A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations
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ABSTRACT Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here, we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and TNG50) simulations against observations from a subsample of the Sloan Digital Sky Survey. Our framework is based on PixelCNN, an autoregressive model for image generation with an explicit likelihood. We adopt a strategy that combines the output of two PixelCNN networks in a metric that isolates the small-scale morphological details of galaxies from the sky background. We are able to quantitatively identify the improvements of IllustrisTNG, particularly in the high-resolution TNG50 run, over the original Illustris. However, we find that the fine details of galaxy structure are still different between observed and simulated galaxies. This difference is mostly driven by small, more spheroidal, and quenched galaxies that are globally less accurate regardless of resolution and which have experienced little improvement between the three simulations explored. We speculate that this disagreement, that is less severe for quenched discy galaxies, may stem from a still too coarse numerical resolution, which struggles to properly capture the inner, dense regions of quenched spheroidal galaxies.
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Zanisi et al. (2021) use the likelihood ratio from Ren et al. (2019) to correct for this effect.
“To correct for this effect, the authors use instead the likelihood ratio presented in Ren et al. (2019) which forces the metric to become sensitive to the fine grained structure of galaxies.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Zanisi et al. (2021)
The likelihood ratio metric is able to measure the improvement in the realism of cosmological simulations from the first Illustris model to the updated TNG one.
“They can then show how the likelihood ratio metric is residual able to measure the improvement in the realism of cosmological simulations from the first Illustris model to the updated TNG one (Figure 38).”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Zanisi et al. (2021)
Zanisi et al. (2021) explores whether anomaly detection with deep learning can compare galaxy images from cosmological simulations to observations.
“Zanisi et al. (2021) also explores whether anomaly detection approaches with deep learning can be used to compare galaxy images from cosmological simulations to observations.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Zanisi et al. (2021)
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Self-citationThe role of mergers and halo spin in shaping galaxy morphology
Rodríguez-Gómez, V., Sales, L. V., Genel, S., Pillepich, A., Zjupa, J., Nelson, D., Griffen, B. F., Torrey, P., Snyder, G. F., Vogelsberger, M., et al. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx305 · Cited by 199 · see citing works
Self-citationMeert, A., Vikram, V., & Bernardi, M. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2333 · Cited by 198 · 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
THEHERSCHELREFERENCE SURVEY: DUST IN EARLY-TYPE GALAXIES AND ACROSS THE HUBBLE SEQUENCE
Smith, M. W. L., Gomez, H. L., Eales, S., Ciesla, L., Boselli, A., Cortese, L., Bendo, G. J., Baes, M., Bianchi, S., Clemens, M., et al. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/748/2/123 · Cited by 195 · see citing works
Zinger, E., Pillepich, A., Nelson, D., Weinberger, R., Pakmor, R., Springel, V., Hernquist, L., Marinacci, F., & Vogelsberger, M. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2607 · Cited by 187 · see citing works
Self-citationThe 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
Self-citationMorphology and star formation in IllustrisTNG: the build-up of spheroids and discs
Tacchella, S., Diemer, B., Hernquist, L., Genel, S., Marinacci, F., Nelson, D., Pillepich, A., Rodríguez-Gómez, V., Sales, L. V., Springel, V., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1657 · Cited by 185 · see citing works
