Identifying galaxy mergers in observations and simulations with deep learning
Open accessAbstract
Context. Mergers are an important aspect of galaxy formation and evolution. With large upcoming surveys, such as Euclid and LSST, accurate techniques that are fast and efficient are needed to identify galaxy mergers for further study. Aims. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. As one of the main difficulties of merger studies is the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. Methods. We developed a convolutional neural network architecture and trained it in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. Results. The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. Conclusions. The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalising prospects for using simulation trained networks for galaxy identification in large surveys.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
(2014) · Cited by 33,419 · see citing works
Stellar population synthesis at the resolution of 2003
Bruzual, G. A., & Charlot, S. (2003) · Monthly Notices of the Royal Astronomical Society · doi:10.1046/j.1365-8711.2003.06897.x · Cited by 10,505 · see citing works
The Sloan Digital Sky Survey: Technical Summary
York, D. G., Adelman-McCarthy, J., Anderson, J. E., Anderson, S. F., Annis, J., Bahcall, N. A., Bakken, J. A., Barkhouser, R. H., Bastian, S., Berman, E., et al. (2000) · The Astronomical Journal · doi:10.1086/301513 · Cited by 10,392 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Galactic Stellar and Substellar Initial Mass Function
Chabrier, G. (2003) · Publications of the Astronomical Society of the Pacific · doi:10.1086/376392 · Cited by 9,071 · see citing works
Komatsu, E., Smith, K. M., Dunkley, J., Bennett, C. L., Gold, B., Hinshaw, G., Jarosik, N., Larson, D., Nolta, M. R., Page, L., et al. (2011) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/192/2/18 · Cited by 8,802 · see citing works
Galactic Stellar and Substellar Initial Mass FunctionPreprint
Chabrier, G. (2003) · Cited by 4,012 · see citing works
The EAGLE project: simulating the evolution and assembly of galaxies and their environments
Schaye, J., Crain, R. A., Bower, R. G., Furlong, M., Schaller, M., Theuns, T., Dalla Vecchia, C., Frenk, C. S., McCarthy, I. G., Helly, J. C., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2058 · Cited by 3,601 · see citing works
Self-citationSanders, D. B., & Mirabel, I. F. (1996) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev.astro.34.1.749 · Cited by 2,776 · see citing works
Astrophysics Source Code LibraryPreprint
Allen, A., DuPrie, K., Berriman, G. B., Hanisch, R. J., Mink, J. D., & Teuben, P. J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1212.1916 · Cited by 2,722 · see citing works
The physical properties of star-forming galaxies in the low-redshift universe
Brinchmann, J., Charlot, S., White, S. D. M., Tremonti, C., Kauffmann, G., Heckman, T., & Brinkmann, J. (2003) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2004.07881.x · Cited by 2,513 · see citing works
Stellar masses and star formation histories for 10 5 galaxies from the Sloan Digital Sky Survey
Kauffmann, G., Heckman, T. M., White, S. D. M., Charlot, S., Tremonti, C., Brinchmann, J., Bruzual, G. A., Peng, E. W., Seibert, M., Bernardi, M., et al. (2003) · Monthly Notices of the Royal Astronomical Society · doi:10.1046/j.1365-8711.2003.06291.x · Cited by 2,421 · see citing works
Vogelsberger, M., Genel, S., Springel, V., Torrey, P., Sijacki, D., Xu, D., Snyder, G., Nelson, D., & Hernquist, L. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu1536 · Cited by 2,251 · see citing works
Modelling feedback from stars and black holes in galaxy mergers
Springel, V., Matteo, T. D., & Hernquist, L. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09238.x · Cited by 2,233 · see citing works
2012 IEEE Conference on Computer Vision and Pattern Recognition
Loy, C., Hospedales, T. M., Xiang, T., & Gong, S. (2012) · Cited by 1,883 · see citing works
Hopkins, P. F., Hernquist, L., Cox, T. J., Matteo, T. D., Robertson, B. E., & Springel, V. (2006) · The Astrophysical Journal Supplement Series · doi:10.1086/499298 · Cited by 1,872 · see citing works
Euclid Definition Study Report
Laureijs, R., Amiaux, J., Arduini, S., Auguères, J., Brinchmann, J., Cole, R., Cropper, M., Dabin, C., Duvet, L., Ealet, A., et al. (2011) · CERN Document Server (European Organization for Nuclear Research) · doi:10.48550/arxiv.1110.3193 · Cited by 1,767 · see citing works
Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., Raddick, M. J., Nichol, R. C., Szalay, A., Andreescu, D., et al. (2008) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2008.13689.x · Cited by 1,488 · see citing works
The EAGLE simulations of galaxy formation: calibration of subgrid physics and model variations
Crain, R. A., Schaye, J., Bower, R. G., Furlong, M., Schaller, M., Theuns, T., Dalla Vecchia, C., Frenk, C. S., McCarthy, I. G., Helly, J. C., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv725 · Cited by 1,451 · see citing works
