Long rotation period main-sequence stars from Kepler SAP light curves

We explore a systematic approach based on the light curve pre-processing, period detection and candidate selection. We also develop a simulated light curve test to quantify our detection limits for the SAP data. After applying our method to the raw SAP light curves, we found more than 1000 main-sequence stars with the period longer than 30 days, 165 are newly discovered.

KIC 12268220: A delta Scuti Pulsating Star and an Active Protohelium White Dwarf in an Eclipsing Binary System

We present a photometric, spectroscopic, asteroseismic, and evolutionary analysis of the Algol-type eclipsing binary KIC 12268220. The evolutionary tracks of our model suggest that the low-mass (~ 0.23 solar masses) evolved secondary shows a similar evolutionary state to the R CMa-type system, which might evolve to an EL CVn system.

Substellar Companions to Nearby Stars

We analyze 5108 AFGKM stars with at least five high-precision radial velocity points, as well as Gaia and Hipparcos astrometric data, utilizing a novel pipeline developed in previous work. 167 cold giants and 68 other types of companions are identified, through combined analyses of radial velocity, astrometry, and imaging data. We find a significant brown-dwarf valley around 40 MJup. We also find a power-law distribution in the host binary fraction beyond 3 au, similar to that found for single stars, indicating no preference of multiplicity for brown dwarfs.

A General Light Curve Classification Framework

We present a novel deep learning framework for classifying light curves using a weakly supervised object detection model. Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data. This allows for automatic feature extraction from both time and frequency domains, enabling our model to handle data across different scales and sampling intervals. We train our model on datasets obtained from both space-based and ground-based multi-band observations of variable stars and transients. We achieve an accuracy of 87% for combined variables and transient events, which is comparable to the performance of previous feature-based models. Our trained model can be utilized directly to other missions, such as ASAS-SN, without requiring any retraining or fine-tuning.. The code is available at GitHub .

Transit Detection

We develop a novel detection algorithm based on a well-proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields 94% precision and 95% recall for transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). The code is available at GitHub .