PREPRINT
D5EBA5C8-C6F8-47CB-B7C7-D665B2C0AF09

Robust field-level inference with dark matter halos

Helen Shao, Francisco Villaescusa-Navarro, Pablo Villanueva-Domingo, Romain Teyssier, Lehman H. Garrison, Marco Gatti, Derek Inman, Yueying Ni, Ulrich P. Steinwandel, Mihir Kulkarni, Eli Visbal, Greg L. Bryan, Daniel Angles-Alcazar, Tiago Castro, Elena Hernandez-Martinez, Klaus Dolag

Submitted on 14 September 2022

Abstract

We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain 5,000 halos with masses 1010 h1M in a periodic volume of (25 h1Mpc)3; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ωm and σ8 with a mean relative error of 6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ωm and σ8 when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP3M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ωm also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.

Preprint

Comment: 25 pages, 11 figures, summary video: https://youtu.be/qkw92Z6owJU

Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics; Astrophysics - Astrophysics of Galaxies; Astrophysics - Instrumentation and Methods for Astrophysics; Computer Science - Artificial Intelligence; Computer Science - Machine Learning

URL: https://arxiv.org/abs/2209.06843