Cluster optical depth and pairwise velocity estimation using machine learning

2025/05/19 by Gong, Yulin, Bean, Rachel
#Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences

paper · doi:10.48550/arxiv.2505.12720

Abstract

We apply two machine learning methods, a CNN deep-leaning model and a gradient-boosting decision tree, to estimate individual cluster optical depths from observed properties derived from multiple complementary datasets. The models are trained and tested with simulated N-body derived halo catalogs and synthetic full-sky CMB maps designed to mirror data from the DESI and Simons Observatory experiments. Specifically, the thermal Sunyaev-Zel'dovich (tSZ) and CMB lensing convergence, along with cluster virial mass estimates are used as features to train the machine learning models. The predicted optical depths are combined with kinematic Sunyaev-Zel'dovich (kSZ) measurements to estimate individual cluster radial peculiar velocities. The method is shown to recover an unbiased estimate of the pairwise velocity statistics of the simulated cluster sample. The model's efficacy is demonstrated for halos with mass range 10¹³ M_\odot < M₂₀₀ < 10¹⁵ M_\odot over a redshift range 0

Citations

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• Tunability: Importance of Hyperparameters of Machine Learning Algorithms
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