The DOI matches its Crossref record; 161 of the 165 references it cites were found in a registry or an index.
Deep learning predictions of galaxy merger stage and the importance of observational realism
Open accessAbstract
Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps ($87.1{{\ \rm per\ cent}}$ compared to $79.6{{\ \rm per\ cent}}$ accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data ($86.0{{\ \rm per\ cent}}$ with r-only compared to $87.1{{\ \rm per\ cent}}$ with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.
Overview
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
Verification
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Their model reached high (87%) accuracy even when using simulated images inserted into real SDSS survey fields to train and test the model.”
From Detecting galaxy tidal features using self-supervised representation learning (2024) · cites this paper as Bottrell et al. (2019)
The cited source says what the claim attributes to it.
In the citing paper
“However, both the works of Bottrell et al. (2019) and Snyder et al. (2019) do not focus explicitly on the detection of tidal features and therefore are not directly comparable to the results of our analysis.”
From Detecting galaxy tidal features using self-supervised representation learning (2024) · cites this paper as Bottrell et al. (2019)
The cited source says what the claim attributes to it.
In the citing paper
“The main conclusion is that it is more important that images used for training reproduce the observational properties of the sample to be analysed, that is, PSF, noise, background sources, than using full radiative transfer to improve the conversion from stellar particles to light.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Bottrell et al. (2019)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
(2016) · Cited by 11,679
SExtractor: Software for source extraction
Bertin, É., & Arnouts, S. (1996) · Astronomy & Astrophysics Supplement Series · doi:10.1051/aas:1996164 · Cited by 8,949
On the variation of the initial mass function
Kroupa, P. (2001) · Monthly Notices of the Royal Astronomical Society · doi:10.1046/j.1365-8711.2001.04022.x · Cited by 7,499
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY
Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., Allam, S. S., Prieto, C. A., An, D., Anderson, K. S. J., Anderson, S. F., Annis, J., Bahcall, N. A., et al. (2009) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/182/2/543 · Cited by 5,341
Multi-messenger Observations of a Binary Neutron Star Merger *
Abbott, B. P., Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R. X., Adya, V. B., et al. (2017) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/aa91c9 · Cited by 4,269
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 5
- Cardie, 1997, ICML, 57
- Chan, 1998, KDD, 164
- Krizhevsky, 2012, Advances in Neural Information Processng Systems 25, 1097
- Lupton, 2001, ASP Conf. Ser. Vol. 238, Astronomical Data Analysis Software and Systems X, 269
- Lupton, 2002, Proc. SPIE Conf. Ser. Vol. 4836, Survey and Other Telescope Technologies and Discoveries, 350
Cited by
Sorted by citations
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
“The importance of the training set was carefully analyzed in the work by Bottrell et al. (2019).”
BackgroundFerreira, 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
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
Self-citationMcAlpine, S., Harrison, C. M., Rosario, D. J., Alexander, D. M., Ellison, S. L., Johansson, P. H., & Patton, D. R. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1123 · Cited by 75
Self-citationGalaxy mergers can rapidly shut down star formation
Ellison, S. L., Wilkinson, S., Woo, J., Leung, H.-H., Wild, V., Bickley, R. W., Patton, D. R., Quai, S., & Gwyn, S. (2022) · Monthly Notices of the Royal Astronomical Society Letters · doi:10.1093/mnrasl/slac109 · Cited by 68
Self-citationZanisi, L., Huertas-Company, M., Lanusse, F., Bottrell, C., Pillepich, A., Nelson, D., Rodriguez-Gomez, V., Shankar, F., Hernquist, L., Dekel, A., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3864 · Cited by 65
Self-citationCheng, T.-Y., Conselice, C. J., Aragón‐Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J. T., Bluck, A. F. L., Brooks, D., Burke, D. L., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2142 · Cited by 64