Self-citationLarson, D., Dunkley, J., Hinshaw, G., Komatsu, E., Nolta, M. R., Bennett, C. L., Gold, B., Halpern, M., Hill, R. S., Jarosik, N., et al. (2011) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/192/2/16 · Cited by 1,358 · see citing works
UV Star Formation Rates in the Local Universe
Salim, S., Rich, R., Charlot, S., Brinchmann, J., Johnson, B., Schiminovich, D., Seibert, M., Mallery, R., Heckman, T., Forster, K., et al. (2007) · The Astrophysical Journal Supplement Series · doi:10.1086/519218 · Cited by 1,356 · see citing works
Physical Models of Galaxy Formation in a Cosmological Framework
Somerville, R., & Dave, R. (2014) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-082812-140951 · Cited by 1,006 · see citing works
WRIGHT, E. L. (2016) · Texas ScholarWorks (Texas Digital Library) · doi:10.15781/t2rn30970 · Cited by 992 · see citing works
Hopkins, P. F., Hernquist, L., Cox, T. J., Matteo, T. D., Robertson, B. E., & Springel, V (2005) · CERN Document Server (European Organization for Nuclear Research) · Cited by 963 · see citing works
LSST Science Book, Version 2.0Preprint
LSST Dark Energy Science Collaboration, Abell, P., Allison, J., Anderson, S. F., Andrew, J., Angel, J. R. P., Armus, L., Arnett, D., Asztalos, S. J., Axelrod, T. S., et al. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0912.0201 · Cited by 905 · see citing works
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Dieleman, S., Willett, K. W., & Dambre, J. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv632 · Cited by 781 · see citing works
A New Nonparametric Approach to Galaxy Morphological Classification
Lotz, J., Primack, J., & Madau, P. (2003) · The Astronomical Journal · doi:10.1086/421849 · Cited by 632 · see citing works
A Direct Measurement of Major Galaxy Mergers atz3
Conselice, C. J., Bershady, M. A., Dickinson, M., & Papovich, C. (2003) · The Astronomical Journal · doi:10.1086/377318 · Cited by 523 · see citing works
The eagle simulations of galaxy formation: Public release of halo and galaxy catalogues
McAlpine, S., Helly, J. C., Schaller, M., Trayford, J. W., Qu, Y., Furlong, M., Bower, R. G., Crain, R. A., Schaye, J., Theuns, T., et al. (2016) · Astronomy and Computing · doi:10.1016/j.ascom.2016.02.004 · Cited by 491 · see citing works
Self-citationThe Accretion Origin of the Milky Way’s Stellar Halo
Bell, E. F., Zucker, D. B., Belokurov, V. A., Sharma, S., Johnston, K. V., Bullock, J. S., Hogg, D. W., Jahnkę, K., de Jong, J. T. A., Beers, T. C., et al. (2008) · The Astrophysical Journal · doi:10.1086/588032 · Cited by 446 · see citing works
Statistical Properties of Collisionless Equal‐ and Unequal‐Mass Merger Remnants of Disk Galaxies
Naab, T., & Burkert, A. (2003) · The Astrophysical Journal · doi:10.1086/378581 · Cited by 437 · see citing works
The EAGLE simulation of galaxy formation: public release of halo and galaxy catalogues
McAlpine, S., Helly, J. C., Schaller, M., Trayford, J. W., Qu, Y., Furlong, M., Bower, R. G., Crain, R. A., Schaye, J., Theuns, T., et al. (2016) · Liverpool John Moores University · Cited by 428 · see citing works
Self-citationPreparing Red‐Green‐Blue Images from CCD Data
Lupton, R. H., Blanton, M. R., Fekete, G., Hogg, D. W., O'Mullane, W., Szalay, A. S., & Wherry, N. (2004) · Publications of the Astronomical Society of the Pacific · doi:10.1086/382245 · Cited by 424 · see citing works
The Asymmetry of Galaxies: Physical Morphology for Nearby and High-Redshift Galaxies
Conselice, C., Bershady, M., & Jangren, A. (1999) · The Astrophysical Journal · doi:10.1086/308300 · Cited by 381 · see citing works
Bershady, M. A., Jangren, A., & Conselice, C. J. (2000) · The Astronomical Journal · doi:10.1086/301386 · Cited by 375 · see citing works
Tidally Triggered Star Formation in Close Pairs of Galaxies
Barton, E. J., Geller, M. J., & Kenyon, S. J. (2000) · The Astrophysical Journal · doi:10.1086/308392 · Cited by 353 · see citing works
The Evolution of Galaxy Structure Over Cosmic Time
Conselice, C. (2014) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-081913-040037 · Cited by 349 · see citing works
Fossil Signatures of Ancient Accretion Events in the Halo
Johnston, K. V., Hernquist, L., & Bolte, M. (1996) · The Astrophysical Journal · doi:10.1086/177418 · Cited by 295 · see citing works
Recent star formation in interacting galaxies - II. Super starbursts in merging galaxies
Joseph, R. D., & Wright, G. S. (1985) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/214.2.87 · Cited by 290 · see citing works
Statistical Properties of Collisionless Equal- and Unequal-Mass Merger Remnants of Disk GalaxiesPreprint
Naab, T., & Burkert, A. M. (2003) · Cited by 289 · see citing works
A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING
Huertas-Company, M., Gravet, R., Cabrera-Vives, G., Pérez-González, P. G., Kartaltepe, J. S., Barro, G., Bernardi, M., Mei, S., Shankar, F., Dimauro, P., et al. (2015) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/221/1/8 · Cited by 285 · see citing works
Lin, L., Koo, D. C., Willmer, C. N. A., Patton, D. R., Conselice, C. J., Yan, R., Coil, A. L., Cooper, M. C., Davis, M., Faber, S. M., et al. (2004) · The Astrophysical Journal · doi:10.1086/427183 · Cited by 263 · see citing works
Galaxy pairs in the 2dF survey - I. Effects of interactions on star formation in the field
