Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter

Abstract

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux$-$cluster mass relation ($Y_{\mathrm{SZ}}-M$), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_{\mathrm{SZ}}$ and concentration of ionized gas ($c_{\mathrm{gas}}$): $M\propto Y_{\mathrm{conc}}^{3/5}\equiv Y_{\mathrm{SZ}}^{3/5}(1-A{c}_{\mathrm{gas}})$. $Y_{\mathrm{conc}}$ reduces the scatter in the predicted $M$ by $\sim 20-30$\% for large clusters ($M\gtrsim 10^{14}{h}^{-1}{M}_{\odot }$), as compared to using just $Y_{\mathrm{SZ}}$. We show that the dependence on $c_{\mathrm{gas}}$ is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test $Y_{\mathrm{conc}}$ on clusters from CAMELS simulations and show that $Y_{\mathrm{conc}}$ is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4.

Version appearing in PNAS. Added additional tests but results unchanged compared to previous version. The code and data associated with this paper are available at https://github.com/JayWadekar/ScalingRelations_ML

References in corpus (36)

• Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics Beyond LambdaCDM
• Simulating galaxy formation with black hole driven thermal and kinetic feedback
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• An Observed Fundamental Plane Relation for Supermassive Black Holes
• Mass Calibration and Cosmological Analysis of the SPT-SZ Galaxy Cluster Sample Using Velocity Dispersion ${\sigma }_v$ and X-ray $Y_{\text{X}}$ Measurements
• Effect of local environment and stellar mass on galaxy quenching and morphology at $0.5<z<2.0$
• A Bayesian machine scientist to aid in the solution of challenging scientific problems
• The eROSITA Final Equatorial-Depth Survey (eFEDS): Catalog of galaxy clusters and groups
• The Atacama Cosmology Telescope: Physical Properties and Purity of a Galaxy Cluster Sample Selected via the Sunyaev-Zel'dovich Effect
• A Measurement of Gravitational Lensing of the Cosmic Microwave Background by Galaxy Clusters Using Data from the South Pole Telescope
• The GIGANTES dataset: precision cosmology from voids in the machine learning era
• Cosmological Constraints from Galaxy Clusters and Groups in the eROSITA Final Equatorial Depth Survey
• An improved deprojection and PSF-deconvolution technique for galaxy-cluster X-ray surface-brightness profiles
• Planck Sunyaev-Zel'dovich Cluster Mass Calibration using Hyper Suprime-Cam Weak Lensing
• Fundamental Physics from Future Weak-Lensing Calibrated Sunyaev-Zel'dovich Galaxy Cluster Counts
• The Impact of Halo Properties, Energy Feedback and Projection Effects on the Mass-SZ Flux Relation
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• Cross-correlation of DES Y3 lensing and ACT/$Planck$ thermal Sunyaev Zel'dovich Effect II: Modeling and constraints on halo pressure profiles
• A Machine Learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves
• Galaxy cluster mass estimation with deep learning and hydrodynamical simulations
• AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics
• A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton$-y$ parameter maps
• Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning
• Finding universal relations in subhalo properties with artificial intelligence
• Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing
• NECOLA: Towards a Universal Field-level Cosmological Emulator
• Detecting the radiative decay of the cosmic neutrino background with line-intensity mapping
• The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
• Galaxy Formation in the Santa Cruz semi-analytic model compared with IllustrisTNG -- I. Galaxy scaling relations, dispersions, and residuals at z=0
• What to expect from dynamical modelling of cluster haloes II. Investigating dynamical state indicators with Random Forest
• The Atacama Cosmology Telescope: Weighing distant clusters with the most ancient light
• Interpreting the star formation - extinction relation with MaNGA
• HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
• The Importance of Being Interpretable: Toward An Understandable Machine Learning Encoder for Galaxy Cluster Cosmology
• Fundamental Plane of Sunyaev-Zeldovich clusters
• Halo Concentrations and the Fundamental Plane of Galaxy Clusters

Cited by in corpus (9)

• Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
• The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
• Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
• SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
• Identifying Galaxy Cluster Mergers with Deep Neural Networks using Idealized Compton-y and X-ray maps
• syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
• kSZ Pairwise Velocity Reconstruction with Machine Learning
• Cluster optical depth and pairwise velocity estimation using machine learning
• Spatially resolved stellar-to-total dynamical mass relation: Radial variations, gradients and profiles of galaxy stellar populations