Neural Networks as Optimal Estimators to Marginalize Over Baryonic Effects - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Astrophys.J. Année : 2022

Neural Networks as Optimal Estimators to Marginalize Over Baryonic Effects

Francisco Villaescusa-Navarro
  • Fonction : Auteur
Daniel Angles-Alcazar
  • Fonction : Auteur
Shy Genel
  • Fonction : Auteur
Jose Manuel Zorrilla Mantilla
  • Fonction : Auteur
Shirley Ho
  • Fonction : Auteur
David N. Spergel
  • Fonction : Auteur

Résumé

Many different studies have shown that a wealth of cosmological information resides on small, nonlinear scales. Unfortunately, there are two challenges to overcome to utilize that information. First, we do not know the optimal estimator that will allow us to retrieve the maximum information. Second, baryonic effects impact that regime significantly and in a poorly understood manner. Ideally, we would like to use an estimator that extracts the maximum cosmological information while marginalizing over baryonic effects. In this work we show that neural networks can achieve that when considering some simple scenarios. We made use of data where the maximum amount of cosmological information is known: power spectra and 2D Gaussian density fields. We also contaminate the data with simplified baryonic effects and train neural networks to predict the value of the cosmological parameters. For this data, we show that neural networks can (1) extract the maximum available cosmological information, (2) marginalize over baryonic effects, and (3) extract cosmological information that is buried in the regime dominated by baryonic physics. We also show that neural networks learn the priors of the data they are trained on, affecting their extrapolation properties. We conclude that a promising strategy to maximize the scientific return of cosmological experiments is to train neural networks on state-of-the-art numerical simulations with different strengths and implementations of baryonic effects.
Fichier principal
Vignette du fichier
Villaescusa-Navarro_2022_ApJ_928_44.pdf (2.08 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03047629 , version 1 (25-06-2022)

Identifiants

Citer

Francisco Villaescusa-Navarro, Benjamin D. Wandelt, Daniel Angles-Alcazar, Shy Genel, Jose Manuel Zorrilla Mantilla, et al.. Neural Networks as Optimal Estimators to Marginalize Over Baryonic Effects. Astrophys.J., 2022, 928 (1), pp.44. ⟨10.3847/1538-4357/ac54a5⟩. ⟨hal-03047629⟩
77 Consultations
17 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More