Lambas, D. G., Tissera, P. B., Alonso, M. S., & Coldwell, G. (2003) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2003.07179.x · Cited by 261 · see citing works
Colours and luminosities of <i>z</i> = 0.1 galaxies in the eagle simulation
Trayford, J. W., Theuns, T., Bower, R. G., Schaye, J., Furlong, M., Schaller, M., Frenk, C. S., Crain, R. A., Vecchia, C. D., & McCarthy, I. G. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv1461 · Cited by 251 · see citing works
Self-citationDynamically Close Galaxy Pairs and Merger Rate Evolution in the CNOC2 Redshift Survey
Patton, D. R., Pritchet, C. J., Carlberg, R. G., Marzke, R. O., Yee, H. K. C., Hall, P. B., Lin, H., Morris, S. L., Sawicki, M., Shepherd, C. W., et al. (2002) · The Astrophysical Journal · doi:10.1086/324543 · Cited by 242 · see citing works
Galaxy growth in the concordance ΛCDM cosmology
Guo, Q., & White, S. D. M. (2008) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2007.12619.x · Cited by 232 · see citing works
Size evolution of normal and compact galaxies in the EAGLE simulation
Furlong, M., Bower, R. G., Crain, R. A., Schaye, J., Theuns, T., Trayford, J. W., Qu, Y., Schaller, M., Berthet, M., & Helly, J. C. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2740 · Cited by 216 · see citing works
Self-citationGalaxy Zoo: the fraction of merging galaxies in the SDSS and their morphologies
Darg, D. W., Kaviraj, S., Lintott, C. J., Schawinski, K., Sarzi, M., Bamford, S. P., Silk, J. I., Proctor, R., Andreescu, D., Murray, P., et al. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15686.x · Cited by 204 · see citing works
Darg, D., Kaviraj, S., Lintott, C. J., Schawinski, K., Sarzi, M., Bamford, S. P., Silk, J. I., Andreescu, D., Murray, P., Nichol, R. C., et al. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15786.x · Cited by 201 · see citing works
Trayford, J. W., Camps, P., Theuns, T., Baes, M., Bower, R. G., Crain, R. A., Gunawardhana, M. L. P., Schaller, M., Schaye, J., & Frenk, C. S. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx1051 · Cited by 195 · see citing works
Self-citationFinding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks
Petrillo, C. E., Tortora, C., Chatterjee, S., Vernardos, G., Koopmans, L. V. E., Kleijn, G. V., Napolitano, N. R., Covone, G., Schneider, P., Grado, A., et al. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2052 · Cited by 189 · see citing works
Self-citationA chronicle of galaxy mass assembly in the EAGLE simulation
Qu, Y., Helly, J. C., Bower, R. G., Theuns, T., Crain, R. A., Frenk, C. S., Furlong, M., McAlpine, S., Schaller, M., Schaye, J., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2437 · Cited by 186 · see citing works
The star formation main sequence and stellar mass assembly of galaxies in the Illustris simulation
Sparre, M., Hayward, C., Springel, V., Vogelsberger, M., Genel, S., Torrey, P., Nelson, D., Sijacki, D., & Hernquist, L. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2713 · Cited by 185 · see citing works
THE CANADA-FRANCE-HAWAII TELESCOPE LEGACY SURVEY: STACKED IMAGES AND CATALOGS
Gwyn, S. (2012) · The Astronomical Journal · doi:10.1088/0004-6256/143/2/38 · Cited by 165 · see citing works
Synthetic galaxy images and spectra from the Illustris simulation
Torrey, P., Snyder, G. F., Vogelsberger, M., Hayward, C. C., Genel, S., Sijacki, D., Springel, V., Hernquist, L., Nelson, D., Kriek, M., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2592 · Cited by 137 · see citing works
Using transfer learning to detect galaxy mergers
Ackermann, S., Schawinski, K., Zhang, C., Weigel, A. K., & Turp, M. D. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1398 · Cited by 137 · see citing works
Genel, S., Genzel, R., Bouché, N., Naab, T., & Sternberg, A. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/701/2/2002 · Cited by 127 · see citing works
Zooming in on major mergers: dense, starbursting gas in cosmological simulations
Sparre, M., & Springel, V. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw1793 · Cited by 109 · see citing works
Interacting galaxies in the nearby Universe: only moderate increase of star formation
Knapen, J. H., Cisternas, M., & Querejeta, M. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv2135 · Cited by 100 · see citing works
The Millennium Galaxy Catalogue: Dynamically Close Pairs of Galaxies and the Global Merger Rate
Propris, R. D., Liske, J., Driver, S. P., Allen, P. D., & Cross, N. J. G. (2005) · The Astronomical Journal · doi:10.1086/433169 · Cited by 88 · see citing works
BAR FORMATION FROM GALAXY FLYBYS
Lang, M., Holley‐Bockelmann, K., & Sinha, M. (2014) · The Astrophysical Journal Letters · doi:10.1088/2041-8205/790/2/l33 · Cited by 68 · see citing works
Sinha, M., & Holley‐Bockelmann, K. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/751/1/17 · Cited by 66 · see citing works
Walmsley, M., Ferguson, A. M. N., Mann, R. G., & Lintott, C. J. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3232 · Cited by 66 · see citing works
Snyder, G. F., Rodriguez-Gomez, V., Lotz, J. M., Torrey, P., Quirk, A. C. N., Hernquist, L., Vogelsberger, M., & Freeman, P. E. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1059 · Cited by 65 · see citing works
