Neural physical engines for inferring the halo mass distribution function
Open accessAbstract
An ambitious goal in cosmology is to forward model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ∼Mpc scales and the distribution of dark matter haloes (used as a proxy for galaxies) in the same region. Here, we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model. The method is based on the Bayesian analysis of simple, physically motivated, neural network-like architectures, which we denote as neural physical engines, and neural density estimation. As a result, we are able to sample the initial phases of the dark matter density field while inferring the parameters describing the halo mass distribution function, providing a fully Bayesian interpretation of both the initial dark matter density distribution and the neural bias model. We successfully run an upgraded borg (Bayesian Origin Reconstruction from Galaxies) inference using our new likelihood and neural bias model with halo catalogues derived from full N-body simulations. In preliminary results, we notice there could potentially be orders of magnitude improvement in modelling compared to classical biasing techniques.
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BNNs compute an approximation of the true posterior distributions which sometimes might not be accurate enough.
“However, one need to keep in mind that BNNs compute an approximation of the true posterior distributions which sometimes might not be accurate enough (see review by Charnock et al. 2020).”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Charnock et al. 2020
Section 3.3 (p. 5) describes sampling the model’s posterior with Hamiltonian Monte Carlo, and Section 6 (p. 9) calls the resulting network Bayesian. Neither passage addresses the claim that BNN posterior approximations can be insufficiently accurate; searches for that claim and its paraphrases found no such discussion in our copy. (LLM, conf=0.73)
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- Authors
- Charnock, T., Lavaux, G., Wandelt, B. D., Sarma Boruah, S., Jasche, J., & Hudson, M. J.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Department of Applied Mathematics, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
- Stockholm University (SE)
- University of Waterloo (CA)
- Corresponding author
- Charnock, Tom
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-03-13
- Volume
- 494
- Issue
- 1
- Pages
- 50-61
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2972671918
- Cited by
- 26
- References
- 42
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 3 · 2023: 4 · 2022: 4 · 2020: 6
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR-16-CE23-0002)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
- Keywords
- dark matter halos, dark matter density field, Bayesian inference, neural density estimation, Borg, large-scale structure, N-body simulations, galaxy distribution
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