PIPS, an advanced platform for period detection in time series – I. Fourier-likelihood periodogram and application to RR Lyrae stars. Issue 3 (7th June 2022)

• Record Type: Journal Article
• Authors: Murakami, Yukei S; Jennings, Connor; Hoffman, Andrew M; Savel, Arjun B; Sunseri, James; Baer-Way, Raphael; Stahl, Benjamin E; Altunin, Ivan; Girish, Nachiket; Filippenko, Alexei V
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 514:Issue 3(2022)
• Page Start: 4489
• Page End: 4505
• Publication Date: 2022-06-07
• Subjects: methods: data analysis -- methods: statistical -- stars: variables; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stac1538
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We describe the Period detection and Identification Pipeline Suite (pips ) – a new, fast, and statistically robust platform for period detection and analysis of astrophysical time-series data. PIPS is an open-source Python package that provides various pre-implemented methods and a customizable framework for automated, robust period measurements with principled uncertainties and statistical significance calculations. In addition to detailing the general algorithm that underlies PIPS, this paper discusses one of PIPS ' central and novel features, the Fourier-likelihood periodogram, and compares its performance to existing methods. The resulting improved performance implies that one can construct deeper, larger, and more reliable sets of derived properties from various observations, including all-sky surveys. We present a comprehensive validation of PIPS against artificially generated data, which demonstrates the reliable performance of our algorithm for a class of periodic variable stars (RR Lyrae stars).