Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
| Autor: | Doogesh Kodi Ramanah, Radosław Wojtak, Nikki Arendse |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
media_common.quotation_subject statistical [methods] Inference FOS: Physical sciences Astrophysics Astrophysics::Cosmology and Extragalactic Astrophysics Convolutional neural network Cluster (physics) clusters: general [galaxies] Instrumentation and Methods for Astrophysics (astro-ph.IM) Galaxy cluster Astrophysics::Galaxy Astrophysics media_common Physics numerical [methods] Astronomy and Astrophysics Astrophysics - Astrophysics of Galaxies Galaxy Redshift Distribution (mathematics) Space and Planetary Science Sky Astrophysics of Galaxies (astro-ph.GA) Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Cosmology and Nongalactic Astrophysics |
| Zdroj: | Ramanah, D K, Wojtak, R & Arendse, N 2021, ' Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks ', Monthly Notices of the Royal Astronomical Society, vol. 501, no. 3, pp. 4080-4091 . https://doi.org/10.1093/mnras/staa3922 |
| DOI: | 10.1093/mnras/staa3922 |
| Popis: | We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities. By formulating the mass estimation problem within this simulation-based inference framework, we are able to quantify the uncertainties on the inferred masses in a straightforward and robust way. We generate a realistic mock catalogue emulating the Sloan Digital Sky Survey (SDSS) Legacy spectroscopic observations (the main galaxy sample) for redshifts $z \lesssim 0.09$ and explicitly illustrate the challenges posed by interloper (non-member) galaxies for cluster mass estimation from actual observations. Our approach constitutes the first optimal machine learning-based exploitation of the information content of the full 3D projected phase-space distribution, including both the virialized and infall cluster regions, for the inference of dynamical cluster masses. We also present, for the first time, the application of a simulation-based inference machinery to obtain dynamical masses of around $800$ galaxy clusters found in the SDSS Legacy Survey, and show that the resulting mass estimates are consistent with mass measurements from the literature. 14 pages, 11 figures. Accepted for publication in MNRAS. Contains non-peer reviewed supplementary material on cluster mass function in appendix |
| Databáze: | OpenAIRE |
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