Speaker
Description
This contribution introduces gammapy_SyLC, an open-source Python package designed for time-domain analysis of gamma-ray light curves, with applications to AGNs. SyLC enables synthetic light curve generation with controlled variability properties using the Timmer&Koenig and Emmanoulopoulos algorithms. It also implements statistical tools for characterizing time series properties through power spectral density (PSD) reconstruction, flux amplitude distribution fitting, and Monte Carlo-based uncertainty estimation. A key feature of the package is the computation of confidence envelopes for periodograms, providing a simple assessment of deviations from stochastic processes. This tool is also the basis of the PSD fitting framework, which incorporates a Poissonian white noise component. For flux amplitude distributions, SyLC implements likelihood-based model selection techniques to compare skewed PDF distributions between each other and with Gaussian scenarios. The package quantifies model preference using likelihood ratio tests, aiding in the distinction between different variability regimes. The tools were tested on a sample of light curves of AGNs published in the Fermi-LAT LCR, including BL Lac, Markarian 421, and PG 1553+113, to illustrate their capabilities. Results highlight the effectiveness of the package in PSD fitting and the challenges of amplitude distribution modeling. A full description of SyLC and its applications is presented in a paper currently in preparation. This contribution will provide an overview of the package's core algorithms, its statistical foundations, and its potential for future time-domain astrophysical studies.
