Sorry, you need to enable JavaScript to visit this website.

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

Citation Author(s):
Sophia Sulis, David Mary, Lionel Bigot
Submitted by:
SULIS Sophia
Last updated:
11 May 2022 - 4:47am
Document Type:
Presentation Slides
Document Year:
2022
Event:
Presenters:
Sophia Sulis
Paper Code:
2871

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.

up
0 users have voted:

Files

SULIS_PAPER_2871_ICASSP_2022.pdf

(40)