Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
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Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks. / Ramanah, Doogesh Kodi; Wojtak, Radoslaw; Arendse, Nikki.
I: Monthly Notices of the Royal Astronomical Society, Bind 501, Nr. 3, 01.03.2021, s. 4080-4091.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
AU - Ramanah, Doogesh Kodi
AU - Wojtak, Radoslaw
AU - Arendse, Nikki
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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 less than or similar to 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.
AB - 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 less than or similar to 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.
KW - methods: numerical
KW - methods: statistical
KW - galaxies: clusters: general
U2 - 10.1093/mnras/staa3922
DO - 10.1093/mnras/staa3922
M3 - Journal article
VL - 501
SP - 4080
EP - 4091
JO - Royal Astronomical Society. Monthly Notices
JF - Royal Astronomical Society. Monthly Notices
SN - 0035-8711
IS - 3
ER -
ID: 259054824