Self-citationCrain, R. A., Theuns, T., Vecchia, C. D., Eke, V. R., Frenk, C. S., Jenkins, A. R., Kay, S. T., Peacock, J. A., Pearce, F. R., Schaye, J., et al. (2009) · Sussex Research Online (University of Sussex) · doi:10.25916/sut.26290456.v1 · Cited by 181 · see citing works
On the estimation of galaxy structural parameters: the Sersic model
Trujillo, I., Graham, A. W., & Caon, N. (2001) · Monthly Notices of the Royal Astronomical Society · doi:10.1046/j.1365-8711.2001.04471.x · Cited by 179 · see citing works
GALACTIC ANGULAR MOMENTUM IN THE ILLUSTRIS SIMULATION: FEEDBACK AND THE HUBBLE SEQUENCE
Genel, S., Fall, S. M., Hernquist, L., Vogelsberger, M., Snyder, G. F., Rodríguez-Gómez, V., Sijacki, D., & Springel, V. (2015) · doi:10.17863/cam.30873 · Cited by 177 · see citing works
Self-citationMAGNETIC FIELDS IN COSMOLOGICAL SIMULATIONS OF DISK GALAXIES
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Donnari, M., Pillepich, A., Joshi, G. D., Nelson, D., Genel, S., Marinacci, F., Rodríguez-Gómez, V., Pakmor, R., Torrey, P., Vogelsberger, M., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3006 · Cited by 162 · see citing works
Self-citationDonnari, M., Pillepich, A., Nelson, D., Vogelsberger, M., Genel, S., Weinberger, R., Marinacci, F., Springel, V., & Hernquist, L. (2018) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz712 · Cited by 154 · see citing works
Self-citationSimulating the dust content of galaxies: successes and failures
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Self-citationDISSIPATION AND EXTRA LIGHT IN GALACTIC NUCLEI. III. “CORE” ELLIPTICALS AND “MISSING” LIGHT
Hopkins, P. F., Lauer, T. R., Cox, T. J., Hernquist, L., & Kormendy, J. (2009) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/181/2/486 · Cited by 152 · see citing works
Self-citationThe role of mergers in driving morphological transformation over cosmic time
Martin, G., Kaviraj, S., Devriendt, J., Dubois, Y., & Pichon, C. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1936 · Cited by 136 · see citing works
INPUT COMPLEXITY AND OUT-OF-DISTRIBUTION DETECTION WITH LIKELIHOOD-BASED GENERATIVE MODELSPreprint
Serrà, J., Álvarez, D., Gómez, V., Slizovskaia, O., Núñez, J. F., & Luque, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11480 · Cited by 131 · see citing works
Terrazas, B. A., Bell, E., Pillepich, A., Nelson, D., Somerville, R., Genel, S., Weinberger, R., Habouzit, M., Li, Y., Hernquist, L., et al. (2019) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/staa374 · Cited by 129 · see citing works
Self-citationLange, R., Moffett, A. J., Driver, S. P., Robotham, A. S. G., Lagos, C. D. P., Kelvin, L. S., Conselice, C. J., Margalef-Bentabol, B., Alpaslan, M., Baldry, I. K., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw1495 · Cited by 127 · 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
Self-citationBernardi, M., Meert, A., Vikram, V., Mei, S., Shankar, F., & Sheth, R. K. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu1106 · Cited by 112 · see citing works
Self-citationZooming 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
On the estimation of galaxy structural parameters: the Sersic ModelPreprint
Trujillo, I., Graham, A. W., & Caon, N. (2001) · Cited by 107 · see citing works
Dissipation and Extra Light in Galactic Nuclei. III. "Core" Ellipticals and "Missing" Light
Kormendy, J. (2016) · Texas ScholarWorks (Texas Digital Library) · doi:10.15781/t2ng4gv3r · Cited by 104 · see citing works
Barbera, F. L., Vazdekis, A., Ferreras, I., Pasquali, A., Cappellari, M., Martín-Navarro, I., Schönebeck, F., & Falcón‐Barroso, J. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv2996 · Cited by 98 · see citing works
Interstellar matter in elliptical galaxies - III. Properties of dust extinction
Goudfrooij, P., de Jong, T., Hansen, L., & Nørgaard-Nielsen, H. U. (1994) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/271.4.833 · Cited by 98 · see citing works
Cold Dust in Early‐Type Galaxies. I. Observations
Temi, P., Brighenti, F., Mathews, W. G., & Bregman, J. D. (2004) · The Astrophysical Journal Supplement Series · doi:10.1086/381963 · Cited by 97 · see citing works
The dust-to-gas and dust-to-metal ratio in galaxies from z = 0 to 6
Li, Q., Narayanan, D., & Dave, R. (2019) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2684 · Cited by 92 · see citing works
