gammapy_SyLC: A Package for Simulating and Fitting Variability in High-Energy Light Curves

Abstract

Characterizing the temporal variability of astrophysical sources is key to understanding the underlying physical processes driving their emissions. This work introduces a gammapy_SyLC, a Python package that offers tools to simulate and fit time-domain data, with a focus on Active Galactic Nuclei (AGN) variability. The package was developed taking into account possible interactions with gammapy but does not directly depend on it. gammapy_SyLC incorporates optimized implementations of the Timmer & Koenig and Emmanoulopoulos algorithms for light curve simulation, capable of generating synthetic lightcurves from specified PSDs and amplitude distribution models. It also provides functionalities for PSD fitting, histogram-based PDF interpolation, and Monte Carlo-based parameter estimation, making it a full-stack tool for investigating variable phenomena and specifically the long-term behavior of AGNs. To showcase its capabilities, the package was applied to gamma-ray light curves from the Fermi Large Area Telescope repository, reconstructing PSDs and PDFs and constraining variability models for observed sources.