de Grijs, R., & Delgado, R. M. G. (2005) · Springer eBooks · doi:10.1007/1-4020-3539-x · Cited by 65 · see citing works
Trayford, J. W., Frenk, C. S., Theuns, T., Schaye, J., & Correa, C. A. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2860 · Cited by 56 · see citing works
Self-citationCONNECTIONS BETWEEN GALAXY MERGERS AND STARBURST: EVIDENCE FROM THE LOCAL UNIVERSE
Luo, W., Yang, X., & Zhang, Y. (2014) · The Astrophysical Journal Letters · doi:10.1088/2041-8205/789/1/l16 · Cited by 34 · see citing works
The spatially resolved star formation history of mergers
Cortijo-Ferrero, C., Delgado, R. M. G., Pérez, E., Fernandes, R. C., García-Benito, R., Matteo, P. D., Sánchez, S. F., de Amorim, A. L., Lacerda, E. A. D., Fernández, R. L., et al. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201731217 · Cited by 31 · see citing works
CONNECTIONS BETWEEN GALAXY MERGERS AND STARBURST: EVIDENCE FROM THE LOCAL UNIVERSE
Luo, W., Yang, X., & Zhang, Y. (2014) · Cited by 26 · see citing works
STARBURSTS ARE PREFERENTIALLY INTERACTING: CONFIRMATION FROM THE NEAREST GALAXIES
Knapen, J. H., & Cisternas, M. (2015) · The Astrophysical Journal Letters · doi:10.1088/2041-8205/807/1/l16 · Cited by 24 · see citing works
Cortijo-Ferrero, C., Delgado, R. M. G., Pérez, E., et al. (2017) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.48550/arxiv.1707.05324 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 74 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- [10]10. Chollet, F. 2017, IEEE Conference on Computer Vision and Pattern Recognition
- [28]28. Ioffe, S., & Szegedy, C. 2015, Int. Conf. Mach. Learn.
- [32]32. Kingma, D. P., & Ba, J. 2015, 3rd International Conference for Learning Representations
- [40]40. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278
- [49]49. Nair, V., & Hinton, G. E. 2010, Proc. 27th Int. Conf. Mach. Learn., 807
Cited by
Sorted by citations
Effect of galaxy mergers on star-formation rates
Pearson, W. J., Wang, L., Alpaslan, M., Baldry, I., Bilicki, M., Brown, M. J. I., Grootes, M. W., Holwerda, B. W., Kitching, T. D., Kruk, S., et al. (2019) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201936337 · Cited by 134 · see citing works
Self-citationDeep learning predictions of galaxy merger stage and the importance of observational realism
Bottrell, C., Hani, M. H., Teimoorinia, H., Ellison, S. L., Moreno, J., Torrey, P., Hayward, C. C., Thorp, M., Simard, L., & Hernquist, L. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2934 · Cited by 113 · see citing works
The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2022.55 · Cited by 102 · see citing works
“Pearson et al. (2019a) applied CNNs to simulated LSST and Euclid images.”
MethodMergers trigger active galactic nuclei out to z ∼ 0.6
Gao, F., Wang, L., Pearson, W., Gordon, Y., Holwerda, B. W., Hopkins, A., Brown, M. J. I., Bland‐Hawthorn, J., & Owers, M. S. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201937178 · Cited by 101 · see citing works
Self-citationEffect of galaxy mergers on star-formation rates
Pearson, W., Wang, L., Alpaslan, M., Baldry, I. K., Bilicki, M., Brown, M. J. I., Grootes, M. W., Holwerda, B. W., Kitching, T., Kruk, S., et al. (2019) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/201936337/pdf · Cited by 98 · see citing works
Self-citationFerreira, L., Conselice, C. J., Duncan, K., Cheng, T.-Y., Griffiths, A., & Whitney, A. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab8f9b · Cited by 92 · see citing works
Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG
Bickley, R. W., Bottrell, C., Hani, M. H., Ellison, S. L., Teimoorinia, H., Yi, K. M., Wilkinson, S., Gwyn, S., & Hudson, M. J. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab806 · Cited by 84 · see citing works
Bílek, M., Duc, P., Cuillandre, J., Gwyn, S., Cappellari, M., Bekaert, D. V., Bonfini, P., Bitsakis, T., Paudel, S., Krajnović, D., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2248 · Cited by 82 · see citing works
Patton, D. R., Wilson, K. D., Metrow, C. J., Ellison, S. L., Torrey, P., Brown, W., Hani, M. H., McAlpine, S., Moreno, J., & Woo, J. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa913 · Cited by 79 · see citing works
Hidden depths in the local Universe: The Stellar Stream Legacy Survey
Martínez‐Delgado, D., Cooper, A. P., Román, J., Pillepich, A., Erkal, D., Pearson, S., Moustakas, J., Laporte, C. F. P., Laine, S. J., Akhlaghi, M., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245011 · Cited by 72 · see citing works
Self-citationDeepMerge: Classifying high-redshift merging galaxies with deep neural networks
Ćiprijanović, A., Snyder, G. F., Nord, B., & Peek, J. E. G. (2020) · Astronomy and Computing · doi:10.1016/j.ascom.2020.100390 · Cited by 55 · see citing works
The merger fraction of post-starburst galaxies in UNIONS
Wilkinson, S., Ellison, S. L., Bottrell, C., Bickley, R. W., Gwyn, S., Cuillandre, J., & Wild, V. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1962 · Cited by 54 · see citing works
Characterization of low surface brightness structures in annotated deep images
