight. The reddening distribution is modeled as a Gaussian (with negative values excluded) with a mean value representing the foreground reddening (E(B − V)) and dispersion representing the internal/differential reddening ($\sigma_{\mathrm{E(B-V)}}$) as free parameters in the code. The synthetic CMDs are then convolved with the photometric uncertainties and completeness characterizing the sub-regions of each tile under analysis, obtained through our extensive ASTs.

The last step is to compare a linear combination of our synthetic CMDs, that with appropriate weights can reproduce the complex stellar population, to the galaxy’s observed CMD. To do so, we binned both synthetic and observed CMDs in color and magnitude and compare the star counts in each CMD cell. For all tiles and relative sub-regions we adopted a bin size of 0.125 in color and 0.05 in magnitude.

To find the best set of weights, we define the ‘distance’ between our models and the data using the following Poissonian likelihood function:

where $obs_k$ and $mod_k$ are the number of stars in the data and model $k$ CMD cell. This Poissonian likelihood has been demonstrated by several authors to be the optimal choice in CMD fitting problems (K. J. Mighell 1999; A. E. Dolphin 2002). To minimize this likelihood, SFERA 2.0 uses a hybrid-genetic minimization algorithm, i.e., a mix between a classical FORTRAN-based genetic algorithm called Pikaia (P. Charbonneau 1995)³ and a local-search algorithm based on the Simulated Annealing method (S. Kirkpatrick et al. 1983). The best model SFH is represented by the set of weights that produced the model CMD that minimizes the Poissonian likelihood when compared to the observed one.

We performed SFH calculations over a coarse grid of distance, E(B − V), and $\sigma_{\mathrm{E(B-V)}}$ for the sub-regions of

each tile of our mosaic. The combinations of values that returned the highest likelihood value are assumed as our best-fit values. In our particular case, we explored values of E(B − V) = [0.0, 0.10, 0.20] and $\sigma_{\mathrm{E(B-V)}}$ = [0, 0.10, 0.20, 0.30] (D. Calzetti et al. 2015; M. Cignoni et al. 2019). Regarding the distance modulus, we explore different values within the range [29.06–29.71] reported by E. Sabbi et al. (2018). Somewhat unsurprisingly given the interacting nature of the system, we obtained the best-fit results in each tile with the same value of $(m-M)_0 = 29.50$ and foreground reddening E(B − V) = 0.10. The best-fit values of differential reddening varies instead from one sub-region to the next. In the fit, we did not impose any particular age-metallicity relation.

The statistical uncertainties are calculated through a bootstrap technique. We randomly re-sample the stars in the observed CMDs multiple times, and derive again the SFH for each data re-sampling. We then calculate the 16th-84th percentiles of the solution distributions as confidence intervals.

The Adequacy of AGB Stellar Evolution Models

It is common practice in CMD-fitting SFH studies to estimate systematic uncertainties by generating synthetic CMDs with multiple sets of stellar evolution models and comparing the recovered SFHs, while keeping all other parameters fixed. PARSEC-COLIBRI (A. Bressan et al. 2012; P. Marigo et al. 2017), MESA-MIST (B. Paxton et al. 2011; J. Choi et al. 2016; A. Dotter 2016), and BaSTI (D. Cordier et al. 2007; S. L. Hidalgo et al. 2018) are among the most widely used stellar isochrone libraries in CMD analyses of resolved galaxies (see, e.g., C. Gallart et al. 2005; D. R. Weisz et al. 2014; E. Sacchi et al. 2018; M. Cignoni et al. 2019; K. B. W. McQuinn et al. 2024; G. Bortolini et al. 2024a). Although these models generally provide consistent predictions across most evolutionary phases, they differ substantially in their treatment of the TPAGB phase. As discussed above, the TPAGB is one of the most challenging stages of stellar evolution to model self-consistently because of the complex interplay between mass loss, third dredge-up, thermal pulses, hot-bottom burning, dust production, and radiative transfer through circumstellar envelopes. Different modeling groups adopt different physical prescriptions, approximations and calibration strategies, resulting in significant variations in the predicted properties of these stars.

