Identification of periodicities with arbitrary shapes in AGN light curves

TL;DR

This paper introduces a flexible Gaussian process-based method to detect complex, arbitrary periodicities in AGN light curves, improving over traditional sinusoidal-focused techniques, and enhances the identification of potential black hole binary signals in large surveys.

Contribution

The paper presents a novel Gaussian process approach with a generic periodic kernel for identifying non-sinusoidal periodicities in unevenly sampled AGN light curves, outperforming previous methods.

Findings

The method effectively detects non-sinusoidal periodicities like sawtooth shapes.

It retrieves a higher fraction of true periodicities compared to traditional periodogram analysis.

Sensitivity increases with the number of observed cycles in the data.

Abstract

Massive black hole binaries are expected to be observable as periodic AGN in time-domain photometric surveys. Periodicities may originate from different physical processes, including the intermittent gas feeding of the black holes caused by the time-varying non-axisymmetric binary potential, the Doppler boosting of the flux emitted by individual accretion discs bound to the orbiting BHs, and the gravitational lensing of the accretion disc of one black hole due to the presence of the other. Only the Doppler boost scenario applied to circular binaries with non-modulated accretion predicts a sinusoidal light curve, while in the general case, binary signals are expected to show more complex periodic patterns. Current searches for massive black hole binaries rely on techniques tailored to quasi-sinusoidal light curves, but fail to identify the more complex periodicities predicted. We present an alternative method that leverages Gaussian processes, making use of a generic periodic kernel flexible enough for the identification of arbitrary periodicities in unevenly sampled light curves with realistic quasar noise. We demonstrate that it outperforms previously proposed strategies in identifying general periodicities by analysing mock light curves with different baselines. Specifically, we find that our analysis can detect non-sinusoidal periodicities (e.g., sawtooth-shaped) and retrieves a higher fraction of true periodicities when compared to periodogram analysis or Gaussian processes analysis with less flexible periodic kernels. Furthermore, by comparing the retrieved fraction of periodicities between mock PTF light curves and mock LSST light curves, we find that our analysis is most sensitive to the number of observed cycles. The application of this analysis has the potential to greatly increase the scientific return of current and upcoming large time-domain photometric surveys.