Bottrell, C., Torrey, P., Simard, L., & Ellison, S. L. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx276 · Cited by 91 · see citing works
Self-citationNew image statistics for detecting disturbed galaxy morphologies at high redshift
Freeman, P. E., Izbicki, R., Lee, A. B., Newman, J. A., Conselice, C. J., Koekemoer, A. M., Lotz, J. M., & Mozena, M. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt1016 · Cited by 89 · see citing works
The three phases of galaxy formation
Clauwens, B., Schaye, J., Franx, M., & Bower, R. G. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1229 · Cited by 89 · see citing works
Galaxy And Mass Assembly (GAMA): Environmental Quenching of Centrals and Satellites in Groups
Davies, L. J. M., Robotham, A. S. G., Lagos, C. D. P., Driver, S. P., Stevens, A. R. H., Bahé, Y. M., Alpaslan, M., Bremer, M., Brown, M. J. I., Brough, S., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3393 · Cited by 80 · see citing works
Conroy, C., van Dokkum, P. G., & Villaume, A. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa6190 · Cited by 80 · see citing works
Croft, R. A. C., Matteo, T. D., Springel, V., & Hernquist, L. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15446.x · Cited by 78 · see citing works
Self-citationFar-infrared and dust properties of present-day galaxies in the EAGLE simulations
Camps, P., Trayford, J., Baes, M., Theuns, T., Schaller, M., & Schaye, J. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw1735 · Cited by 73 · see citing works
The fate of disc galaxies in IllustrisTNG clusters
Joshi, G. D., Pillepich, A., Nelson, D., Marinacci, F., Springel, V., Rodríguez-Gómez, V., Vogelsberger, M., & Hernquist, L. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1668 · Cited by 73 · see citing works
Self-citationCold Dust in Early-Type Galaxies. I. Observations 1Preprint
Temi, P., Brighenti, F., Mathews, W. G., & Bregman, J. D. (2003) · Cited by 70 · see citing works
Mass assembly and morphological transformations since z ∼ 3 from CANDELS
M. Huertas-Company, Bernardi, M., P'erez-Gonz'alez, P., Ashby, M., Barro, G., Conselice, C., Daddi, E., Dekel, A., Dimauro, P., Faber, S., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw1866 · Cited by 69 · see citing works
Self-citationEales, S., Vis, P. D., Smith, M. W. L., Appah, K., Ciesla, L., Duffield, C., & Schofield, S. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2875 · Cited by 68 · see citing works
Nearly all Massive Quiescent Disk Galaxies Have a Surprisingly Large Atomic Gas Reservoir
Zhang, C., Peng, Y., Ho, L. C., Maiolino, R., Dekel, A., Guo, Q., Mannucci, F., Li, D., Yuan, F., Renzini, A., et al. (2019) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ab4ae4 · Cited by 57 · see citing works
Self-citationDetecting outliers in astronomical images with deep generative networks
Margalef-Bentabol, B., Huertas-Company, M., Charnock, T., Margalef-Bentabol, C., Bernardi, M., Dubois, Y., Storey-Fisher, K., & Zanisi, L. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1647 · Cited by 56 · 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
Galaxy properties as revealed by MaNGA – I. Constraints on IMF and M*/L gradients in ellipticals
Sánchez, H. D., Bernardi, M., Brownstein, J. R., Drory, N., & Sheth, R. K. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2414 · Cited by 48 · see citing works
The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning
M. Huertas-Company, Rodriguez-Gomez, V., Nelson, D., Pillepich, A., Bottrell, C., Bernardi, M., Domínguez-Sánchez, H., Genel, S., Pakmor, R., Snyder, G., et al. (2019) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2191 · Cited by 46 · see citing works
Self-citationBottrell, C., Torrey, P., Simard, L., & Ellison, S. L. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx017 · Cited by 40 · see citing works
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 · see citing works
Self-citationThe new galaxy evolution paradigm revealed by the Herschel surveys
Eales, S., Smith, D. J. B., Bourne, N., Loveday, J., Rowlands, K., van der Werf, P., Driver, S., Dunne, L., Dye, S., Furlanetto, C., et al. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2548 · 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
