PIPS, an advanced platform for period detection in time series -- I. Fourier-likelihood periodogram and application to RR Lyrae Stars

TL;DR

PIPS is a new open-source Python platform that enhances period detection in astrophysical time-series data, featuring a novel Fourier-likelihood periodogram that improves reliability and depth of analysis for variable stars like RR Lyrae.

Contribution

Introduction of PIPS, a robust, customizable platform with a novel Fourier-likelihood periodogram for improved period detection in astrophysics.

Findings

Fourier-likelihood periodogram outperforms existing methods

PIPS provides reliable period measurements with principled uncertainties

Validated on simulated data for RR Lyrae stars

Abstract

We describe the $\texttt{Period detection and Identification Pipeline Suite}$ ($\texttt{PIPS}$) -- a new, fast, and statistically robust platform for period detection and analysis of astrophysical time-series data. $\texttt{PIPS}$ is an open-source Python package that provides various pre-implemented methods and a customisable framework for automated, robust period measurements with principled uncertainties and statistical significance calculations. In addition to detailing the general algorithm that underlies $\texttt{PIPS}$, this paper discusses one of $\texttt{PIPS'}$ 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 $\texttt{PIPS}$ against artificially generated data, which demonstrates the reliable performance of our algorithm for a class of periodic variable stars (RR Lyrae stars).