KLLR: A scale-dependent, multivariate model class for regression analysis

Abstract

The underlying physics of astronomical systems governs the relation between their measurable properties. Consequently, quantifying the statistical relationships between system-level observable properties of a population offers insights into the astrophysical drivers of that class of systems. While purely linear models capture behavior over a limited range of system scale, the fact that astrophysics is ultimately scale-dependent implies the need for a more flexible approach to describing population statistics over a wide dynamic range. For such applications, we introduce and implement a class of Kernel-Localized Linear Regression (KLLR) models. KLLR is a natural extension to the commonly-used linear models that allows the parameters of the linear model -- normalization, slope, and covariance matrix -- to be scale-dependent. KLLR performs inference in two steps: (1) it estimates the mean relation between a set of independent variables and a dependent variable and; (2) it estimates the conditional covariance of the dependent variables given a set of independent variables. We demonstrate the model's performance in a simulated setting and showcase an application of the proposed model in analyzing the baryonic content of dark matter halos. As a part of this work, we publicly release a Python implementation of the KLLR method.

The code is publicly available at https://github.com/afarahi/kllr and can be installed through `pip install kllr`

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Cited by in corpus (4)

• Baryonic Imprints on DM Halos: The concentration-mass relation in the CAMELS simulations
• Correlations of Dark Matter, Gas and Stellar Profiles in Dark Matter Halos
• Red Dragon: A Redshift-Evolving Gaussian Mixture Model for Galaxies
• C2-GaMe: Classification of Cluster Galaxy Membership with Machine Learning