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The Cosmological analysis of X-ray cluster surveys VII. Bypassing scaling relations with Lagrangian Deep Learning and Simulation-based inference

N Cerardi, M Pierre, F Lanusse, X Corap - arXiv preprint arXiv:2507.01820, 2025 - arxiv.org
Astrophysics paper astro-ph.CO Suggest

… We now present the results of the simulation-based inference with the ResNet compressor and the NPE method. We recall that in the SBI steps, our models are trained and …

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BibTeX

@article{2507.01820v1,
Author        = {Nicolas Cerardi and Marguerite Pierre and François Lanusse and Xavier Corap},
Title         = {The Cosmological analysis of X-ray cluster surveys VII. Bypassing
  scaling relations with Lagrangian Deep Learning and Simulation-based
  inference},
Eprint        = {2507.01820v1},
DOI           = {10.1051/0004-6361/202453553},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Galaxy clusters, the pinnacle of structure formation in our universe, are a
powerful cosmological probe. Several approaches have been proposed to express
cluster number counts, but all these methods rely on empirical explicit scaling
relations that link observed properties to the total cluster mass. These
scaling relations are over-parametrised, inducing some degeneracy with
cosmology. Moreover, they do not provide a direct handle on the numerous
non-gravitational phenomena that affect the physics of the intra-cluster
medium. We present a proof-of-concept to model cluster number counts, that
bypasses the explicit use of scaling relations. We rather implement the effect
of several astrophysical processes to describe the cluster properties. We then
evaluate the performances of this modelling for the cosmological inference. We
developed an accelerated machine learning baryonic field-emulator, built upon
the Lagrangian Deep Learning method and trained on the CAMELS simulations. We
then created a pipeline that simulates cluster counts in terms of XMM
observable quantities. We finally compare the performances of our model, with
that involving scaling relations, for the purpose of cosmological inference
based on simulations. Our model correctly reproduces the cluster population
from the calibration simulations at the fiducial parameter values, and allows
us to constrain feedback mechanisms. The cosmological-inference analyses
indicate that our simulation-based model is less degenerate than the approach
using scaling relations. This novel approach to model observed cluster number
counts from simulations opens interesting perspectives for cluster cosmology.
It has the potential to overcome the limitations of the standard approach,
provided that the resolution and the volume of the simulations will allow a
most realistic implementation of the complex phenomena driving cluster
evolution.},
Year          = {2025},
Month         = {Jul},
Note          = {A&A 701, A110 (2025)},
Url           = {http://arxiv.org/abs/2507.01820v1},
File          = {2507.01820v1.pdf}
}

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