| |  Online-Ressource |
|---|
| Verfasst von: | Eisert, Lukas [VerfasserIn]  |
|---|
| | Bottrell, Connor [VerfasserIn] |
|---|
| | Pillepich, Annalisa [VerfasserIn] |
|---|
| | Shimakawa, Rhythm [VerfasserIn] |
|---|
| | Rodriguez-Gomez, Vicente [VerfasserIn] |
|---|
| | Nelson, Dylan [VerfasserIn] |
|---|
| | Angeloudi, Eirini [VerfasserIn] |
|---|
| | Huertas-Company, Marc [VerfasserIn] |
|---|
| Titel: | ERGO-ML |
|---|
| Titelzusatz: | comparing IllustrisTNG and HSC galaxy images via contrastive learning |
|---|
| Verf.angabe: | Lukas Eisert, Connor Bottrell, Annalisa Pillepich, Rhythm Shimakawa, Vicente Rodriguez-Gomez, Dylan Nelson, Eirini Angeloudi and Marc Huertas-Company |
|---|
| E-Jahr: | 2024 |
|---|
| Jahr: | March 2024 |
|---|
| Umfang: | 29 S. |
|---|
| Illustrationen: | Illustrationen |
|---|
| Fussnoten: | Veröffentlicht: 20 February 2024 ; Gesehen am 01.07.2024 |
|---|
| Titel Quelle: | Enthalten in: Royal Astronomical SocietyMonthly notices of the Royal Astronomical Society |
|---|
| Ort Quelle: | Oxford : Oxford Univ. Press, 1827 |
|---|
| Jahr Quelle: | 2024 |
|---|
| Band/Heft Quelle: | 528(2024), 4 vom: März, Seite 7411-7439 |
|---|
| ISSN Quelle: | 1365-2966 |
|---|
| Abstract: | Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the Extracting Reality from Galaxy Observables with Machine Learning series, we utilize contrastive learning to directly compare a large sample of simulated and observed galaxies based on their stellar-light images. This eliminates the need to specify summary statistics and allows to exploit the whole information content of the observations. We produce survey-realistic galaxy mock data sets resembling real Hyper Suprime-Cam (HSC) observations using the cosmological simulations TNG50 and TNG100. Our focus is on galaxies with stellar masses between 109 and 1012 M⊙ at z = 0.1-0.4. This allows us to evaluate the realism of the simulated TNG galaxies in comparison to actual HSC observations. We apply the self-supervised contrastive learning method Nearest Neighbour Contrastive Learning to the images from both simulated and observed data sets (g-, r-, i-bands). This results in a 256-dimensional representation space, encoding all relevant observable galaxy properties. First, this allows us to identify simulated galaxies that closely resemble real ones by seeking similar images in this multidimensional space. Even more powerful, we quantify the alignment between the representations of these two image sets, finding that the majority (≳ 70 per cent) of the TNG galaxies align well with observed HSC images. However, a subset of simulated galaxies with larger sizes, steeper Sérsic profiles, smaller Sérsic ellipticities, and larger asymmetries appears unrealistic. We also demonstrate the utility of our derived image representations by inferring properties of real HSC galaxies using simulated TNG galaxies as the ground truth. |
|---|
| DOI: | doi:10.1093/mnras/stae481 |
|---|
| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1093/mnras/stae481 |
|---|
| | DOI: https://doi.org/10.1093/mnras/stae481 |
|---|
| Datenträger: | Online-Ressource |
|---|
| Sprache: | eng |
|---|
| K10plus-PPN: | 1892751577 |
|---|
| Verknüpfungen: | → Zeitschrift |
|---|
