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Kepler Refreshed: New Rotation Measurements from Kepler Bonus Background Light Curves

Claytor, Zach; Tayar, Jamie

Abstract

The Kepler field hosts the best studied sample of field star rotation periods. However, due to Kepler's large 4 arcsecond pixels, many of its light curves are at high risk of contamination from background sources. The new Kepler Bonus Background light curves (Martinez-Palomera et al. 2023) are de-blended using a PSF algorithm, providing light curves of over 400,000 new background sources in addition to over 200,000 re-analyzed Kepler prime targets. These light curves provide the opportunity to search for new rotation periods. We present nearly 10,000 new periods for both Kepler prime and background sources, inferred by a convolutional neural network (CNN) trained on synthetic spot-modulated light curves. We also regressed nearly 20,000 periods from previously-measured sources and 600 pulsation frequencies from known asteroseismic oscillators, enabling the first robust validation of this CNN approach on real data. Examining the period distribution, we compare the periods and light curves of foreground-background pairs, finding that as many as 63% of periodic background light curves are still blended with the foreground. Finally, we highlight the regions of parameter space where the deblending is most and least effective for rotation, and we discuss exciting possible applications of this rich, new dataset.

Full text

Kepler Refreshed: New Rotation Measurements from Kepler Bonus Background Light Curves Zach Claytor1 & Jamie Tayar2 1. Space Telescope Science Institute 2. University of Florida Kepler transformed our understanding of stellar rotation with 55,000+ period measurements from 200,000 light curves. The new Kepler Bonus Background data (MartinezPalomera et al. 2023) provide 400,000 new background light curves to search for rotation in Kepler. Shown to the right are 55,000 Kepler rotation periods from Santos et al. (2019, 2021, hex bins) and 10,000 periods from new Kbonus background sources, inferred using machine learning (Claytor & Tayar 2025, red points). Our machine learning periods agree very well with prior measurements (left), and we even detect oscillations (right). This test provides the first largescale, real-world validation of our machine learning period regressor, which has also been used with TESS and will be used with Roman. Many periodic light curves still show blending with close neighbors, but the cross-correlation statistic effectively identifies deblended targets. Summary •We used machine learning to estimate 30,000 stellar rotation periods (10,000 new; 20,000 with prior measurements) from the Kepler Bonus light curves. •Kepler Bonus is a rich trove of data that extends the already immense scientific value of the Kepler mission, but many (half or more) of periodic background light curves are still blended with close neighbors. •We recommend the use of cross-correlation analysis to verify the deblending of science targets. References •Claytor & Tayar 2025. ApJ 987, 8 •Martinez-Palomera et al. 2023. AJ 166, 265 •Santos et al. 2019. ApJS 244, 21 •Santos et al. 2021. ApJS 255, 17 Research supported by NASA ROSES 80NSSC24K0081. arXiv: 2506.03248