Bottrell, C., Yesuf, H. M., Popping, G., Omori, K. C., Tang, S., Ding, X., Pillepich, A., Nelson, D., Eisert, L., Gao, H., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2971 · Cited by 57
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 59
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
Self-citationCharacterization 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
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 55
Eisert, L., Pillepich, A., Nelson, D., Klessen, R. S., & Rodríguez-Gómez, V. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3295 · Cited by 41
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
Self-citationGuzmán-Ortega, A., Rodríguez-Gómez, V., Snyder, G. F., Chamberlain, K., & Hernquist, L. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3334 · Cited by 40
Self-citationBickley, 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
Self-citationGalaxy 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
Self-citationGalaxy sizes and the galaxy–halo connection – I. The remarkable tightness of the size distributions
Zanisi, L., Shankar, F., Lapi, A., Menci, N., Bernardi, M., Duckworth, C., Grylls, P. J., & Salucci, P. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3516 · Cited by 37
A Significant Excess in Major Merger Rate for AGNs with the Highest Eddington Ratios at z < 0.2
Marian, V., Jahnkę, K., Andika, I., Bañados, E., Bennert, V. N., Cohen, S. H., Husemann, B., Kaasinen, M., Koekemoer, A. M., Mechtley, M., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abbd3e · Cited by 35
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 37
Self-citationNorth 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
Omori, 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
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 34
Identifying 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
Identification of tidal features in deep optical galaxy images with convolutional neural networks
Sánchez, H. D., Martin, G., Damjanov, I., Buitrago, F., Huertas-Company, M., Bottrell, C., Bernardi, M., Knapen, J. H., Vega-Ferrero, J., Hausen, R., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad750 · Cited by 28
Self-citationFerreira, 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
Self-citationA Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
Steinhardt, C., Weaver, J., Maxfield, J., Davidzon, I., Faisst, A., Masters, D., Schemel, M., & Toft, S. (2020) · Astrophysical Journal · doi:10.3847/1538-4357/ab76be · Cited by 20
Mass Estimation of Galaxy Clusters with Deep Learning. I. Sunyaev–Zel’dovich Effect
Gupta, N., & Reichardt, C. (2020) · Astrophysical Journal · doi:10.3847/1538-4357/aba694 · Cited by 17
Accurate Identification of Galaxy Mergers with Stellar Kinematics
Nevin, R., Blecha, L., Comerford, J., Greene, J. E., Law, D. R., Stark, D. V., Westfall, K. B., Vazquez-Mata, J. A., Smethurst, R., Argudo-Fernández, M., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abe2a9 · Cited by 26
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
Self-citationStellar populations with optical spectra: deep learning versus popular spectrum fitting codes
Woo, J., Walters, D., Archinuk, F., Faber, S. M., Ellison, S. L., Teimoorinia, H., & Iyer, K. G. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1114 · Cited by 26
Self-citationThe connection between mergers and AGN activity in simulated and observed massive galaxies
Sharma, R. S., Choi, E., Somerville, R. S., Snyder, G. F., Jhee, H., Kocevski, D. D., Hirschmann, M., Moster, B. P., Naab, T., Narayanan, D., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3836 · Cited by 25
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
Villforth, C. (2023) · The Open Journal of Astrophysics · doi:10.21105/astro.2309.03276 · Cited by 25
A reassessment of strong line metallicity conversions in the machine learning era
Teimoorinia, H., Jalilkhany, M., Scudder, J. M., Jensen, J., & Ellison, S. L. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab466 · Cited by 24
Self-citationThorp, 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
Self-citationTowards 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 23
The observability of galaxy merger signatures in nearby gas-rich spirals
