New Rotation Periods from the Kepler Bonus Background Light Curves

Abstract

The Kepler field hosts the best-studied sample of field star rotation periods. However, due to Kepler’s large 4″ pixels, many of its light curves are at high risk of contamination from background sources. The new Kepler Bonus Background light curves are deblended using a point-spread function algorithm, providing light curves of over 400,000 new background sources in addition to over 200,000 reanalyzed Kepler prime targets. These light curves provide the opportunity to search for new rotation periods. Here we apply a convolutional neural network trained on synthetic spot-modulated light curves to regress rotation periods from the Kepler Bonus light curves. We obtained periods for 32,159 total sources, 19,650 of which had previously been measured and 9811 of which are new periods for both Kepler prime and background sources. Our method also detected 608 pulsation frequencies from asteroseismic oscillations in red giants. We validate our Kepler prime periods against literature values and present the full period sample. We find excellent agreement with previously known literature periods, validating deep learning as a viable class of period determination methods. Comparing the periods and light curves of foreground–background pairs, we find that as many as 63% of periodic background light curves are still blended with the foreground, highlighting the limitations of the deblending technique.

Keywords

• Stellar rotation
• Convolutional neural networks
• Light curves
• Stellar properties
• Stellar physics
• Stellar populations