A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations
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 k...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Oxford University Press (OUP)
2022
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| Online Access: | https://hdl.handle.net/1721.1/142553 |
| _version_ | 1811085693323051008 |
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| author | Zanisi, Lorenzo Huertas-Company, Marc Lanusse, François Bottrell, Connor Pillepich, Annalisa Nelson, Dylan Rodriguez-Gomez, Vicente Shankar, Francesco Hernquist, Lars Dekel, Avishai Margalef-Bentabol, Berta Vogelsberger, Mark Primack, Joel |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Zanisi, Lorenzo Huertas-Company, Marc Lanusse, François Bottrell, Connor Pillepich, Annalisa Nelson, Dylan Rodriguez-Gomez, Vicente Shankar, Francesco Hernquist, Lars Dekel, Avishai Margalef-Bentabol, Berta Vogelsberger, Mark Primack, Joel |
| author_sort | Zanisi, Lorenzo |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:14:04Z |
| format | Article |
| id | mit-1721.1/142553 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:14:04Z |
| publishDate | 2022 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1425532023-12-08T18:09:03Z A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations Zanisi, Lorenzo Huertas-Company, Marc Lanusse, François Bottrell, Connor Pillepich, Annalisa Nelson, Dylan Rodriguez-Gomez, Vicente Shankar, Francesco Hernquist, Lars Dekel, Avishai Margalef-Bentabol, Berta Vogelsberger, Mark Primack, Joel Massachusetts Institute of Technology. Department of Physics 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. 2022-05-16T17:37:40Z 2022-05-16T17:37:40Z 2021 2022-05-16T17:27:59Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142553 Zanisi, Lorenzo, Huertas-Company, Marc, Lanusse, François, Bottrell, Connor, Pillepich, Annalisa et al. 2021. "A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations." Monthly Notices of the Royal Astronomical Society, 501 (3). en 10.1093/MNRAS/STAA3864 Monthly Notices of the Royal Astronomical Society Attribution-NonCommercial-ShareAlike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press (OUP) Prof. Vogelsberger via Barbara Williams |
| spellingShingle | Zanisi, Lorenzo Huertas-Company, Marc Lanusse, François Bottrell, Connor Pillepich, Annalisa Nelson, Dylan Rodriguez-Gomez, Vicente Shankar, Francesco Hernquist, Lars Dekel, Avishai Margalef-Bentabol, Berta Vogelsberger, Mark Primack, Joel A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations |
| title | A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations |
| title_full | A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations |
| title_fullStr | A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations |
| title_full_unstemmed | A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations |
| title_short | A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations |
| title_sort | deep learning approach to test the small scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations |
| url | https://hdl.handle.net/1721.1/142553 |
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