Among the available models cited above, COLIBRI (P. Marigo et al. 2008, 2013, 2017) has the most comprehensive set of isochrones, including the TPAGB evolution. Its calculations include detailed prescriptions for third dredge-up, hot-bottom burning, changes in low-temperature opacities, stellar pulsation properties, mass-loss, dust formation, and the interaction of stellar radiation with circumstellar material through radiative transfer calculations. In contrast, the BaSTI and MESA-MIST isochrones do not model several of these processes. As a result, their TPAGB predictions are generally hotter and bluer than those of COLIBRI and fail to reproduce many of the complex features observed in the CMDs of star-forming galaxies (see Section 3.8 and Figure 11 of P. Marigo et al. 2017 and the discussion in A. J. Lee et al. 2025b).

For instance, in Figure 9 predictions from PARSEC-COLIBRI (red dots), MESA-MIST (blue dots), and BaSTI (green dots) stellar isochrones with ages of [100, 200, 400, 600, 800] Myr and $Z = 0.008$ (close to that of the LMC’s young stellar population) are shown in the specific NIRCam filters used in this paper. In the observational CMD, the portion of the PARSEC-COLIBRI isochrones populated by C-rich AGB stars is substantially redder compared to the region of the CMD populated by the respective MESA-MIST models that do not predict stars redder than $m_{\mathrm{F115W}} - m_{\mathrm{F200W}} = 1.5$, while the BaSTI models do not include at all C-rich AGB stars.

Although there are several codes that calculate self-consistently the entire structure of the stars during the AGB phase, e.g., MONASH (A. I. Karakas 2014; A. I. Karakas et al. 2022), ATON (P. Ventura et al. 1998, 2014, 2018, 2020), FRUITY (S. Cristallo et al. 2009, 2011, 2015), they do not provide isochrones that cover the full stellar mass range required for our analysis, as they do not include massive stars. Since our galaxies host ongoing star formation, we adopt a homogeneous set of stellar evolution models spanning the entire mass range needed for the SFH recovery. For an interested reader, the application of the ATON models to the population of AGB stars of the irregular dwarf galaxy NGC 4449, is presented in M. Correnti et al. (2025b) and in a forthcoming study by Dell’Agli et al. (in prep).

Given the significant population of red AGB stars in our CMDs, PARSEC-COLIBRI is the only stellar isochrones set used for the SFH recovery presented in this work. Nevertheless, for completeness, we provide a comparison between the SFHs recovered using the PARSEC-COLIBRI and MESA-MIST models with and without the inclusion of AGB stars in Section A of the Appendix. While we emphasize that the latter lacks the detailed TPAGB treatment required for a realistic description of these stellar populations, this comparison illustrates the systematic differences that arise from adopting alternative stellar evolution libraries and including or not the AGB stars in the fitting.

Star Formation Histories

In Figure 10, we compare the observed CMDs with SFERA 2.0 best-fit model CMDs for the three sub-regions of tile 1: region 1 (R1, upper panels), region 2 (R2, middle panels), and region 3 (R3, lower panels; see also Figure 6). The CMDs are binned using the same color and magnitude bin sizes adopted in the fitting routine. Each bin is color-coded according to the number of stars, ranging from white (low density) to black (high density). White iso-density contours corresponding to stellar counts of [1, 5, 15, 20, 30, 50] are also overlaid to help the reader identify and compare the main stellar features in the CMDs.

For each CMD bin, we show the residuals between the observed stellar counts and the predictions of the SFERA 2.0 best-fit model, expressed in units of the Poisson uncertainty (i.e., $(o_{\mathrm{obs}} - m_{\mathrm{odk}})/\sqrt{m_{\mathrm{odk}}}$). Sources lying below the 50% completeness limit in each sub-region are excluded from the fit and are therefore not shown in the figure, producing the sharp cutoff visible at the faint end of the diagrams.

The SFERA 2.0 best-fit models successfully reproduce the main features of the observed CMDs in all three sub-regions. This includes the young upper-MS and He-burning populations, the intermediate-age AGB stars, and the RGB population. The most noticeable discrepancy is found in the locus of the O- and C-rich AGB stars, which appears systematically brighter in the mod-

els than in the observations, in particular for the deeper CMDs of region 2 and 3 which provide stronger constraints. This offset is likely related to the complex effects of circumstellar dust produced during the intense stochastic mass-loss episodes experienced by these stars throughout their thermal-pulsing evolution. The resulting dust envelopes can significantly attenuate and redden the stellar flux. As discussed in § 4.1, this evolutionary phase remains particularly challenging to model self-consistently. Nevertheless, the PARSEC-COLIBRI stellar evolution models reproduce well the color and overall number of stars populating this region of the CMD. Similar fit quality is obtained for all other tiles and their respective sub-regions, indicating that the SFERA 2.0 best-fit models provide a consistently good representation of the observed CMDs across the entire dataset.