Further constraining galaxy evolution models through the size function of SDSS early-type galaxies
Shankar, F., Marulli, F., Bernardi, M., Boylan-Kolchin, M., Dai, X., & Khochfar, S. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.16540.x · Cited by 30 · see citing works
Self-citationPROBING ASYMMETRIC STRUCTURES IN THE OUTSKIRTS OF GALAXIES
Wen, Z. Z., Zheng, X. Z., & An, F. X. (2014) · The Astrophysical Journal · doi:10.1088/0004-637x/787/2/130 · Cited by 25 · see citing works
Nonparametric galaxy morphology from UV to submm wavelengths
Baes, M., Nersesian, A., Casasola, V., Bianchi, S., Cassarà, L. P., Clark, C. J. R., Looze, I. D., Dobbels, W., Fritz, J., Galametz, M., et al. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038470 · Cited by 25 · see citing works
Photometric scaling relations for bulges of galaxies
Ravikumar, C. D., Barway, S., Kembhavi, A., Mobasher, B., & Kuriakose, V. C. (2006) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20053547 · Cited by 22 · see citing works
Nonparametric galaxy morphology from UV to submm wavelengths
Baes, M., Nersesian, A., Casasola, V., Bianchi, S., Cassara, L. P., Clark, C. J. R., Looze, I. D., Dobbels, W., Fritz, J., Galametz, M., et al. (2020) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202038470/pdf · Cited by 21 · see citing works
Formation of compact galaxies in the Extreme-Horizon simulation
Chabanier, S., Bournaud, F., Dubois, Y., Codis, S., Chapon, D., Elbaz, D., Pichon, C., Bressand, O., Devriendt, J., Gavazzi, R., et al. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038614 · Cited by 14 · see citing works
Formation of compact galaxies in the Extreme-Horizon simulation
Chabanier, S., Bournaud, F., Dubois, Y., Codis, S., Chapon, D., Elbaz, D., Pichon, C., Bressand, O., Devriendt, J., Gavazzi, R., et al. (2020) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202038614/pdf · Cited by 13 · see citing works
Cold gas and dust: Hunting spiral-like structures in early-type galaxies
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Cold gas and dust: Hunting spiral-like structures in early-type galaxies
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Nuza, S., Dolag, K., & Saro, A. (2010) · MPG.PuRe (Max Planck Society) · Cited by 6 · see citing works
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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
Self-citationPillepich, A., Nelson, D., Truong, N., Weinberger, R., Martin-Navarro, I., Springel, V., Faber, S. M., & Hernquist, L. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2779 · Cited by 87 · see citing works
Self-citationMilky Way and Andromeda analogues from the TNG50 simulation
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Self-citationPractical galaxy morphology tools from deep supervised representation learning
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Self-citationDeep generative models for galaxy image simulations
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Self-citationAre All Post-starbursts Mergers? HST Reveals Hidden Disturbances in the Majority of PSBs
Sazonova, E., Alatalo, K., Rowlands, K., Deustua, S. E., French, D., Heckman, T. M., Lanz, L., Lisenfeld, U., Luo, Y., Medling, A. M., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac0f7f · Cited by 50 · see citing works
Anisotropic satellite galaxy quenching modulated by black hole activity
Martín-Navarro, I., Pillepich, A., Nelson, D., Rodríguez-Gómez, V., Donnari, M., Hernquist, L., & Springel, V. (2021) · Nature · doi:10.1038/s41586-021-03545-9 · Cited by 48 · see citing works
Self-citationThe integrated metallicity profile of the Milky Way
Lian, J., Bergemann, M., Pillepich, A., Zasowski, G., & Lane, R. R. (2023) · Nature Astronomy · doi:10.1038/s41550-023-01977-z · Cited by 47 · see citing works
Self-citationEisert, 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 · see citing works
Self-citationMiller, T. B., van Dokkum, P., & Mowla, L. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acbc74 · Cited by 41 · see citing works