Sola, E., Duc, P., Richards, F., Paiement, A., Urbano, M., Klehammer, J., Bílek, M., Cuillandre, J., Gwyn, S., & McConnachie, A. W. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202142675 · Cited by 53 · see citing works
Galaxy morphology classification using automated machine learning
Reza, M. (2021) · Astronomy and Computing · doi:10.1016/j.ascom.2021.100492 · Cited by 47 · see citing works
Ćiprijanović, A., Kafkes, D., Downey, K., Jenkins, S., Perdue, G., Madireddy, S., Johnston, T., Snyder, G. F., & Nord, B. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1677 · Cited by 45 · see citing works
Bustamante, S., Ellison, S. L., Patton, D. R., & Sparre, M. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1025 · Cited by 41 · see citing works
Bickley, R. W., Ellison, S. L., Patton, D. R., Bottrell, C., Gwyn, S., & Hudson, M. J. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1500 · Cited by 39 · see citing works
Galaxy interactions in IllustrisTNG-100, I: The power and limitations of visual identification
Blumenthal, K. A., Moreno, J., Barnes, J. E., Hernquist, L., Torrey, P., Claytor, Z. R., Rodríguez-Gómez, V., Marinacci, F., & Vogelsberger, M. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3472 · Cited by 38 · see citing works
Bottrell, C., Hani, M. H., Teimoorinia, H., Patton, D. R., & Ellison, S. L. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3717 · Cited by 34 · see citing works
North Ecliptic Pole merging galaxy catalogue
Pearson, W., Suelves, L. E., Ho, S. C.-C., Oi, N., Brough, S., Holwerda, B. W., Hopkins, A., Huang, T.-C., Hwang, H. S., Kelvin, L. S., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202141013 · Cited by 34 · see citing works
Self-citationOmori, K. C., Bottrell, C., Walmsley, M., Yesuf, H. M., Goulding, A. D., Ding, X., Popping, G., Silverman, J. D., Takeuchi, T. T., & Toba, Y. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202346743 · Cited by 33 · see citing works
Bignone, L., Pedrosa, S., Trayford, J. W., Tissera, P. B., & Pellizza, L. J. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3014 · Cited by 31 · see citing works
Self-citationTowards a consistent framework of comparing galaxy mergers in observations and simulations
Wang, L., Pearson, W. J., & Rodriguez-Gomez, V. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038084 · Cited by 29 · see citing works
Self-citationIdentifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests
Rose, C., Kartaltepe, J. S., Snyder, G. F., Rodríguez-Gómez, V., Yung, L. Y. A., Haro, P. A., Bagley, M. B., Calabrò, A., Cleri, N. J., Cooper, M. C., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ac9f10 · Cited by 29 · see citing works
Ferreira, L., Bickley, R. W., Ellison, S. L., Patton, D. R., Byrne-Mamahit, S., Wilkinson, S., Bottrell, C., Fabbro, S., Gwyn, S., & McConnachie, A. W. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1885 · Cited by 28 · see citing works
AGNs in post-mergers from the ultraviolet near infrared optical northern survey
Bickley, R. W., Ellison, S. L., Patton, D. R., & Wilkinson, S. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad088 · Cited by 26 · see citing works
An IFU View of the Active Galactic Nuclei in MaNGA Galaxy Pairs
Jin, G., Dai, Y. S., Pan, H.-A., Lin, L., Li, C., Hsieh, B.-C., Shen, S., Yuan, F.-T., Feng, S., Cheng, C., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac2901 · Cited by 25 · see citing works
Thorp, M., Bluck, A. F. L., Ellison, S. L., Maiolino, R., Conselice, C. J., Hani, M. H., & Bottrell, C. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2201 · Cited by 24 · see citing works
Towards a consistent framework of comparing galaxy mergers in observations and simulations
Wang, L., Pearson, W., & Rodríguez-Gómez, V. (2020) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202038084/pdf · Cited by 22 · see citing works
Self-citationGalaxy merger challenge: A comparison study between machine learning-based detection methods
Margalef-Bentabol, B., Wang, L., Marca, A. L., Blanco-Prieto, C., Chudy, D., Sánchez, H. D., Goulding, A. D., Guzmán-Ortega, A., Martin, G., Pearson, W., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202348239 · Cited by 20 · see citing works
Self-citationCharacterizing tidal features around galaxies in cosmological simulations
Khalid, A., Brough, S., Martin, G., Kimmig, L. C., Lagos, C. D. P., Remus, R., & Martínez-Lombilla, C. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1064 · Cited by 19 · see citing works
Desmons, A., Brough, S., Martínez-Lombilla, C., De Propris, R., Holwerda, B., & López-Sánchez, Á. R. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1639 · Cited by 18 · see citing works
What drives the growth of black holes: A decade of progress
Alexander, D. M., Hickox, R. C., Aird, J., Combes, F., Costa, T., Habouzit, M., Harrison, C. M., Leng, R. I., Morabito, L. K., Uckelman, S. L., et al. (2025) · New Astronomy Reviews · doi:10.1016/j.newar.2025.101733 · Cited by 18 · see citing works
Ferreira, L., Conselice, C. J., Kuchner, U., & Tohill, C.-B. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac66ea · Cited by 18 · see citing works
Do galaxy mergers prefer under-dense environments?
