Impact of model parameter degeneracy on leptonic radiation models. The case of blazar multi-wavelength spectra

TL;DR

This study investigates how parameter degeneracy affects the interpretation of leptonic radiation models for blazar spectra, emphasizing the importance of data coverage and optimization methods in accurately characterizing source properties.

Contribution

The paper demonstrates the impact of data coverage and fitting procedures on parameter degeneracy in leptonic models, highlighting the need for comprehensive data to constrain model parameters effectively.

Findings

Significant degeneracies exist in seven-dimensional parameter space.

Choice of optimization algorithm influences best-fit results.

Improved data coverage reduces parameter degeneracy.

Abstract

Leptonic one-zone radiation models are commonly used to describe multi-wavelength data and explore the physical properties of high-energy sources, such as active galactic nuclei. However, these models often require a large number of free parameters. In the context of possible parameter degeneracy and the complex landscape of the parameter space, we study how the choice of the fitting procedure impacts the characterization of the source properties. Furthermore, we examine how the data coverage and the uncertainties associated to the data influence the model parameter degeneracy. We generated simulated spectral energy distribution datasets with different properties, which we then fit with a numerical model. The model describes the relevant radiation processes with seven free parameters. We compare different optimization algorithms and study the parameter degeneracy using t-distributed stochastic neighbor embedding. Additionally, we applied the same fitting procedures to the observational data of two sources, Mrk 501 and PKS 0735+178. We demonstrate significant degeneracies in the seven-dimensional parameter space of the one-zone leptonic models caused by the incomplete wavelength coverage of the data. Given the same goodness-of-fit function, the best-fit result depends on the choice of the minimization algorithm. Source properties extracted from the best-fit solution to realistic datasets cannot be interpreted as the only solution due to significant degeneracies of the model parameters. Adding new energy ranges (e.g. MeV) and regular source monitoring would allow to reduce gaps in the data and significantly decrease the parameter degeneracy.