Ornstein-Uhlenbeck parameter extraction from light curves of Fermi-LAT observed blazars

TL;DR

This study demonstrates that a simple Ornstein-Uhlenbeck stochastic process can accurately model the long-term gamma-ray flux variability of blazars observed by Fermi-LAT, capturing key statistical properties and flare dynamics.

Contribution

The paper introduces a method to extract OU model parameters from gamma-ray light curves and shows this model effectively describes blazar variability.

Findings

OU process reproduces flux variability statistics

Extracted parameters are narrowly distributed around specific values

Simulated flare timescales match observed data

Abstract

Context. Monthly-binned gamma-ray light curves of 236 bright gamma-ray sources, particularly blazars, selected from a sample of 2278 high-galactic latitude objects observed with Fermi-LAT, show flux variability characterized by power spectral densities consisting of a single power-law component, ranging from Brownian to white noise. Aims. The main goal here is to assess the Ornstein-Uhlenbeck (OU) model by studying the range of its three parameters that reproduces these statistical properties. Methods. We develop procedures for extracting values of the three OU model parameters (mean flux, correlation length, and random amplitude) from time series data, and apply them to compare numerical integrations of the OU process with the Fermi-LAT data. Results. The OU process fully describes the statistical properties of the flux variations of the 236 blazars. The distributions of the extracted OU parameters are narrowly peaked about well-defined values (sigma, mu, theta) = (0.2, -8.4, 0.5) with variances (0.004, 0.07, 0.13). The distributions of rise and decay time scales of flares in the numerical simulations, i.e. major flux variations fulfilling pre-defined criteria, are in agreement with the observed ones. The power spectral densities of the synthetic light curves are statistically indistinguishable from those of the measured light curves. Conclusions. Long-term gamma-ray flux variability of blazars on monthly time scales is well described by a stochastic model involving only three parameters. The methods described here are powerful tools to study randomness in light curves and thereby constrain the physical mechanisms responsible for the observed flux variations.