Sureshkumar, U., Durkalec, A., Pollo, A., Pearson, W., Farrow, D. J., Narayanan, A., Loveday, J., Taylor, E. N., & Suelves, L. E. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202347705 · Cited by 17 · see citing works
Self-citationWilkinson, S., Ellison, S. L., Bottrell, C., Bickley, R. W., Byrne-Mamahit, S., Ferreira, L., & Patton, D. R. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae287 · Cited by 16 · see citing works
DECORAS: detection and characterization of radio-astronomical sources using deep learning
Rezaei, S., McKean, J. P., Biehl, M. L., & Javadpour, A. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3519 · Cited by 16 · see citing works
ERGO-ML: comparing IllustrisTNG and HSC galaxy images via contrastive learning
Eisert, L., Bottrell, C., Pillepich, A., Shimakawa, R., Rodríguez-Gómez, V., Nelson, D., & Angeloudi, E. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae481 · Cited by 15 · see citing works
A new automated tool for the spectral classification of OB stars
Kyritsis, E., Maravelias, G., Zezas, A., Bonfini, P., Kovlakas, K., & Reig, P. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202040224 · Cited by 15 · see citing works
Detecting galaxy tidal features using self-supervised representation learning
Desmons, A., Brough, S., & Lanusse, F. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1402 · Cited by 15 · see citing works
Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Luo, Z., Chen, J., Chen, Z., Zhang, S., Fu, L., Xiao, H., & Shu, C. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/addb4c · Cited by 13 · see citing works
Angeloudi, E., Falcón‐Barroso, J., Sarmiento, R., Pillepich, A., Walo-Martín, D., & Eisert, L. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1669 · Cited by 12 · see citing works
Uncovering tidal treasures: automated classification of faint tidal features in DECaLS data
Gordon, A. J., Ferguson, A. M. N., & Mann, R. G. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2169 · Cited by 10 · see citing works
Machine learning the gap between real and simulated nebulae
Belfiore, F., Ginolfi, M., Blanc, G. A., Boquien, M., Chevance, M., Congiu, E., Glover, S. C. O., Groves, B. A., Klessen, R. S., Méndez-Delgado, J. E., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202451934 · Cited by 10 · see citing works
Sola, E., Duc, P., Urbano, M., Richards, F., Paiement, A., Bílek, M., Yıldız, M. K., Boselli, A., Côté, P., Cuillandre, J., et al. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1139 · Cited by 10 · see citing works
Merger or Not: Accounting for Human Biases in Identifying Galactic Merger Signatures
Lambrides, E., Watts, D. J., Chiaberge, M., Tchernyshyov, K., Kirkpatrick, A., Meyer, E. T., Heckman, T. M., Simons, R. C., Amram, O., Hall, K., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac0fdf · Cited by 10 · see citing works
A new automated tool for the spectral classification of OB stars
Kyritsis, E., Maravelias, G., Zezas, A., Bonfini, P., Kovlakas, K., & Reig, P. (2022) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202040224/pdf · Cited by 10 · see citing works
3D detection and characterization of ALMA sources through deep learning
Veneri, M. D., Tychoniec, Ł., Guglielmetti, F., Longo, G., & Villard, E. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3314 · Cited by 9 · see citing works
Euclid Quick Data Release (Q1)
Walmsley, M., Quilley, L., Masters, K. L., Kruk, S., Remmelgas, K. A., Popp, J., Romelli, E., O’Ryan, D., Dickinson, H. J., Lintott, C. J., et al. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202555140 · Cited by 8 · see citing works
SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning
Chang, Y.-Y., Lin, L., Pan, H.-A., Lin, C.-A., Hsieh, B.-C., Bottrell, C., & Wang, P. W. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac8c27 · Cited by 8 · see citing works
Merger identification through photometric bands, colours, and their errors
Suelves, L. E., Pearson, W., & Pollo, A. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202244509 · Cited by 8 · see citing works
Self-citationThe effect of image quality on galaxy merger identification with deep learning
Bickley, R. W., Wilkinson, S., Ferreira, L., Ellison, S. L., Bottrell, C., & Jyoti, D. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2246 · Cited by 8 · see citing works
Ukraine, M. A. O. O. T. N. O., Vavilova, I. B., Khramtsov, V., Dobrycheva, D. V., Ukraine, M. A. O. O. T. N. O., Vasylenko, M. Y., Ukraine, M. A. O. O. T. N. O., Elyiv, A., Ukraine, M. A. O. O. T. N. O., Мelnyk, О. V., et al. (2022) · Kosmìčna nauka ì tehnologìâ · doi:10.15407/knit2022.01.003 · Cited by 8 · see citing works
Khramtsov, V., Vavilova, I. B., Dobrycheva, D. V., Vasylenko, M. Y., Мelnyk, О. V., Elyiv, A., Akhmetov, V. S., & Dmytrenko, A. (2022) · Kosmìčna nauka ì tehnologìâ · doi:10.15407/knit2022.05.027 · Cited by 8 · see citing works
