Stopyra, Stephen;
Peiris, Hiranya;
Pontzen, Andrew;
Jasche, Jens;
Lavaux, Guilhem;
(2024)
Towards accurate field-level inference of massive cosmic structures.
Monthly Notices of the Royal Astronomical Society
, 527
(1)
pp. 1244-1256.
10.1093/mnras/stad3170.
Preview |
Text
stad3170.pdf - Published Version Download (1MB) | Preview |
Abstract
We investigate the accuracy requirements for field-level inference of cluster and void masses using data from galaxy surveys. We introduce a two-step framework that takes advantage of the fact that cluster masses are determined by flows on larger scales than the clusters themselves. First, we determine the integration accuracy required to perform field-level inference of cosmic initial conditions on these large scales by fitting to late-time galaxy counts using the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm. A 20-step COLA integrator is able to accurately describe the density field surrounding the most massive clusters in the local super-volume ($\lt 135\, {h^{-1}\mathrm{\, Mpc}}$), but does not by itself lead to converged virial mass estimates. Therefore, we carry out ‘posterior resimulations’, using full N-body dynamics while sampling from the inferred initial conditions, and thereby obtain estimates of masses for nearby massive clusters. We show that these are in broad agreement with existing estimates, and find that mass functions in the local super-volume are compatible with ΛCDM.
Type: | Article |
---|---|
Title: | Towards accurate field-level inference of massive cosmic structures |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1093/mnras/stad3170 |
Publisher version: | https://doi.org/10.1093/mnras/stad3170 |
Language: | English |
Additional information: | © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Methods: data analysis, large-scale structure of Universe, cosmology: theory |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
URI: | https://discovery.ucl.ac.uk/id/eprint/10188755 |
Archive Staff Only
View Item |