Field-level inference under pressure: the information content of the thermal Sunyaev-Zel'dovich field
Abstract
The thermal Sunyaev--Zel'dovich (tSZ) field is highly non-Gaussian and contains cosmological information that cannot be fully captured by its angular power spectrum. In this work, we investigate the information content of this field using a convolutional neural network (CNN), and compare to simple and interpretable summary statistics, in a simulation-based inference (SBI) framework. We perform the first field-level analysis of the tSZ field, using a suite of 10,000 simulated Compton-y maps, where we jointly vary the cosmological parameters Ωc and σ8 (the density fraction for cold dark matter and the matter clustering amplitude, respectively) and two parameters describing the amplitude and shape of the electron pressure profile, P0 and β, that aim to capture astrophysical uncertainties. We consider both noiseless simulations and simulations including realistic Simons Observatory (SO) noise after component separation. We use SBI to compare the constraints obtained from the CNN with those from the three Minkowski functionals (MFs), individually and in combination, as well as the angular power spectrum. This is the first application of SBI to the MFs of the tSZ field. We find that the CNN provides tighter constraints than conventional summary statistics in the noiseless case. However, at SO-like noise levels, the combination of MFs and the power spectrum recovers constraints comparable to those obtained with the CNN. Our results highlight the potential of these interpretable summary statistics, as well as field-level inference, for extracting information from the tSZ field, motivating efforts toward their application to real data.
Related
- Constraining Effective Viscosity in the Intracluster Medium via the Thermal Sunyaev-Zeldovich Effect -- Predictions from the SLOW Constrained Coma Cluster Simulations
- Joint measurements of thermal Sunyaev-Zel'dovich and cosmic infrared background cross-correlations with cosmic shear
- Thermal Sunyaev-Zel'dovich Effect in the circumgalactic medium - III: does richness matter?
- Optical Depths from the Thermal Sunyaev-Zel'dovich Effect with ACT DR6 and DESI DR1 Spectroscopic Galaxies and Optically-Selected Clusters
- Cosmic topology. Part Va. Information content of the observable Universe
BibTeX
@misc{villagra2026fieldlevel,
title = {{Field-level inference under pressure: the information content of the thermal Sunyaev-Zel'dovich field}},
author = {Carmen Embil Villagra and Adrian E. Bayer and William R. Coulton and Alina Sabyr and Francisco Villaescusa-Navarro and Blake D. Sherwin and Anthony Challinor and J. Colin Hill and Nicholas Battaglia},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40617},
eprint = {2610.40617},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40617},
}
RIS
TY - UNPB
TI - Field-level inference under pressure: the information content of the thermal Sunyaev-Zel'dovich field
AU - Carmen Embil Villagra
AU - Adrian E. Bayer
AU - William R. Coulton
AU - Alina Sabyr
AU - Francisco Villaescusa-Navarro
AU - Blake D. Sherwin
AU - Anthony Challinor
AU - J. Colin Hill
AU - Nicholas Battaglia
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40617
UR - https://arxiv.org/abs/2610.40617
AB - The thermal Sunyaev--Zel'dovich (tSZ) field is highly non-Gaussian and contains cosmological information that cannot be fully captured by its angular power spectrum. In this work, we investigate the information content of this field using a convolutional neural network (CNN), and compare to simple and interpretable summary statistics, in a simulation-based inference (SBI) framework. We perform the first field-level analysis of the tSZ field, using a suite of 10,000 simulated Compton-y maps, where we jointly vary the cosmological parameters Ω_c and σ_8 (the density fraction for cold dark matter and the matter clustering amplitude, respectively) and two parameters describing the amplitude and shape of the electron pressure profile, P_0 and β, that aim to capture astrophysical uncertainties. We consider both noiseless simulations and simulations including realistic Simons Observatory (SO) noise after component separation. We use SBI to compare the constraints obtained from the CNN with those from the three Minkowski functionals (MFs), individually and in combination, as well as the angular power spectrum. This is the first application of SBI to the MFs of the tSZ field. We find that the CNN provides tighter constraints than conventional summary statistics in the noiseless case. However, at SO-like noise levels, the combination of MFs and the power spectrum recovers constraints comparable to those obtained with the CNN. Our results highlight the potential of these interpretable summary statistics, as well as field-level inference, for extracting information from the tSZ field, motivating efforts toward their application to real data.
ER -