A Catalogue of Variable Active Galactic Nuclei Based on Multi-Timescale Variability Analysis from Fermi-LAT Data

TL;DR

This work tackles the lack of short-timescale γ-ray variability characterization in large AGN catalogs. It introduces a multi-timescale analysis using the Fermi-LAT Light Curve Repository to quantify variability with the Normalized Excess Variance, $ \sigma_{\rm NXS}^{2} $, for cadences of $3$-day, $7$-day, and $30$-day across $1429$ AGN, with TS$>4$ and $ \sigma_{\rm NXS}^{2} > 0 $ selections. The results reveal that, while many sources have $ \sigma_{\rm NXS}^{2} $ values similar across timescales, a subset shows $ \sigma_{\rm NXS}^{2} $ increasing with longer monitoring, and FSRQs show stronger variability than BL Lacs, which span all classes. The study expands the short-timescale sample to $407$ sources using a proportionality from $30$-day data, providing a practical proxy for daily variability in CTAO planning, and identifies the most luminous variable targets for upcoming VHE observatories. This catalog enables a unified, timescale-aware variability metric to inform observational strategies and population forecasts for next-generation γ-ray facilities.

Abstract

Active Galactic Nuclei (AGN) sources feature supermassive black holes that launch relativistic plasma jets. They are key $γ$-ray sources providing a unique laboratory for studying extreme particle acceleration and plasma physics. Variability in $γ$-ray emission is an important signature that may constrain the size of the emission region and the physical processes driving flares. However, current large-scale $γ$-ray catalogs, such as the Fermi-LAT 4LAC-DR3, typically characterize variability only on long timescales (yearly or 60-day), lacking necessary constraints on short-term behavior from days to weeks. To address this, we systematically characterize $γ$-ray variability in AGNs across short timescales: 3-day, 7-day (weekly), and 30-day (monthly). We present a preliminary catalogue of variable AGN based on light curves from the Fermi-LAT Light Curve Repository. We show that the variability amplitude ($σ_{\rm NXS}^{2}$) presents similar values across different timescales, potentially increasing for a subsample of sources as the observation timescale increases. This high-cadence analysis reinforces the known dichotomy between flat-spectrum radio quasars (FSRQs) and BL Lacertae objects (BL Lacs), with FSRQs consistently exhibiting stronger variability. By identifying the most luminous and variable sources at each timescale, we highlight key targets for follow-up with next-generation observatories such as the Cherenkov Telescope Array Observatory (CTAO), ASTRI Mini-Array, and the Southern Wide-field Gamma-ray Observatory (SWGO), where strong short-term variability suggests highly compact emission zones and extreme particle acceleration efficiency. This catalogue contributes to the understanding of high-energy outflows in AGN jets and provides a foundation for optimizing observational strategies through a unified variability metric across timescales.

Figures (1)

• Figure 1: Distributions of the Normalized Excess Variance ($\sigma_{\rm NXS}^{2}$, NXS) for sources in the catalogue, illustrating the correlation between variability amplitude across three distinct temporal regimes. The figure is structured as a $3 \times 3$ matrix of NXS correlations: Rows (Synchrotron Peak Class): The three rows highlight the source populations (Top to Bottom): Low Synchrotron Peak (LSP) blazars (red triangles), Intermediate Synchrotron Peak (ISP) blazars (green stars), and High Synchrotron Peak (HSP) blazars (blue squares). Note the larger count of LSP sources, consistent with Fermi-LAT's sensitivity. Columns (Timescale Comparison): The columns (Left to Right) compare NXS estimates derived from the light curve binnings: 7-day vs. 3-day; 30-day vs. 3-day; and 30-day vs. 7-day. For all panels, the X-axis represents the longer cadence and the Y-axis represents the shorter cadence. The faint grey 1:1 line is included in all plots, representing the case where $\sigma_{\rm NXS, \text{shorter}}^{2} = \sigma_{\rm NXS, \text{longer}}^{2}$, nearly all data points present similar values across timescales, with a subset falling within a systematic distribution below the line, suggesting $\sigma_{\rm NXS}^{2}$ increasing with timescale ($\sigma_{\rm NXS, \text{shorter}}^{2} < \sigma_{\rm NXS, \text{longer}}^{2}$).