Dynamical mass inference of galaxy clusters with neural flows
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ABSTRACT We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, that is, the observed line-of-sight velocities and projected distances of galaxies from the cluster centre. Our method employs normalizing flows, a deep neural network capable of learning arbitrary high-dimensional probability distributions, and inherently accounts, to an adequate extent, for the presence of interloper galaxies which are not bounded to a given cluster, the primary contaminant of dynamical mass measurements. We validate and showcase the performance of our neural flow approach to robustly infer the dynamical mass of clusters from a realistic mock cluster catalogue. A key aspect of our novel algorithm is that it yields the probability density function of the mass of a particular cluster, thereby providing a principled way of quantifying uncertainties, in contrast to conventional machine learning (ML) approaches. The neural network mass predictions, when applied to a contaminated catalogue with interlopers, have a mean overall logarithmic residual scatter of 0.028 dex, with a lognormal scatter of 0.126 dex, which goes down to 0.089 dex for clusters in the intermediate- to high-mass range. This is an improvement by nearly a factor of 4 relative to the classical cluster mass scaling relation with the velocity dispersion, and outperforms recently proposed ML approaches. We also apply our neural flow mass estimator to a compilation of galaxy observations of some well-studied clusters with robust dynamical mass estimates, further substantiating the efficacy of our algorithm.
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The works by Kodi Ramanah et al. (2020, 2021) provide a full probability distribution of the cluster mass instead of a single point estimate, which can be used to account for uncertainties.
“The key addition of their approach is that the network provides therefore a full probability distribution of the cluster mass instead of a single point estimate, which can therefore be used to account for uncertainties.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Kodi Ramanah et al. (2020)
Ramanah et al. (2020) proposed the first method for simulation superresolution using a 3D Convolutional WGAN.
“The first method for simulation superresolution, proposed by Ramanah et al. (2020), relied on a 3D Convolutional WGAN with a generator taking as inputs the density field from a low-resolution simulation run and a highresolution set of initial conditions, and tasked with outputting a high-resolution final density field.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Ramanah et al. (2020)
Ramanah et al. (2020) upsampling ratio represented a computational speedup of about ×11 for a 1 h^-1 Mpc and 512^3 particles simulation.
“This upsampling ratio represented a computational speedup of about × 11 for a 1 h -1 Mpcand512 3 particles simulation.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Ramanah et al. (2020)
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- Authors
- Kodi Ramanah, D., Wojtak, R., Ansari, Z., Gall, C., & Hjorth, J.
- Affiliations
- DARK, Niels Bohr Institute, University of Copenhagen, Jagtvej 128, DK-2200 Copenhagen, Denmark
- University of Copenhagen (DK)
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-09-21
- Volume
- 499
- Issue
- 2
- Pages
- 1985-1997
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W3012085163
- Cited by
- 37
- References
- 91
- Citations by year
- 2025: 5 · 2024: 6 · 2023: 9 · 2022: 13 · 2021: 3 · 2020: 1
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Center for Dark Energy Biosphere Investigations
- Carlsbergfondet
- Villum Fonden
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Keywords
- galaxy clusters, dynamical mass, normalizing flows, interloper galaxies, radial velocity, phase space distribution, uncertainty quantification, velocity dispersion
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