Automating galaxy morphology classification using k-nearest neighbours and non-parametric statistics
Mukundan, K., Nair, P. B., Bailin, J., & Li, W. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1684 · Cited by 7 · see citing works
STRRINGS: STReams in Residual Images of Nearby GalaxieS
Sola, E., Chemaly, D., Belokurov, V. A., Müller, O., Arentsen, A., Davies, E. Y., Laguna-Miralles, J., Myeong, G., Panagiotakis, K., Zhang, H., et al. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1647 · Cited by 7 · see citing works
Astrophysics with the Laser Interferometer Space Antenna
Amaro‐Seoane, P., Andrews, J. J., Sedda, M. A., Askar, A., Baghi, Q., Balasov, R., Bartos, I., Bavera, S. S., Bellovary, J., Berry, C. P. L., et al. (2023) · Living Reviews in Relativity · doi:10.1007/s41114-022-00041-y · Cited by 6 · see citing works
Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Zhou, X., Gong, Y., Zhang, X., Li, N., Meng, X., Chen, X., Wen, R., Han, Y., Zou, H., Zheng, X., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad8bbf · Cited by 6 · see citing works
Miao, J., Tu, L., Liu, H., & Zhao, J. N. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/adca34 · Cited by 6 · see citing works
A Deep Learning Approach for Characterizing Major Galaxy MergersPreprint
Koppula, S., Bapst, V., Marc Huertas-Company, Blackwell, S., Grabska‐Barwińska, A., Dieleman, S., Huber, A., Antropova, N., Bińkowski, M., Openshaw, H. D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.05182 · Cited by 6 · see citing works
Reste, A. L., Jaskot, A. E., Brazie, J., Scarlata, C. M., Flury, S. R., Mantha, K. B., Henry, A., Hayes, M., Östlin, G., Saldana-Lopez, A., et al. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae2ad8 · Cited by 5 · see citing works
Rose, C., Kartaltepe, J. S., Snyder, G. F., Yung, L. Y. A., Haro, P. A., Bagley, M. B., Bisigello, L., Calabrò, A., Cleri, N. J., Dickinson, M., et al. (2024) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ad8dd4 · Cited by 5 · see citing works
Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Jing, Y., Mao, T., Wang, J., Liu, C., & Chen, X. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ada895 · Cited by 5 · see citing works
Contreras-Santos, A., Knebe, A., Cui, W., Haggar, R., Pearce, F. R., Gray, M. E., Petris, M. D., & Yepes, G. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1061 · Cited by 5 · see citing works
A post-merger enhancement only in star-forming Type 2 Seyfert galaxies: the deep learning view
Avirett‐Mackenzie, M., Villforth, C., Wuyts, S., Alexander, D. M., Bonoli, S., Lapi, A., López, I. E., Almeida, C. R., & Shankar, F. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae183 · Cited by 5 · see citing works
Measuring the Intracluster Light Fraction with Machine Learning
Canepa, L., Brough, S., Lanusse, F., Montes, M., & Hatch, N. A. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adabc7 · Cited by 4 · see citing works
GalaxyGenius: Mock galaxy image generator for various telescopes from hydrodynamical simulations
Zhou, X., Yang, H., Li, N., Xiong, Q., Deng, F., Meng, X., Ye, R., Shen, S., Wei, P., Cui, Q., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202554287 · Cited by 4 · see citing works
Domain adaptation techniques for improved cross-domain study of galaxy mergersPreprint
Ćiprijanović, A., Kafkes, D., Jenkins, S., Downey, K., Perdue, G., Madireddy, S., Johnston, T., & Nord, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03591 · Cited by 4 · see citing works
Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations
de Graaff, R., Margalef-Bentabol, B., Wang, L., Marca, A. L., Pearson, W., Rodríguez-Gómez, V., & Walmsley, M. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202452659 · Cited by 3 · see citing works
Self-citationGalaxy merger challenge: A comparison study between machine learning-based detection methods
Margalef-Bentabol, B., Wang, L., Marca, A. L., Blanco-Prieto, C., Chudy, D., Sánchez, H. D., Goulding, A. D., Guzmán-Ortega, A., M. Huertas-Company, Martin, G., et al. (2024) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202348239/pdf · Cited by 3 · see citing works
Self-citationScaling Laws for Galaxy Images
Walmsley, M., Bowles, M., Scaife, A. M. M., Makechemu, J. S., Gordon, A. J., Ferguson, A. M. N., Mann, R. G., Pearson, J., Popp, J. J., Bovy, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.02973 · Cited by 2 · see citing works
“Characterizing the remnants reveals the details of each collision, but remnants are hard to distinguish automatically from typical galaxy structure; finding LSB features has been challenging for both rule-based algorithms and deep learning approaches (e.g. [48, 49, 50]).”
