The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning
© 2019 The Author(s). We analyse the optical morphologies of galaxies in the IllustrisTNG simulation at z ∼ 0 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 ∼ 12 000 galaxies in the simulat...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Oxford University Press (OUP)
2021
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| Online Access: | https://hdl.handle.net/1721.1/132540 |
| _version_ | 1811091215263727616 |
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| author | Huertas-Company, Marc Rodriguez-Gomez, Vicente Nelson, Dylan Pillepich, Annalisa Bottrell, Connor Bernardi, Mariangela Domínguez-Sánchez, Helena Genel, Shy Pakmor, Ruediger Snyder, Gregory F Vogelsberger, Mark |
| author_facet | Huertas-Company, Marc Rodriguez-Gomez, Vicente Nelson, Dylan Pillepich, Annalisa Bottrell, Connor Bernardi, Mariangela Domínguez-Sánchez, Helena Genel, Shy Pakmor, Ruediger Snyder, Gregory F Vogelsberger, Mark |
| author_sort | Huertas-Company, Marc |
| collection | MIT |
| description | © 2019 The Author(s). We analyse the optical morphologies of galaxies in the IllustrisTNG simulation at z ∼ 0 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 ∼ 12 000 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 classifies simulated galaxies in one of the four main classes with the same uncertainty as for observed galaxies. 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 a reasonable diversity of optical morphologies in the TNG suite. However we find a weak correlation between optical morphology and Sersic index in the TNG suite as opposed to SDSS which might require further investigation. The stellar mass functions (SMFs) decomposed into different morphologies still show some discrepancies with observations especially at the high-mass end. We find an overabundance of late-type galaxies (∼ 50 per cent versus ∼ 20 per cent) at the high-mass end [log(M∗/Mθ) > 11] of the SMF as compared to observations according to the CNN classifications and a lack of S0 galaxies (∼ 20 per cent versus ∼ 40 per cent) at intermediate masses. This work highlights the importance of detailed comparisons between observations and simulations in comparable conditions. |
| first_indexed | 2024-09-23T14:58:47Z |
| format | Article |
| id | mit-1721.1/132540 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:58:47Z |
| publishDate | 2021 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1325402021-09-21T03:15:48Z The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning Huertas-Company, Marc Rodriguez-Gomez, Vicente Nelson, Dylan Pillepich, Annalisa Bottrell, Connor Bernardi, Mariangela Domínguez-Sánchez, Helena Genel, Shy Pakmor, Ruediger Snyder, Gregory F Vogelsberger, Mark © 2019 The Author(s). We analyse the optical morphologies of galaxies in the IllustrisTNG simulation at z ∼ 0 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 ∼ 12 000 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 classifies simulated galaxies in one of the four main classes with the same uncertainty as for observed galaxies. 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 a reasonable diversity of optical morphologies in the TNG suite. However we find a weak correlation between optical morphology and Sersic index in the TNG suite as opposed to SDSS which might require further investigation. The stellar mass functions (SMFs) decomposed into different morphologies still show some discrepancies with observations especially at the high-mass end. We find an overabundance of late-type galaxies (∼ 50 per cent versus ∼ 20 per cent) at the high-mass end [log(M∗/Mθ) > 11] of the SMF as compared to observations according to the CNN classifications and a lack of S0 galaxies (∼ 20 per cent versus ∼ 40 per cent) at intermediate masses. This work highlights the importance of detailed comparisons between observations and simulations in comparable conditions. 2021-09-20T18:22:56Z 2021-09-20T18:22:56Z 2020-11-16T18:20:57Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/132540 en 10.1093/MNRAS/STZ2191 Monthly Notices of the Royal Astronomical Society Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press (OUP) arXiv |
| spellingShingle | Huertas-Company, Marc Rodriguez-Gomez, Vicente Nelson, Dylan Pillepich, Annalisa Bottrell, Connor Bernardi, Mariangela Domínguez-Sánchez, Helena Genel, Shy Pakmor, Ruediger Snyder, Gregory F Vogelsberger, Mark The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning |
| title | The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning |
| title_full | The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning |
| title_fullStr | The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning |
| title_full_unstemmed | The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning |
| title_short | The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning |
| title_sort | hubble sequence at z ∼ 0 in the illustristng simulation with deep learning |
| url | https://hdl.handle.net/1721.1/132540 |
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