Figure 11 presents the SFERA 2.0 derived SFHs for each tile of our NIRCam F200W mosaic, excluding the central tiles (11 and 14) of NGC 4490. Note that the x-axis shows the logarithm of the age, while the y-axis shows the star-formation rate in units of $M_{\odot}\,\mathrm{yr}^{-1}$, using the same scale and limits for all tiles to facilitate a direct comparison of the SFRs across different regions of the system.

The first feature that catches the eye is a strong enhancement in the recent star formation activity (ages between $\sim10$–30 Myr) in the tidal bridge region (tiles 5 and 9) and throughout much of the main body of NGC 4490 (e.g., tiles 10, 12, 13, 15, and 16). In contrast, the center of NGC 4485 (e.g., tile 1) shows recent star formation, but at substantially lower rate. Significant star formation activity is also observed at intermediate ages (200 Myr $\lesssim$ age $\lesssim$ 1 Gyr), particularly in the central region of NGC 4485 (tile 1), the tidal bridge (tiles 5 and 9), and across most of NGC 4490. This burst of star formation is especially evident when compared with the generally lower SFRs inferred at lookback-times older than $\sim1$ Gyr. Tiles 3 and 7 exhibit significantly lower star formation activity over the entire lookback time than the other tiles analyzed, indicating that they have formed substantially less stellar mass.

To highlight these trends more clearly, Figure 12 shows the combined SFHs of three distinct regions: NGC 4485, represented by tiles 1, 2, and 4 (in blue); the tidal bridge, represented by tiles 5, 6, 8, and 9 (in red); and the main body of NGC 4490, excluding its central region, represented by tiles 10, 12, 13, 15, 16, 17, and 18 (in green). Tiles 3 and 7 are excluded because their association with any of these regions is ambiguous. However, their contribution to the combined SFHs is negligible owing to their relatively low SFRs. To enable a meaningful comparison between the three regions, the SFHs were normalized by the total area in kpc$^2$ covered by each group of tiles, thereby accounting for their different spatial extents.

All three regions exhibit SFHs broadly similar in overall shape. They are characterized by relatively low SFRs at times older than $\sim1$ Gyr, followed by a pronounced enhancement in star formation at intermediate ages, between 200 Myr $\lesssim$ age $\lesssim$ 1 Gyr, with the peak around 500–800 Myr ago. After this episode, the SFR declines, reaching a local minimum, before rising again to reach its highest value during the most recent time bin at ages between $\sim10$–30 Myr ago. Despite these similarities, the relative strengths of the star forming episodes differ significantly when compared to the average SFR of each region, weighted by its total stellar mass, indicated by the gray-shaded area in each panel. Indeed the older SFR peak in NGC 4485 is $\sim2$ times stronger than the average SFR, while the youngest peak reaches values up to $\sim4$ times the average. NGC 4490 has an older peak $\sim4$ times stronger than the average, while the youngest peak is $\sim7$ times stronger. The tidal bridge is the region in which the differences between the peaks of SFRs are the highest if compared to the averaged SFR of the region, with the oldest and youngest peaks in star-formation activity $\sim6$ and $\sim15$ times higher, respectively.

Another notable difference is that the enhancement in SFR at intermediate-ages in NGC 4485 and NGC 4490 appears to have persisted for a longer period, extending to epochs around 60 Myr ago. In contrast, the corresponding activity in the tidal bridge seems to have been declining already $\sim200$ Myr ago, before increasing again in the two youngest age bins. For reference, the predicted epochs of the first ($\sim1.3$ Gyr ago) and second ($\sim200$ Myr ago) pericenter passages from the independent N-body simulation of S. Pearson et al. (2018) are indicated by the vertical gray-dashed lines. In all three regions, the two recovered peaks of star formation in our SFHs occur in time bins corresponding to a few hundred million years after the predicted times of the close encounters between the galaxies. This suggests a temporal offset between the encounters and the subsequent star formation activity, although the coarse time resolution of our SFHs, together with the uncertainties in the predicted encounter times, prevents us from precisely quantifying this delay (see the discussion in § 3 and Appendix § 11).

