A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations
ABSTRACT. Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morpho
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2021
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| In: |
Monthly notices of the Royal Astronomical Society
Year: 2021, Jahrgang: 501, Heft: 3, Pages: 4359-4382 |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/staa3864 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/mnras/staa3864 Verlag, lizenzpflichtig, Volltext: https://academic.oup.com/mnras/article/501/3/4359/6039332 
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| Verfasserangaben: | Lorenzo Zanisi, Marc Huertas-Company, François Lanusse, Connor Bottrell, Annalisa Pillepich, Dylan Nelson, Vicente Rodriguez-Gomez, Francesco Shankar, Lars Hernquist, Avishai Dekel, Berta Margalef-Bentabol, Mark Vogelsberger and Joel Primack |
MARC
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