WP
W. J. Pearson
astro-ph.GAastro-ph.IMastro-ph.CO
On Valency
published · living versionsW_735trsfs·v1 · currentpublished
Effect of galaxy mergers on dust masses
with M. Romano, K. Małek, D. Donevski, K. Lisiecki +5
1 version
Preprints & journals
37 papers in the corpus · 2017–2026Effect of galaxy mergers on dust masses2610.26166v1 · W. J. Pearson, M. Romano, K. Ma\lek et al.2026 · 0 citationsarXivon Valency
Performance of morphological classifiers for galaxy mergers compared to current machine learning methods2607.09209v2 · Aidan P. Cotter, William J. Pearson, Subhrata Dey et al.2026 · 0 citationsarXiv
From DES to KiDS: Domain adaptation for cross-survey detection of low-surface-brightness galaxies2605.13842v1 · Hareesh Thuruthipilly, Krzysztof Lisiecki, Junais et al.2026 · 0 citationsarXiv
Observationally derived change in the star formation rate as mergers progress2510.20697v2 · W. J. Pearson, L. Wang, V. Rodriguez-Gomez et al.2025 · 4 citationsA&A 704, A234 (2025)
Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey2509.22311v2 · James Pearson, Hugh Dickinson, Stephen Serjeant et al.2025 · 1 citationMNRAS, 546:3 (2026)
Impact of stochastic star-formation histories and dust on selecting quiescent galaxies with JWST photometry2509.10117v2 · K. Lisiecki, D. Donevski, A. W. S. Man et al.2025 · 0 citationsarXiv
Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures2505.10976v1 · Francesco Pistis, Michele Fumagalli, Matteo Fossati et al.2025 · 3 citationsA&A 698, A292 (2025)
Galaxy mergers classification using CNNs trained on Sérsic models, residuals and raw images2502.16603v3 · D.M. Chudy, W.J. Pearson, A. Pollo et al.2025 · 0 citationsarXiv
Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations2502.06254v1 · Rosa de Graaff, Berta Margalef-Bentabol, Lingyu Wang et al.2025 · 3 citationsA&A 697, A207 (2025)
Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning2501.06340v1 · G. Aguilar-Arguello, G. Fuentes-Pineda, H. M. Hern'andez-Toledo et al.2025 · 8 citationsarXiv
Probabilistic and progressive deblended far-infrared and sub-millimetre point source catalogues I. Methodology and first application in the COSMOS field2405.18290v1 · Lingyu Wang, Antonio La Marca, Fangyou Gao et al.2024 · 9 citationsA&A 688, A20 (2024)
Determining the time before or after a galaxy merger event2404.11166v1 · W. J. Pearson, V. Rodriguez-Gomez, S. Kruk et al.2024 · 7 citationsA&A 687, A45 (2024)
Galaxy merger challenge: A comparison study between machine learning-based detection methods2403.15118v2 · B. Margalef-Bentabol, L. Wang, A. La Marca et al.2024 · 20 citationsA&A 687, A24 (2024)
Effects of galaxy environment on merger fraction2403.11615v1 · W. J. Pearson, D. J. D. Santos, T. Goto et al.2024 · 18 citationsA&A 686, A94 (2024)
Do galaxy mergers prefer under-dense environments?2402.18520v2 · U. Sureshkumar, A. Durkalec, A. Pollo et al.2024 · 17 citationsA&A 686, A40 (2024)
Attenuation proxy hidden in surface brightness-colour diagrams. A new strategy for the LSST era2401.12831v2 · K. Ma\lek, Junais, A. Pollo et al.2024 · 0 citationsarXiv
A comparative study of the fundamental metallicity relation: the impact of methodology on its observed evolution2312.00930v1 · F. Pistis, A. Pollo, M. Figueira et al.2023 · 0 citationsarXiv
Influence of star-forming galaxy selection on the galaxy main sequence2308.16605v1 · W. J. Pearson, F. Pistis, M. Figueira et al.2023 · 9 citationsA&A 679, A35 (2023)