Finally, in Figure 13 we show the total SFH of the NGC 4485/NGC 4490 system, after summing all the tiles in our field of view, again with the important caveat that the center of NGC 4490 (tile 11 and 14) is excluded.

As for the SFHs in Figure 12, the mass-weighted average SFR is shown by the gray-shaded area, while the position of the two perigalactic passages predicted by S. Pearson et al. (2018) are marked by the gray-dashed vertical lines. The two peaks in star formation activity are still clearly visible in the global SFH of the system, with the oldest peak $\sim4$ times higher than the averaged SFR, and the youngest one $\sim7$ times stronger than the average.

Discussion and Conclusions

The spatially resolved star-formation histories of NGC 4485 and NGC 4490 (see Figure 11) nicely demonstrate how star formation in low-mass and gas-rich interacting galaxies is widely spread across the two galaxies. In particular, NGC 4485 shows prominent sites of enhanced young star formation activity displaced from its central region (i.e., tiles 2 and 5), in agreement with that found by recent UV observations (M. Cignoni et al. 2019), and co-spatial with the presence of dense gas also displaced from its center (Cannon et al. in prep, see Figure 2). This behavior contrasts markedly with the centrally concentrated nuclear starbursts commonly associated with mergers of massive spiral galaxies ([10] (J. C. Mihos & L. Hernquist 1994; S. L. Ellison et al. 2013; M. D. Thorp et al. 2019)). Tile 9, 12, 13, and 16 in NGC 4490 exhibit some of the highest gas densities (see Figure 2), that match with the strongest signatures of recent star-formation activity recovered by SFERA 2.0 (see Figure 11). However, because the two central tiles of NGC 4490 are not included in our spatially resolved SFH analysis, we cannot assess the recent star-formation activity in its central regions directly.

Interestingly, the combined SFHs of NGC 4485, the tidal bridge, and NGC 4490 shown in Figure 12, as well as the total SFH presented in Figure 13, exhibit two distinct peaks in star formation, several times stronger than the average SFR. These peaks occur a few hundred million years after the time of first and second perigalactic passages predicted by the N-body simulations of S. Pearson et al. (2018) (gray-dashed vertical lines). This behavior is consistent with state-of-the-art hydrodynamical simulations of interacting, gas-rich, low-mass galaxies resolved down to massive stars (N. Lahén et al. 2019, 2020). These models predict enhancements in the global star-formation rate following pericenter passages, particularly within the tidal bridge, where interaction-induced gas compression can promote elevated star-formation activity. They also predict the formation of star-forming regions displaced from the galactic centers (see Figure 2 and 5 of N. Lahén et al. 2020), in good agreement with the spatial distribution of the recent star-formation clumps observed in the NGC 4485/4490 system (i.e., tiles 5, 9, 10, 13, 15, 16).

Taken together, our spatially resolved SFHs and the observed morphology of recent star-forming clumps are consistent with current theoretical predictions for dwarf–dwarf interactions, suggesting that repeated tidal interactions may have played a key role in triggering the major episodes of enhanced star formation observed in the system.

The results described above allow us to suggest the following qualitative scenario for the evolution of this system. At the earliest epochs the two galaxies were not interacting, and had a rather low SFR. The bridge did not exist, and its gas was still part of the two galaxies. As shown in Figure 12, around 3 Gyr ago, the two galaxies experienced a factor of $\sim2$ increase in the star formation rate per unit area (SFR/area). At this stage, the two galaxies had not yet interacted and were forming stars primarily within their main bodies. The gradual increase in their SFR can therefore be understood as a natural consequence of their growing stellar mass, as expected for star-forming galaxies evolving along the star-forming main sequence. As gas accretion continued, the galaxies increased in mass, leading to a corresponding increase in their star-formation activity. The values of the SFR/area ranged from $10^{-3}$ M$_\odot$ yr$^{-1}$ kpc$^{-2}$ for NGC 4490 to $4 \times10^{-4}$ M$_\odot$ yr$^{-1}$ kpc$^{-2}$ for NGC 4485, or $1$ M$_\odot$ Gyr$^{-1}$ pc$^{-2}$ and $0.4$ M$_\odot$ Gyr$^{-1}$ pc$^{-2}$, respectively.

