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Status: Bibliographieeintrag
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Verfasst von:Eisert, Lukas [VerfasserIn]   i
 Pillepich, Annalisa [VerfasserIn]   i
 Nelson, Dylan [VerfasserIn]   i
 Klessen, Ralf S. [VerfasserIn]   i
 Huertas-Company, Marc [VerfasserIn]   i
 Rodriguez-Gomez, Vicente [VerfasserIn]   i
Titel:ERGO-ML I
Titelzusatz:inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks
Verf.angabe:Lukas Eisert, Annalisa Pillepich, Dylan Nelson, Ralf S. Klessen, Marc Huertas-Company and Vicente Rodriguez-Gomez
E-Jahr:2022
Jahr:14 Feb 2022
Umfang:25 S.
Fussnoten:Gesehen am 12.10.2022
Titel Quelle:Enthalten in: De.arxiv.org
Ort Quelle:[S.l.] : Arxiv.org, 1991
Jahr Quelle:2022
Band/Heft Quelle:(2022), Artikel-ID 2202.06967, Seite 1-25
Abstract:A fundamental prediction of the LambdaCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history, this merger history remains in principle unobservable. By using the TNG100 simulation of the IllustrisTNG project, we show that it is possible to infer the unobservable stellar assembly and merger history of central galaxies from their observable properties by using machine learning techniques. In particular, in this first paper of ERGO-ML (Extracting Reality from Galaxy Observables with Machine Learning), we choose a set of 7 observable integral properties of galaxies (i.e. total stellar mass, redshift, color, stellar size, morphology, metallicity, and age) to infer, from those, the stellar ex-situ fraction, the average merger lookback times and mass ratios, and the lookback time and stellar mass of the last major merger. To do so, we use and compare a Multilayer Perceptron Neural Network and a conditional Invertible Neural Network (cINN): thanks to the latter we are also able to infer the posterior distribution for these parameters and hence estimate the uncertainties in the predictions. We find that the stellar ex-situ fraction and the time of the last major merger are well determined by the selected set of observables, that the mass-weighted merger mass ratio is unconstrained, and that, beyond stellar mass, stellar morphology and stellar age are the most informative properties. Finally, we show that the cINN recovers the remaining unexplained scatter and secondary cross-correlations. Our tools can be applied to large galaxy surveys in order to infer unobservable properties of galaxies' past, enabling empirical studies of galaxy evolution enriched by cosmological simulations.
DOI:doi:10.48550/arXiv.2202.06967
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 ; Verlag: https://doi.org/10.48550/arXiv.2202.06967
 Volltext: http://arxiv.org/abs/2202.06967
 DOI: https://doi.org/10.48550/arXiv.2202.06967
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Astrophysics - Astrophysics of Galaxies
K10plus-PPN:1818788357
Verknüpfungen:→ Sammelwerk

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