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

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. PIPSis 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 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 PIPSagainst artificially generated data, which demonstrates the reliable performance of our algorithm for a class of periodic variable stars (RR Lyrae stars).

Stellar Rotation and Structure of the α Persei Complex: When Does Gyrochronology Start to Work?

“…For our analysis, we only calculated uncertainties for stars with the "g" and "c" designations as defined above. Although uncertainties on stellar rotation periods have historically not been handled robustly in the literature, software packages such as PIPS (Murakami et al 2022) are making progress toward easy-to-use and accurate methods for calculating uncertainties.…”