Abstract :
Periodograms, and more generally the matched filter, 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. I will 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 periodic signal template, rendering it non-periodic. We show on simulations and real data that 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. It can therefore be used to estimate the false positive probability in a way that is robust to the unmodeled features in the data.
I will show how this approach can be applied to the search for supermassive black hole binaries (SMBHB) and the exoplanet transit search. We reject SMBHB proposed by Charisi et al., 2016, and confirm several Earth-like exoplanets in the habitable zone around Sun-like stars, including the most Earth-like Kepler exoplanet to date, Kepler 452-b.
