Periodicity significance testing with null-signal templates: reassessment of PTF's SMBH binary candidates

TL;DR

This paper introduces a null-signal template method for more accurate periodicity significance testing in complex data, applied to SMBH binary candidates, revealing no significant signals and improving detection sensitivity with a new Bayesian approach.

Contribution

The paper develops a null-signal template approach that accurately estimates false positive rates without simulations and introduces a Bayesian method to enhance detection sensitivity.

Findings

No significant periodic signals detected in PTF data.

The null-signal template method matches the false positive distribution of traditional methods.

Bayesian approach improves sensitivity by over an order of magnitude.

Abstract

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template regardless of the noise probability distribution, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal to (white) noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic, in contrast to the standard signal to white noise. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, using the Bayes Factor approach also results in no statistically significant detections in the PTF data.