Vega-Ferrero, J., Costantin, L., Pérez‐González, P. G., Sarmiento, R., Kartaltepe, J. S., Pillepich, A., Bagley, M. B., Finkelstein, S. L., McGrath, E. J., Knapen, J. H., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad05bb · Cited by 41 · see citing works
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 · see citing works
Self-citationMass of the dynamically hot inner stellar halo predicts the ancient accreted stellar mass
Ling, Z., Pillepich, A., van de Ven, G., Leaman, R., Hernquist, L., Nelson, D., Pakmor, R., Vogelsberger, M., & Zhang, L. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202142496 · Cited by 38 · see citing works
Self-citationStevens, A. R. H., Lagos, C. D. P., Cortese, L., Catinella, B., Diemer, B., Nelson, D., Pillepich, A., Hernquist, L., Marinacci, F., & Vogelsberger, M. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3662 · Cited by 35 · see citing works
Self-citationThe Evolutionary Pathways of Disk-, Bulge-, and Halo-dominated Galaxies
Du, M., Ho, L. C., Debattista, V. P., Pillepich, A., Nelson, D., Hernquist, L., & Weinberger, R. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac0e98 · Cited by 35 · see citing works
Self-citationBottrell, 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
Self-citationBretonnière, H., Boucaud, A., Lanusse, F., Jullo, E., Merlin, E., Tuccillo, D., Castellano, M., Brinchmann, J., Conselice, C. J., Dole, H. A., et al. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202141393 · Cited by 32 · see citing works
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 · see citing works
Self-citationAGNs 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
The 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 · see citing works
MaNGIA: 10 000 mock galaxies for stellar population analysis
Sarmiento, R., Knapen, J. H., Ibarra-Medel, H. J., Pillepich, A., Sánchez, S. F., & Boecker, A. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245509 · Cited by 25 · see citing works
Self-citationYOLO–CL: Galaxy cluster detection in the SDSS with deep machine learning
Grishin, K., Mei, S., & Ilić, S. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202345976 · Cited by 23 · see citing works
Galaxy merger challenge: A comparison study between machine learning-based detection methods
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Galaxy merger challenge: A comparison study between machine learning-based detection methods
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Self-citation
- Authors
- Zanisi, L., Huertas-Company, M., Lanusse, F., Bottrell, C., Pillepich, A., Nelson, D., Rodriguez-Gomez, V., Shankar, F., Hernquist, L., Dekel, A., Margalef-Bentabol, B., Vogelsberger, M., & Primack, J.
- Affiliations
- University of Southampton (GB)
- Instituto de Astrofísica de Canarias (IAC); Departamento de Astrofísica, Universidad de La Laguna (ULL), E-38200 La Laguna, Spain
- Centre National de la Recherche Scientifique (FR)
- University of Victoria (CA)
- Heidelberg University (DE)
- Instituto de Radioastronomía y Astrofísica, Universidad Nacional Autónoma de México, Apdo. Postal 72-3, 58089 Morelia, Mexico
- Center for Astrophysics Harvard & Smithsonian (US)
- Hebrew University of Jerusalem (IL)
- Massachusetts Institute of Technology (US)
- University of California, Santa Cruz (US)
- Corresponding author
- Zanisi, Lorenzo
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12-16
- Volume
- 501
- Issue
- 3
- Pages
- 4359-4382
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W3039233188
- Cited by
- 65
- References
- 138
- Citations by year
- 2026: 1 · 2025: 12 · 2024: 16 · 2023: 12 · 2022: 12 · 2021: 11 · 2020: 1
- FWCI
- 4.01
- 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)
- ePrints Soton (University of Southampton) (submittedVersion)
- HAL (submittedVersion, other-oa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Science and Technology Facilities Council
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysics and Star Formation Studies
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Keywords
- galaxy morphology, star formation activity, galaxy formation and evolution, fluid simulation, Illustris, IllustrisTNG, PixelCNN, unsupervised deep learning, galaxy scaling relations, disk galaxies, numerical resolution
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