Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates
[1];
[2];
- University of California, Berkeley, CA (United States)
- Princeton Univ., NJ (United States); Flatiron Institute, New York, NY (United States)
- Washington State Univ., Pullman, WA (United States); Foundation for Research & Technology-Hellas, Heraklion (Greece)
- Columbia Univ., New York, NY (United States)
- University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
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, 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-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. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); European Union (EU); Heising-Simons Foundation
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 2479508
- Journal Information:
- Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 2 Vol. 534; ISSN 0035-8711
- Publisher:
- Oxford University PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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