A Bayesian test for periodic signals in red noise

TL;DR

This paper introduces a Bayesian statistical method using Markov chain Monte Carlo techniques to detect weak periodic signals in astrophysical sources with broad, noisy power spectra, improving the analysis of X-ray observations.

Contribution

The paper develops a Bayesian approach for identifying periodic signals in red noise-dominated data, incorporating posterior predictive checks and improved continuum modeling.

Findings

Detected the known QPO in RE J1034+396 with revised significance

Applied the method to Seyfert galaxy data, confirming spectral features

Enhanced detection reliability through better nuisance parameter treatment

Abstract

Many astrophysical sources, especially compact accreting sources, show strong, random brightness fluctuations with broad power spectra in addition to periodic or quasi-periodic oscillations (QPOs) that have narrower spectra. The random nature of the dominant source of variance greatly complicates the process of searching for possible weak periodic signals. We have addressed this problem using the tools of Bayesian statistics; in particular using Markov chain Monte Carlo techniques to approximate the posterior distribution of model parameters, and posterior predictive model checking to assess model fits and search for periodogram outliers that may represent periodic signals. The methods developed are applied to two example datasets, both long XMM-Newton observations of highly variable Seyfert 1 galaxies: RE J1034+396 and Mrk 766. In both cases a bend (or break) in the power spectrum is evident. In the case of RE J1034+396 the previously reported QPO is found but with somewhat weaker statistical significance than reported in previous analyses. The difference is due partly to the improved continuum modelling, better treatment of nuisance parameters, and partly to different data selection methods.