Hybrid bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space

Author:

Pellejero Ibañez Marcos12ORCID,Angulo Raul E13ORCID,Jamieson Drew4ORCID,Li Yin567ORCID

Affiliation:

1. Donostia International Physics Centre , Paseo Manuel de Lardizabal 4, E-20018 Donostia-San Sebastian , Spain

2. Institute for Astronomy, University of Edinburgh , Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ , UK

3. IKERBASQUE, Basque Foundation for Science , E-48013 Bilbao , Spain

4. Max-Planck-Institut für Astrophysik , Karl-Schwarzschild-Straβe 1, D-85748 Garching , Germany

5. Department of Mathematics and Theory, Peng Cheng Laboratory , Shenzhen, Guangdong 518066 , China

6. Center for Computational Mathematics, Flatiron Institute , New York, NY 10010 , USA

7. Center for Computational Astrophysics, Flatiron Institute , New York, NY 10010 , USA

Abstract

ABSTRACT Recently, hybrid bias expansions have emerged as a powerful approach to modelling the way in which galaxies are distributed in the Universe. Similarly, field-level emulators have recently become possible, thanks to advances in machine learning and N-body simulations. In this paper, we explore whether both techniques can be combined to provide a field-level model for the clustering of galaxies in real and redshift space. Specifically, here we will demonstrate that field-level emulators are able to accurately predict all the operators of a second-order hybrid bias expansion. The precision achieved in real and redshift space is similar to that obtained for the non-linear matter power spectrum. This translates to roughly 1–2 per cent precision for the power spectrum of a BOSS (Baryon Oscillation Spectroscopic Survey) and a Euclid-like galaxy sample up to $k\sim 0.6\ h\, {\rm Mpc}^{-1}$. Remarkably, this combined approach also delivers precise predictions for field-level galaxy statistics. Despite all these promising results, we detect several areas where further improvements are required. Therefore, this work serves as a road map for the developments required for a more complete exploitation of upcoming large-scale structure surveys.

Funder

Spanish Ministry of Science and Innovation

Deutsche Forschungsgemeinschaft

Publisher

Oxford University Press (OUP)

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