Stochastic acceleration in Extreme TeV BL Lacs through MCMC

TL;DR

This paper introduces a two-step acceleration model for Extreme TeV BL Lacs, utilizing MCMC to explore parameter space and derive well-constrained physical parameters from spectral data.

Contribution

The study develops a novel two-steps acceleration model and applies MCMC to systematically explore and constrain its parameters using observational data.

Findings

Model successfully fits spectral energy distributions of Extreme TeV BL Lacs.

Parameter distributions are well constrained and reveal complex correlations.

MCMC proves effective for characterizing multi-parametric blazar models.

Abstract

Extreme TeV BL Lacs are a class of blazars with unique spectral and temporal features, not easily reproducible using standard one-zone models based on single shock acceleration. To account for their peculiar properties we elaborated a two-steps acceleration model where a recollimation shock and the subsequent downstream turbulence energize non-thermal electrons. We apply the model to a sample of Extreme TeV BL Lacs with well characterized spectral energy distributions. Since we are considering several sources, we automatized the exploration of the parameter space. This also allows us to derive the parameter distributions and to study the correlations among them. We numerically solved a system of two coupled non-linear differential equations to obtain the non-thermal particles and turbulence spectra. We calculated the spectral energy distribution via the Synchrotron Self-Compton emission model. The automatization of the parameter space exploration is possible through a Markov Chain Monte Carlo (MCMC) ensemble sampler, in our case emcee. We derived well defined posterior distributions for the parameters, showing that the model is well constrained by available data and supporting the suitability of our method. The cross-correlations among some of the physical parameters are not trivial, therefore we conclude that MCMC sampling is a key instrument to characterize the complexity of our multi-parametric phenomenological model.