McElroy, R., Bottrell, C., Hani, M. H., Moreno, J., Croom, S. M., Hayward, C. C., Twum, A., Feldmann, R., Hopkins, P. F., Hernquist, L., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1715 · Cited by 21
Self-citationThe galaxy morphology–density relation in the EAGLE simulation
Pfeffer, J., Cavanagh, M. K., Bekki, K., Couch, W. J., Drinkwater, M. J., Forbes, D. A., & Koribalski, B. S. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3466 · Cited by 26
Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Teimoorinia, H., Archinuk, F., Woo, J., Shishehchi, S., & Bluck, A. F. L. (2022) · The Astronomical Journal · doi:10.3847/1538-3881/ac4039 · Cited by 20
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 31
Self-citationMarca, A. L., Margalef-Bentabol, B., Wang, L., Gao, F., Goulding, A. D., Martin, G., Rodríguez-Gómez, V., Trager, S. C., Yang, G., Davé, R., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202348188 · Cited by 26
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
Ferreira, L., Conselice, C. J., Kuchner, U., & Tohill, C.-B. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac66ea · Cited by 18
Realistic synthetic integral field spectroscopy with RealSim-IFS
Bottrell, C., & Hani, M. H. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1532 · Cited by 18
Self-citationMolecular Gas and Star Formation in Nearby Starburst Galaxy Mergers
He, H., Bottrell, C., Wilson, C. D., Moreno, J., Burkhart, B., Hayward, C. C., Hernquist, L., & Twum, A. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acca76 · Cited by 17
Self-citationGinzburg, O., M. Huertas-Company, Dekel, A., Mandelker, N., Snyder, G., Ceverino, D., & Primack, J. (2020) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3778 · Cited by 8
Wilkinson, 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
Self-citationERGO-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
Self-citationTeimoorinia, H., Toyonaga, R. D., Fabbro, S., & Bottrell, C. (2020) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab747b · Cited by 16
Self-citationDetecting 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
Bickley, R. W., Ellison, S. L., Salvato, M., Salim, S., Patton, D. R., Merloni, A., Byrne-Mamahit, S., Ferreira, L., & Wilkinson, S. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1951 · Cited by 14
Self-citationZhang, X. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2531 · Cited by 13
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
Bhattacharya, A., Nehal, C. P., Das, M., Paswan, A., Saha, S., & Combes, F. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2117 · Cited by 11
A sub-pc BBH system in SDSS J1609+1756 through optical QPOs in ZTF light curves
Zhang, X. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2848 · Cited by 11
Gaussian Process Classification for Galaxy Blend Identification in LSST
Buchanan, J., Schneider, M. D., Armstrong, R., Muyskens, A. L., Priest, B. W., & Dana, R. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac35ca · Cited by 11
Cheng, T.-Y., Sánchez, H. D., Vega-Ferrero, J., Conselice, C. J., Siudek, M., Aragón‐Salamanca, A., Bernardi, M., Cooke, R., Ferreira, L., Krywult, J., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3228 · Cited by 11
Cosmology with galaxy cluster properties using machine learning
Qiu, L., Napolitano, N. R., Borgani, S., Zhong, F., Li, X.-D., Radovich, M., Lin, W., Dolag, K., Tortora, C., Wang, Y. O., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202346683 · Cited by 11
Sarkar, S., Narayanan, G., Banerjee, A., & Prakash, P. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3096 · Cited by 11
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
Mock galaxy surveys for HST and JWST from the IllustrisTNG simulations
Snyder, G. F., Peña, T., Yung, L. Y. A., Rose, C., Kartaltepe, J. S., & Ferguson, H. C. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3397 · Cited by 10
Harnessing the Hubble Space Telescope Archives: A Catalog of 21,926 Interacting Galaxies
O’Ryan, D., Merín, B., Simmons, B. D., Vojtekova, A., Anku, A., Walmsley, M., Garland, I. L., Géron, T., Keel, W. C., Kruk, S., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acc0ff · Cited by 9
Teimoorinia, H., Shishehchi, S., Tazwar, A., Lin, P., Archinuk, F., Gwyn, S., & Kavelaars, J. J. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/abea7e · Cited by 9
Self-citationHow many stars form in galaxy mergers?
