Detection of periodic signals in AGN red noise light curves: empirical tests on the Auto-Correlation Function and Phase Dispersion Minimization
S. Krishnan, A.G. Markowitz, A. Schwarzenberg-Czerny, M.J., Middleton

TL;DR
This study empirically tests the effectiveness of Auto-Correlation Function and Phase Dispersion Minimization methods for detecting quasi-periodic oscillations in AGN light curves affected by red noise, highlighting the challenges and limitations.
Contribution
It provides Monte Carlo simulation-based guidance on QPO detection feasibility and false alarm probabilities in AGN light curves using ACF and PDM techniques.
Findings
False positives tend to occur at timescales longer than one-third of the light curve duration.
Detection sensitivity depends on the QPO strength relative to red noise and the PSD slope.
Very high QPO power (10^4-10^5 times red noise) is needed for reliable detection.
Abstract
Active galactic nucleus (AGN) emission is dominated by stochastic, aperiodic variability which overwhelms any periodic/quasi-periodic signal (QPO) if one is present. The Auto Correlation Function (ACF) and Phase Dispersion Minimization (PDM) techniques have been used previously to claim detections of QPOs in AGN light curves. In this paper we perform Monte Carlo simulations to empirically test QPO detection feasibility in the presence of red noise. Given the community's access to large databases of monitoring light curves via large-area monitoring programmes, our goal is to provide guidance to those searching for QPOs via data trawls. We simulate evenly-sampled pure red noise light curves to estimate false alarm probabilities; false positives in both tools tend to occur towards timescales longer than (very roughly) one-third of the light curve duration. We simulate QPOs mixed with pure…
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