BackgroundAnalysing the prevalence of tidal features in HSC-SSP using self-supervised representation learning
Desmons, A., Brough, S., Lanusse, F., Canepa, L., & Khalid, A. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1617 · Cited by 2 · see citing works
Byrne-Mamahit, S., Ellison, S. L., Patton, D. R., Wilkinson, S., Ferreira, L., & Bottrell, C. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1765 · Cited by 2 · see citing works
Time-scales for the effects of interactions on galaxy properties and SMBH growth
O’Ryan, D., Simmons, B. D., Faisst, A. L., Garland, I. L., Géron, T., Gozaliasl, G., Gillman, S., Pinto, S. G. V., Keel, W. C., Koekemoer, A. M., et al. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf541 · Cited by 1 · see citing works
Recent Research Progress on Galaxy Pairs and Mergers with JWST Observations
Yi-lu, T., & Yu, D. J. (2025) · Chinese Astronomy and Astrophysics · doi:10.1016/j.chinastron.2025.09.008 · Cited by 1 · see citing works
Unions with UNIONS: Using Galaxy–Galaxy Lensing to Probe Galaxy Mergers
Cheng, I., Elvin-Poole, J., Hudson, M. J., Barré, R., Ellison, S. L., Bickley, R. W., de Boer, T. J. L., Fabbro, S., Ferreira, L., Guerrini, S., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ae03b8 · Cited by 1 · see citing works
O’Ryan, D., & Gómez, P. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202555512 · Cited by 1 · see citing works
Galaxy mergers in UNIONS – II. Predicting time-scales in the post-merger regime
Ferreira, L., Ellison, S. L., Patton, D. R., Byrne-Mamahit, S., Wilkinson, S., & Bickley, R. W. (2026) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stag178 · Cited by 1 · see citing works
Identifying Merging Galaxies in MaNGA using H-a and Hi Observations
Lee, D. A. A., Abidin, Z., Jwel, A. K. M., & Hassan, M. S. R. (2022) · Sains Malaysiana · doi:10.17576/jsm-2022-5107-20 · Cited by 1 · see citing works
Identifying Mergers in the Legacy Surveys with Few-shot Learning
Wei, S., Song, X., Zhang, Z., Liang, B., Dai, W., Lu, W., & Tao, J. (2024) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ad66ca · Cited by 1 · see citing works
Narrow-band Hα imaging of nearby Wolf–Rayet galaxies
Paswan, A., Saha, K., & Omar, A. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2833 · Cited by 1 · see citing works
Black Hole Weather Forecasting Using Deep Learning
Pereira, R. D. (2020) · doi:10.11606/d.14.2020.tde-23102020-170517 · Cited by 1 · see citing works
Moriwaki, K. (2022) · Springer theses · doi:10.1007/978-981-19-5880-9_1
Omori, K. C. (2024) · Springer theses · doi:10.1007/978-981-97-8735-7_1
The Effect of Environment in Galaxy Merger Incidence
Omori, K. C. (2024) · Springer theses · doi:10.1007/978-981-97-8735-7_3
Lee, Y., Song, H., Shin, J., Hong, S., Lee, J., & Chun, K. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae371a
Galaxy Mergers Classification Using CNNs Trained on Sérsic Models, Residuals, and Raw Images
Chudy, D. M., Pearson, W., Suelves, L. E., Margalef-Bentabol, B., Pollo, A., Wang, L., Rodríguez-Gómez, V., & Marca, A. L. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae41b8
Self-citationComparison and verification methods to trace interaction-driven disturbances in galaxies
Lyu, H., Brough, S., Khalid, A., Desmons, A., & Sazonova, E. (2026) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stag865
- Authors
- Pearson, W. J., Wang, L., Trayford, J. W., Petrillo, C. E., & van der Tak, F. F. S.
- Affiliations
- University of Groningen (NL)
- Leiden University (NL)
- Venue
- Astronomy & Astrophysics
- Publisher
- EDP Sciences
- Published
- 2019-06
- Volume
- 626
- Pages
- A49
- ISSN
- 0004-6361 (ISSN-L)
- 1432-0746
- OpenAlex
- W2918807486
- Cited by
- 90
- References
- 74
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 17 · 2023: 9 · 2022: 16 · 2021: 10 · 2020: 10 · 2019: 6
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Astronomy and Astrophysics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Springer Link (Chiba Institute of Technology) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- U.S. Department of Energy
- National Aeronautics and Space Administration
- Alfred P. Sloan Foundation
- Princeton University
- Ohio State University
- University of Washington
- Johns Hopkins University
- University of Pittsburgh
- Case Western Reserve University
- Drexel University
- Universität Basel
- University of Portsmouth
- New Mexico State University
- Chinese Academy of Sciences
- Max-Planck-Gesellschaft
- Max-Planck-Institut für Astronomie
- U.S. Naval Observatory
- Fermilab
- Los Alamos National Laboratory
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Keywords
- galaxy mergers, convolutional neural networks, galaxy simulations, EAGLE simulation, SDSS galaxies, galaxy formation and evolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