Variation of optical and infrared properties of galaxies with their surface brightness2306.06454v1 · Junais, K. Ma\lek, S. Boissier et al.2023 · 10 citationsA&A 676, A41 (2023)
Pitfalls of AI classification of rare objects: Galaxy Mergers2302.11288v1 · W. J. Pearson, L. E. Suelves, S. C. -C. Ho et al.2023 · 1 citationarXiv
Metallicity-PAH Relation of MIR-selected Star-forming Galaxies in AKARI North Ecliptic Pole-wide Survey2212.04635v1 · Hyunjin Shim, Ho Seong Hwang, Woong-Seob Jeong et al.2022 · 6 citationsarXiv
Merger identification through photometric bands, colours, and their errors2211.07489v2 · L. E. Suelves, W. J. Pearson, A. Pollo2022 · 8 citationsA&A 669, A141 (2023)
North Ecliptic Pole merging galaxy catalogue2202.10780v1 · W. J. Pearson, L. E. Suelves, S. C.-C. Ho et al.2022 · 34 citationsarXiv
Optically-detected galaxy cluster candidates in the North Ecliptic Pole field based on photometric redshift from Subaru Hyper Suprime-Cam2107.10010v1 · T.-C. Huang (SOKENDAI, JAXA), H. Matsuhara (SOKENDAI et al.2021 · 7 citationsarXiv
Active galactic nuclei catalog from the AKARI NEP Wide field2104.13428v1 · Artem Poliszczuk, Agnieszka Pollo, Katarzyna Ma\lek et al.2021 · 6 citationsA&A 651, A108 (2021)
Hidden depths in the local Universe: The Stellar Stream Legacy Survey2104.06071v2 · David Martinez-Delgado, Andrew P. Cooper, Javier Roman et al.2021 · 72 citationsA&A 671, A141 (2023)
Galaxy And Mass Assembly: Group and field galaxy morphologies in the star-formation rate - stellar mass plane2101.03804v1 · W. J. Pearson, L. Wang, S. Brough et al.2021 · 12 citationsA&A 646, A151 (2021)
The bright end of the infrared luminosity functions and the abundance of hyperluminous infrared galaxies2011.08798v1 · L. Wang, F. Gao, P. N. Best et al.2020 · 26 citationsarXiv
Towards a consistent framework of comparing galaxy mergers in observations and simulations2009.02974v1 · L. Wang, W. J. Pearson, V. Rodriguez-Gomez2020 · 29 citationsA&A 644, A87 (2020)
Knocking on giants' doors: I. The evolution of the dust-to-stellar mass ratio in distant dusty galaxies2008.09995v1 · D. Donevski, A. Lapi, K. Ma\lek et al.2020 · 65 citationsA&A 644, A144 (2020)
Mergers Do Trigger AGNs out to z 0.62004.00680v1 · F. Gao, L. Wang, W. J. Pearson et al.2020 · 100 citationsA&A 637, A94 (2020)
Effect of galaxy mergers on star formation rates1908.10115v1 · W. J. Pearson, L. Wang, M. Alpaslan et al.2019 · 134 citationsA&A 631, A51 (2019)
Identifying Galaxy Mergers in Observations and Simulations with Deep Learning1902.10626v3 · W. J. Pearson, L. Wang, J. W. Trayford et al.2019 · 90 citationsA&A 626, A49 (2019)
A multi-wavelength de-blended Herschel view of the statistical properties of dusty star-forming galaxies across cosmic time1902.09172v1 · L. Wang, W. J. Pearson, W. Cowley et al.2019 · 44 citationsA&A 624, A98 (2019)
Deep Learning for Galaxy Mergers in the Galaxy Main Sequence1901.07266v1 · William J. Pearson, Lingyu Wang, James Trayford et al.2019 · 1 citationProc. IAU 15 (2019) 104-108
Main sequence of star forming galaxies beyond the Herschel confusion limit1804.03482v2 · W. J. Pearson, L. Wang, P. D. Hurley et al.2018 · 153 citationsA&A 615, A146 (2018)
De-blending Deep Herschel Surveys: A Multi-wavelength Approach1704.02192v1 · W. J. Pearson, L. Wang, F. F. S. van der Tak et al.2017 · 27 citationsA&A 603, A102 (2017)
Career total: at least 131 works. 37 are in this corpus.
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