Search for periodicities in High Energy AGNs with a time-domain approach

TL;DR

This paper introduces a novel time-domain methodology for detecting periodicities in high-energy gamma-ray light curves of AGNs, accounting for stochastic noise and comparing results with existing tools.

Contribution

It presents a new approach that models periodic signals alongside noise components using MCMC, improving period detection in stochastic AGN light curves.

Findings

Identified periodicities in several AGN light curves.

Compared new method with Agatha, showing consistent results.

Analyzed variability of periods and amplitudes over time.

Abstract

This paper investigates a new methodology to search for periods in light-curves of high-energy gamma-ray sources such as Active Galactic Nuclei (AGNs). High-energy light curves have significant stochastic components, making period detection somewhat challenging. In our model, periodic terms, drifts of the light-curves and random walk with correlation between flux points due to colored noise are taken into account independently. The parameters of the model are obtained directly from a Markov Chain Monte-Carlo minimization. The time periods found are compared to the output of the publicly available Agatha program. The search method is applied to high-energy periodic AGN candidates from the Fermi-LAT catalogue. The significance of periodic models over pure noise models is discussed. Finally, the variability of the period and amplitude of oscillating terms is studied on the most significant candidates.