Semi-Supervised Standardized Detection of Periodic Signals with Application to Exoplanet Detection

Sophia Sulis; David Mary; Lionel Bigot

ICASSP 2022accepted0 citations

Semi-Supervised Standardized Detection of Periodic Signals with Application to Exoplanet Detection

Abstract

We propose a numerical methodology for detecting periodicities in unknown colored noise and for evaluating the ‘significance levels’ (p-values) of the test statistics. The procedure assumes and leverages the existence of a set of time series obtained under the null hypothesis (a null training sample, NTS) and possibly complementary side information. The test statistic is computed from a standardized periodogram, which is a pointwise division of the periodogram of the series under test to an averaged periodogram obtained from the NTS. The procedure provides accurate p-values estimation through a dedicated Monte Carlo procedure. While the methodology is general, our application is here exoplanet detection. The proposed methods are benchmarked on astrophysical data.

BibTeX

@inproceedings{icassp2022_semisupervisedst,
  title = {Semi-Supervised Standardized Detection of Periodic Signals with Application to Exoplanet Detection},
  author = {Sophia Sulis and David Mary and Lionel Bigot},
  booktitle = {ICASSP 2022},
  year = {2022}
}