A Machine Learning Approach for Predicting Black Hole Mass in Blazars Using Broadband Emission Model Parameters

Abstract

Blazars are observed to emit non-thermal radiation across the entire electromagnetic spectrum from the radio to the very-high-energy γ-ray region. The broadband radiation measured from a blazar is dominated by emission from a relativistic plasma jet which is assumed to be powered by a spinning supermassive black hole situated in the central region of the host galaxy. The formation of jets, their mode of energy transport, actual power budget, and connection with the central black hole are among the most fundamental open problems in blazar research. However, the observed broadband spectral energy distribution from blazars is generally explained by a simple one-zone leptonic emission model. The model parameters place constraints on the contributions from the magnetic field, radiation field, and kinetic power of particles to the emission region in the jet. This in turn constrains the minimum power transported by the jet from the central engine. In this work, we explore the potential of machine learning frameworks including linear regression, support vector machine, adaptive boosting, bagging, gradient boosting, and random forests for the estimation of the mass of the supermassive black hole at the center of the host galaxy of blazars using the best-fit emission model parameters derived from the broadband spectral energy distribution modeling in the literature. Our study suggests that the support vector machine, adaptive boosting, bagging, and random forest algorithms can predict black hole masses with reasonably good accuracy.