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Abstract The 3D radial escape-velocity profile of galaxy clusters has been suggested to be a promising and competitive tool for constraining mass profiles and cosmological parameters in an accelerating universe. However, the observed line-of-sight escape profile is known to be suppressed compared to the underlying 3D radial (or tangential) escape profile. Past work has suggested that velocity anisotropy in the phase-space data is the root cause. Instead, we find that the observed suppression is from the statistical undersampling of the phase spaces and that the 3D radial escape edge can be accurately inferred from projected data. We build an analytical model for this suppression that only requires the number of observed galaxiesNin the phase-space data within the sky-projected range 0.3 ≤r⊥/R200,critical≤ 1. The radially averaged suppression function is an inverse power law withN0= 17.818 andλ= 0.362. We test our model withN-body simulations, using dark matter particles, subhalos, and semianalytic galaxies as the phase-space tracers, and find excellent agreement. We also assess the model for systematic biases from cosmology (ΩΛ,H0), cluster mass (M200,critical), and velocity anisotropy (β). We find that varying these parameters over large ranges can impart a maximal additional fractional change in 〈Zv〉 of 2.7%. These systematics are highly subdominant (by at least a factor of 13.7) to the suppression fromN.
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This content will become publicly available on March 6, 2026
Cosmological and Astrophysical Parameter Inference from Stacked Galaxy Cluster Profiles Using CAMELS-zoomGZ
Abstract We present a study on the inference of cosmological and astrophysical parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ simulations, we explore how various cluster properties—such as X-ray surface brightness, gas density, temperature, metallicity, and Compton-y profiles—can be used to predict parameters within the 28-dimensional parameter space of the IllustrisTNG model. Through neural networks, we achieve a high correlation coefficient of 0.97 or above for all cosmological parameters, including Ωm,H0, andσ8, and over 0.90 for the remaining astrophysical parameters, showcasing the effectiveness of these profiles for parameter inference. We investigate the impact of different radial cuts, with bins ranging from 0.1R200cto 0.7R200c, to simulate current observational constraints. Additionally, we perform a noise sensitivity analysis, adding up to 40% Gaussian noise (corresponding to signal-to-noise ratios as low as 2.5), revealing that key parameters such as Ωm,H0, and the initial mass function slope remain robust even under extreme noise conditions. We also compare the performance of full radial profiles against integrated quantities, finding that profiles generally lead to more accurate parameter inferences. Our results demonstrate that stacked galaxy cluster profiles contain crucial information on both astrophysical processes within groups and clusters and the underlying cosmology of the Universe. This underscores their significance for interpreting the complex data expected from next-generation surveys and reveals, for the first time, their potential as a powerful tool for parameter inference.
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- Award ID(s):
- 2108678
- PAR ID:
- 10643542
- Publisher / Repository:
- American Astronomical Society
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 981
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 170
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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