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A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations

A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations
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 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.
0035-8711
Zanisi, Lorenzo
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Huertas-Company, Marc
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Lanusse, François
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Bottrell, Connor
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Pillepich, Annalisa
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Nelson, Dylan
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Rodriguez-Gomez, Vicente
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Shankar, Francesco
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Hernquist, Lars
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Dekel, Avishai
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Margalef-Bentabol, Berta
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Vogelsberger, Mark
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Primack, Joel
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Zanisi, Lorenzo
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Huertas-Company, Marc
4bef253f-2c31-4c69-8da9-027a187dbb7e
Lanusse, François
4af721c2-ffd2-45aa-a389-a9807dd03956
Bottrell, Connor
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Pillepich, Annalisa
dd918a46-47c6-4979-b85d-769854de851d
Nelson, Dylan
57f135e1-afe5-4c98-852c-de89748b68fe
Rodriguez-Gomez, Vicente
52828ab9-0a81-4481-934a-0c6736bfb0dd
Shankar, Francesco
b10c91e4-85cd-4394-a18a-d4f049fd9cdb
Hernquist, Lars
c08c0f39-571f-44ed-9408-c0f674e2bc13
Dekel, Avishai
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Margalef-Bentabol, Berta
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Vogelsberger, Mark
e67a4ae9-3c6f-4b4f-982e-b26ffc478ed1
Primack, Joel
3335f45a-46b4-4ccc-a9d3-256bc1978a9a

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 and Primack, Joel (2020) 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). (doi:10.1093/mnras/staa3864).

Record type: Article

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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A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations - Accepted Manuscript
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Accepted/In Press date: 10 December 2020
Published date: 16 December 2020

Identifiers

Local EPrints ID: 447880
URI: http://eprints.soton.ac.uk/id/eprint/447880
ISSN: 0035-8711
PURE UUID: 3f66e1a5-03d3-4e75-b805-b6480e63f047

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Date deposited: 25 Mar 2021 18:19
Last modified: 25 Nov 2021 18:45

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Contributors

Author: Lorenzo Zanisi
Author: Marc Huertas-Company
Author: François Lanusse
Author: Connor Bottrell
Author: Annalisa Pillepich
Author: Dylan Nelson
Author: Vicente Rodriguez-Gomez
Author: Lars Hernquist
Author: Avishai Dekel
Author: Berta Margalef-Bentabol
Author: Mark Vogelsberger
Author: Joel Primack

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