Hybrid-bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913234096750592 |
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| author | Ibanez, Marcos Pellejero Angulo, Raul E. Jamieson, Drew Li, Yin |
| author_facet | Ibanez, Marcos Pellejero Angulo, Raul E. Jamieson, Drew Li, Yin |
| contents | 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 $2^{\rm nd}$-order hybrid bias expansion. The precision achieved in real and redshift space is similar to that obtained for the nonlinear matter power spectrum. This translates to roughly 1-2\% precision for the power spectrum of a BOSS and a Euclid-like galaxy sample up to $k\sim 0.6 h^{-1}$Mpc. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_09134 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hybrid-bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space Ibanez, Marcos Pellejero Angulo, Raul E. Jamieson, Drew Li, Yin Cosmology and Nongalactic Astrophysics 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 $2^{\rm nd}$-order hybrid bias expansion. The precision achieved in real and redshift space is similar to that obtained for the nonlinear matter power spectrum. This translates to roughly 1-2\% precision for the power spectrum of a BOSS and a Euclid-like galaxy sample up to $k\sim 0.6 h^{-1}$Mpc. 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. |
| title | Hybrid-bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2307.09134 |