Reeves, A. M. M., & Hudson, M. J. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3211 · Cited by 8
Omori, K. C., Bottrell, C., Bellstedt, S., Robotham, A. S. G., Yesuf, H. M., Goulding, A. D., Sawicki, M., Nagao, T., & Takeuchi, T. T. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ade989 · Cited by 8
Self-citationSDSS-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
Self-citationMerger 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
Zhang, X. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2240 · Cited by 8
The 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
Self-citationUkraine, 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
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
HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology
Hahn, C., Bottrell, C., & Lee, K. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad4344 · Cited by 7
Self-citationDetermining the time before or after a galaxy merger event
Pearson, W., Rodríguez-Gómez, V., Kruk, S., & Margalef-Bentabol, B. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202449532 · Cited by 7
Halo growth and merger rates as a cosmological test
Amoura, Y., Drakos, N. E., Berrouet, A., & Taylor, J. E. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3416 · Cited by 7
Miao, J., Tu, L., Liu, H., & Zhao, J. N. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/adca34 · Cited by 6
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
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
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
Total and dark mass from observations of galaxy centers with machine learning
Wu, S., Napolitano, N. R., Tortora, C., von Marttens, R., Casarini, L., Li, R., & Lin, W. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202348152 · Cited by 5
IllustrisTNG in the HSC-SSP: No Shortage of Thin Disk Galaxies in TNG50
Xu, D., Gao, H., Bottrell, C., Yesuf, H. M., & Shi, J. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad684f · Cited by 4
Self-citationGalaxyGenius: 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
Vega-Ferrero, J., Ceballos, M., Cobo, B., Carrera, F., García, P., & Puyol-Gruart, J. (2022) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ac5159 · Cited by 2
Liao, G., Chen, X., Zheng, Q., Liu, Y., & Zhang, X. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202553747 · Cited by 4
Margalef-Bentabol, B., Wang, L., Pandolfo, N., Marca, A. L., Rodríguez-Gómez, V., Fu, Y., & Chen, M. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202554157 · Cited by 9
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 5
The major merger–active galactic nucleus connection up to cosmic noon
Marca, A. L., Margalef-Bentabol, B., Wang, L., Trager, S. C., Rodríguez-Gómez, V., & Martin, G. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202555185 · Cited by 3
Harvesting the Ly α forest with convolutional neural networks
Cheng, T.-Y., Cooke, R., & Rudie, G. C. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2631 · Cited by 5
Evidence to disfavour dual-core systems leading to double-peaked narrow emission lines
Zhang, X., & Zheng, Q. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad411 · Cited by 3
Galaxy 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
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
“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]).”
BackgroundQuantitatively rating galaxy simulations against real observations with anomaly detection
Jin, Z., Macciò, A. V., Faucher, N., Pasquato, M., Buck, T., Dixon, K. L., Arora, N., Blank, M., & Vulanovic, P. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae552 · Cited by 2
Zhou, L., Radev, S. T., Oliver, W. H., Obreja, A., Jin, Z., & Buck, T. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202453399 · Cited by 2
Harborne, K. E., Lagos, C. D. P., Croom, S. M., van de Sande, J., Ludlow, A. D., Remus, R., Kimmig, L. C., & Power, C. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2526 · Cited by 2
Parameter measurement based on photometric images
Cai, J., Li, N., Yang, H., Wang, L., Zheng, A.-Y., Hao, J., Zhao, X., & Xun, Y. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202452813 · Cited by 2
Nagele, C., Silverman, J. D., Hartwig, T., Li, J., Bottrell, C., Ding, X., & Toba, Y. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acbd4a · Cited by 2
Self-citationUsing Neural Networks to Automate the Identification of Brightest Cluster Galaxies in Large Surveys
Janulewicz, P., Webb, T. M. A., & Perreault-Levasseur, L. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adb1ad · Cited by 1
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
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
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
Self-citation
Metadata
- Authors
- Bottrell, C., Hani, M. H., Teimoorinia, H., Ellison, S. L., Moreno, J., Torrey, P., Hayward, C. C., Thorp, M., Simard, L., & Hernquist, L.
- Affiliations
- University of Victoria (CA)
- California Institute of Technology (US)
- University of Florida (US)
- Flatiron Institute (US)
- National Research Council Canada (CA)
- Center for Astrophysics Harvard & Smithsonian (US)
- Corresponding author
- Bottrell, Connor
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-18
- Volume
- 490
- Issue
- 4
- Pages
- 5390-5413
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2980735226
- Cited by
- 113
- References
- 165
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 22 · 2023: 21 · 2022: 26 · 2021: 9 · 2020: 12 · 2019: 2
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- NPARC (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Sciences and Engineering Research Council of Canada
- NSF (1516374)
- 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 › Astrophysical Phenomena and Observations
- Keywords
- galaxy mergers, synthetic galaxy images, radiative transfer, convolutional neural networks, point spread function, fluid simulation, galaxy morphology
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