Assuming a typical gas consumption timescale of 1 Gyr, and a gas surface density of $\sim1\,M_{\odot}\,\mathrm{pc}^{-2}$, typical for dwarfs, the SFR/area in NGC 4490 was then rather standard (B. G. Elmegreen & D. A. Hunter 2026; B. G. Elmegreen et al. 2026). Taking into account that the consumption timescale tends to increase for the smallest systems because their low metallicities and pressures prevent an efficient formation of giant molecular clouds (see, e.g., F. Bigiel et al. 2010), NGC 4485 appears to have had a rather normal SFR/area at that early epoch as well.

About 1 Gyr ago the first interaction occurred (S. Pearson et al. 2018), and likely triggered the formation of the bridge and the first burst. According to Figure 12, the duration of this burst was different in the two galaxies and their bridge. We interpret this difference again in terms of the different gas consumption timescales, which allowed the burst to last $\sim0.8$ Gyr in NGC 4490, and about twice as long in NGC 4485. The drop of the SFR/area in the bridge was much faster than in the two galaxies, presumably because its surface gas density was still too low to support much star formation on its own.

About 200 Myr ago, when the second interaction probably occurred (S. Pearson et al. 2018), the three regions reacted in different ways: the bigger galaxy, NGC 4490, showed a slow increase in the SFR/area, while the smaller galaxy, NGC 4485, was still experiencing the high SFR of the first burst and did not change its SFR significantly, nor did the bridge show significant SFR enhancements. We can speculate that there was no new gas accreted by the small galaxy and the bridge between the interactions, so there was no fuel for a new burst after the previous episode exhausted their gas. Instead, the bigger galaxy had still sufficient gas, and/or some new gas could have been accreted by it, that could let the SFR increase. The latter possibility is plausible as the center of mass of the system is closer to (or inside) the big galaxy given their factor of 10 difference in stellar mass.

At the current epoch, all the three regions show a clear peak in SFR started around 30 Myr ago and still ongoing. Almost all star-forming dwarfs are characterized by ongoing, or recent, bursts, that make them visible in the first place even at quite large distances, mostly thanks to the brightness of their HII regions and massive stars and young clusters (see e.g., K. B. W. McQuinn et al. 2010; M. Cignoni et al. 2019). While NGC 4490 and the bridge do present concentrations of neutral and molecular gas able to sustain efficient SF (see Figure 2), what is it that allows a SF burst at the border between NGC 4485 and the bridge? Are the current components of tile 5 still attributable to NGC 4485 or have they eventually become part of the bridge and exploit its gas richness? More detailed hydrodynamical simulations of the systems would be needed to answer this question.

The key results of our analysis are summarized as follows.

• We resolved stellar populations across a wide range of evolutionary phases, from the youngest ages probed by upper-MS, RSG, and blue-loop stars, to intermediate-age AGB stars and an old population traced by RGB stars.

• We showed that, among the set of isochrones most commonly used in studies of resolved stellar populations and star formation histories, only the PARSEC-COLIBRI (A. Bressan et al. 2012; P. Marigo et al. 2008, 2013, 2017) models provide a sufficiently detailed treatment of the complex physical processes occurring during the AGB evolutionary phase. Owing to their high luminosities, AGB stars dominate the observed near-infrared CMDs of star-forming galaxies beyond the Local Group, where fainter evolutionary features that trace similar ages, such as the red clump and the oldest main-sequence turn-off, are generally inaccessible. Nevertheless, significant discrepancies between the observed and predicted distributions of AGB stars in our CMDs remain, highlighting the need for further improvements in the modeling of AGB stars.

• The spatially resolved SFHs reveal spatially extended recent star formation across NGC 4485 and NGC 4490, with prominent young star-forming complexes located in the tidal bridge away from the central region of NGC 4485 (i.e. tiles 2 and 5), and reaching the outskirts of NGC 4490 (tiles 9, 10, 12, 13, 15, 16). However, the lack of SFHs for the two central tiles of NGC 4490 prevents us from directly characterizing its star-formation activity in the central regions.

• The SFHs of NGC 4485, NGC 4490, the tidal bridge, and the system as a whole exhibit two significant bursts of star formation, one occurring $\sim10$–$30$ Myr ago and a second peaking at approximately 500–800 Myr ago. These episodes of enhanced star formation occur a few hundred million years after the epochs of the first and second perigalactic passages predicted by the independent N-body simulations of S. Pearson et al. (2018), which took place approximately 1.3 Gyr and $\sim200$ Myr ago, respectively. The tidal bridge shows the highest increase with respect to the averaged

SFR during the bursts, followed by NGC 4490 and NGC 4485.

