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Validation of the RR Lyrae period determination in the Pan-STARRS PS1 3π survey with K2

2024/08/26 by Adrienn Forró, Forró, Adrienn, L. Molnár +7 · 1 citation
Earth and Planetary Sciences · Engineering · Physics and Astronomy · #Astronomical Observations and Instrumentation #FOS: Physical sciences #Gamma-ray bursts and supernovae #Geophysics and Gravity Measurements #Solar and Stellar Astrophysics (astro-ph.SR)

paper · pdf · doi:10.48550/arxiv.2408.14260

openalex publication_date 2024/08/26 · openalex created_date 2024/09/21 · openalex updated_date 2026/07/28

Abstract

RR Lyrae variables are old, helium-core burning stars on the horizontal branch that are important tools for tracing the structure of the Galaxy thanks to their high luminosity and their period-luminosity relation. Pan-STARRS observed a large number of them during its PS1 3π survey. However, as a ground-based telescope, it could only observe during night-time and its 4-year-long light curves contain a dozen or so data points in each filter. Sesar et al. (2017) constructed a catalog of RR Lyrae stars found in the Pan-STARRS data. The objective of this study was to validate both the classification and the period determination of the stars. During Kepler’s K2 mission only limited sky areas were observed, with a high sampling frequency, although each for a shorter duration of ~80 days. We investigated the overlap and found 1,353 RR Lyrae stars in total. This list was also cross-matched with the Gaia DR3 and Gaia RR Lyrae catalogs. For the vast majority of the stars, the classification and the period from the K2 data was consistent with those from the Sesar et al. catalog and the Gaia data. Analyzing the frequencies of the remaning stars, we found a systematic difference, a 1 or 2 c/d offset between the data sets. It affected 7.4% of the sample, and 25.3% of RRc stars specifically. The reason for aliases in the Pan-STARRS frequencies is the sampling bias resulting from the diurnal cycle. Since the RRc subtypes have a less sharp light curve shape, they are more likely to be affected.

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