The Hubble Sequence at $z\sim0$ in the IllustrisTNG simulation with deep learning
| Autor: | Dylan Nelson, Vicente Rodriguez-Gomez, Connor Bottrell, Marc Huertas-Company, Mariangela Bernardi, Annalisa Pillepich, Mark Vogelsberger, Helena Domínguez-Sánchez, Gregory F. Snyder, Shy Genel, Ruediger Pakmor |
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| Přispěvatelé: | Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Rok vydání: | 2019 |
| Předmět: |
Normalization (statistics)
Stellar mass media_common.quotation_subject FOS: Physical sciences Astrophysics Astrophysics::Cosmology and Extragalactic Astrophysics 01 natural sciences Convolutional neural network Hubble sequence symbols.namesake 0103 physical sciences Radiative transfer 010303 astronomy & astrophysics ComputingMilieux_MISCELLANEOUS Astrophysics::Galaxy Astrophysics media_common [PHYS]Physics [physics] Physics Artificial neural network 010308 nuclear & particles physics Astronomy and Astrophysics Astrophysics - Astrophysics of Galaxies Galaxy Space and Planetary Science Sky Astrophysics of Galaxies (astro-ph.GA) symbols [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] |
| Zdroj: | Monthly Notices of the Royal Astronomical Society Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2019, 489 (2), pp.1859-1879. ⟨10.1093/mnras/stz2191⟩ arXiv |
| ISSN: | 0035-8711 1365-2966 |
| DOI: | 10.48550/arxiv.1903.07625 |
| Popis: | We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $z\sim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete sample of $\sim12,000$ galaxies in the simulation using the radiative transfer code SKIRT and include PSF and noise to match the SDSS r-band properties. The images are then processed through the exact same neural network used to estimate SDSS morphologies to classify simulated galaxies in four morphological classes (E, S0/a, Sab, Scd). The CNN model finds that $\sim95\%$ of the simulated galaxies fall in one the four main classes with high confidence. The mass-size relations of the simulated galaxies divided by morphological type also reproduce well the slope and the normalization of observed relations which confirms the realism of optical morphologies in the TNG suite. However, the Stellar Mass Functions decomposed into different morphologies still show significant discrepancies with observations both at the low and high mass end. We find that the high mass end of the SMF is dominated in TNG by massive disk galaxies while early-type galaxies dominate in the observations according to the CNN classifications. The present work highlights the importance of detailed comparisons between observations and simulations in comparable conditions. Comment: submitted to MNRAS, comments welcome |
| Databáze: | OpenAIRE |
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