• Taken together, our results support a scenario in which the NGC 4485/4490 system has experienced at least two major interaction events. These encounters appear to have played a fundamental role in shaping the recent SFH of both galaxies and in producing the actively star-forming tidal bridge observed today. These findings are consistent with recent numerical simulations of interacting gas-rich low-mass galaxies (e.g., N. Lahén et al. 2019, 2020; T. Ejdetjärn et al. 2025). In particular, these simulations predict enhanced star formation following perigalactic passages, both within tidal bridges and in off-center star-forming clumps, as a consequence of interaction-induced gas redistribution and compression.

In conclusion, the superior spatial resolution and sensitivity of the JWST/NIRCam observations (B. Gregg et al. 2024; A. Pedrini et al. 2024; A. Knutas et al. 2025; G. Bortolini et al. 2025), combined with our CMD-fitting routine SFERA 2.0, enable one of the most detailed spatially resolved studies to date of the stellar populations and star formation history of NGC 4485/NGC 4490, an interacting, gas-rich, low-mass galaxy pair at a distance of $\sim7.4$ Mpc. The SFHs presented in this work provide robust constraints on the evolutionary history of the system and represent valuable inputs for future dynamical and chemical-evolution models. More broadly, our results highlight the potential role of galaxy interactions in driving bursty star formation in gas-rich, low-mass galaxies, a process that is expected to be particularly important in the early Universe, when such systems and mergers were more prevalent. In this context, our resolved view of a nearby interacting galaxy pair provides a valuable benchmark for interpreting recent JWST observations of the earliest galaxies (A. Adamo et al. [1]; M. Curti 2025; D. P. Stark et al. 2026).

Acknowledgments

This work is based in part on observations made with the NASA/ESA/CSA James Webb Space Telescope, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127. These observations are associated with program # 1783. Support for program # 1783 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127. AA acknowledges support from Vetenskapsrådet 2021-05559. A.A and A.P. acknowledge support from the Swedish National Space Agency (SNSА) through the grant 2021-00108. A.A. and H.F.V. acknowledges support from (SNSА) 2023-00260. KG is supported by the Australian Research Council through the Discovery Early Career Researcher Award (DECRA) Fellowship (project number DE220100766) funded by the Australian Government.

Author Contributions

GB performed the artificial star tests, wrote the SFERA 2.0 routine and performed the analysis presented in the paper. He wrote the bulk of the text and produced all the figures. M. Correnti performed the PSF-fitting photometry with DOLPHOT. AA, M. Correnti, M. Cignoni, MT, and GO helped with the analysis, interpretations of the results and in drafting the paper. The remaining authors provided comments and helped in the writing and proofreading of the draft.

Data Availability

The data supporting the findings of this study are openly available at the following MAST DOI: http://dx.doi.org/10.17909/ybh0-r149. All our data products are available at MAST as a High Level Science Product via https://doi.org/10.17909/6dc1-9h53 and DOI: 10.17909/6dc1-9h53 and on the FEAST webpage https://feast-survey.github.io/.

Facilities: JWST/NIRCam.

Software: DOLPHOT (http://americano.dolphinsim.com/dolphot/; E. A. Dolphin 2000; A. Dolphin 2016), Astropy (Astropy Collaboration et al. [7, 8, 9]), Numpy (C. R. Harris et al. 2020), Pandas (Wes McKinney 2010), SciPy (P. Virtanen et al. 2020), Matplotlib (J. D. Hunter 2007), SAOImageDS9 (developed by the Smithsonian Astrophysical Observatory 2000, W. A. Joye & E. Mandel 2003), TOPCAT (M. B. Taylor 2005), SFERA 2.0 (G. Bortolini et al. 2024b), Pikaia (P. Charbonneau 1995).

Appendix

Exploring sources of systematic errors in the star formation histories

As discussed in § 4.1, the PARSEC-COLIBRI stellar models provide a more realistic treatment of the complex AGB evolutionary phase than the MESA-MIST models. Nevertheless, to assess the systematic uncertainties introduced by adopting a different set of stellar models, we recovered the SFHs of Tiles 5, 9, and 17 using SFERA 2.0 with the same set of best-fitting parameters presented in the main text, but employing MESA-MIST isochrones to construct the synthetic CMDs. The results are shown in Figure 14, where the SFHs derived with PARSEC-COLIBRI are represented by dark-violet solid lines, while those obtained with MESA-MIST are shown as green solid lines.

As expected, the SFHs for the three tiles differ significantly in the age range traced by AGB stars ($100\ \mathrm{Myr} \lesssim\mathrm{age} \lesssim5\ \mathrm{Gyr}$), with MESA-MIST predicting higher star formation rates than those inferred from PARSEC-COLIBRI models. Another notable difference is apparent in Tiles 5 and 9, where the inferred strength of the youngest burst of star formation (age $\lesssim30\ \mathrm{Myr}$) differs between the two sets of models, with PARSEC-COLIBRI systematically recovering higher rates. These epochs are traced by massive and luminous red supergiants and blue-loop helium-burning stars. This difference is not surprising, given that our understanding of massive helium-burning stellar evolution is still subject to significant uncertainties. While not as uncertain as the TP-AGB phase, this evolutionary stage remains a major source of disagreement between stellar evolution codes, due to differing prescriptions for convective mixing and overshooting, rotation, nuclear reaction rates, and mass loss (A. Maeder & G. Meynet 2000; J. J. Eldridge & C. A. Tout 2004; A. Maeder & G. Meynet 2001; C. Gallart et al. 2005; A. Bressan et al. 2012). Despite these differences, the predicted ages of the star formation peaks are consistent between the two stellar models, demonstrating the robustness of the synthetic CMD method.

In Figure 14, we also show the SFERA 2.0 SFHs obtained using PARSEC-COLIBRI (dashed dark-violet line) and MESA-MIST (dashed green line) stellar models, excluding from the fitting procedure the region of the CMD populated by AGB stars. As expected, these SFHs are consistent within the uncertainties with those obtained when AGB stars are included, in the age ranges not constrained by these stars (i.e., younger than $\sim100\ \mathrm{Myr}$ and older than $\sim5\ \mathrm{Gyr}$). In contrast, in the age interval dominated by AGB stars in our CMDs, the PARSEC-COLIBRI solutions without AGB constraints yield star formation rates more similar to those inferred using MESA-MIST models, which remain unchanged within the uncertainties when AGB stars are excluded from the fit. These results confirm the crucial importance of detailed AGB evolutionary models to properly fit the CMD of star-forming galaxies beyond the Local Group, and derive an unbiased SFH.

To explore the impact of the adopted age binning for the simple stellar population synthetic CMDs on the recovered SFHs, we ran SFERA 2.0 on the CMDs of Tiles 5, 9, and 17 using the same best-fitting parameters as presented in the main text, but assuming two alternative age binnings. In particular, we shifted the age bins by $\pm0.1$ dex in $\log(\mathrm{age}/\mathrm{yr})$ relative to those adopted in the main text. The results are shown in Figure 15, where the best-fit SFHs from the main text are shown as dark-blue solid lines, while the SFHs recovered with the perturbed age binnings are

The adopted IMF and unresolved binary fraction may affect the recovered SFHs. However, given the limited depth of our CMDs, which are dominated by evolved stars and do not reach the low-mass main sequence, we expect these effects to be negligible compared with other sources of uncertainty, such as photometric errors at the distance of our target galaxy ($\sim7$ Mpc) and differences among stellar evolution models (see, e.g., M. Cignoni & M. Tosi 2010).

Tiles completeness maps

Figure 16 shows zoom-ins of the NIRCam F200W filter mosaic and completeness maps for the remaining tiles for which we recovered the star formation history that are not shown in Figure 6 in the main text. Each pixel in the completeness maps covers an area of $1.7^{\prime\prime} \times1.4^{\prime\prime}$ ($\sim60 \times50$ pc) and is color-coded according to the F200W magnitude at which the 50% completeness limit is reached, ranging from red (shallower CMDs and lower completeness) to blue (deeper CMDs and higher completeness). The different sub-regions into which each tile is divided are delimited by black and magenta solid lines and are labeled R1, R2, and R3, in order of increasing completeness. Tiles 3 and 7 exhibit relatively uniform completeness across their entire area; therefore, they were not further subdivided into sub-regions. Most of the rest of the tiles required no more than two sub-regions (for instance tiles 2, 6, 8), while more crowded tiles required up to three sub-regions (see for instance tiles 10, 12, or 15).