Document Type
Research paper
Publication Date
7-25-2024
Abstract
Data science techniques have wide-ranging applications throughout scientific explorations. One, is filtering astronomical data to better understand specific populations, such as binary stars. Specifically, binary stars that exhibit the O’Connell effect are worthy of study as this phenomenon is still not well understood. The O’Connell effect can be defined as the asymmetry of maxima in the light curves, as captured by the instrument, while observing the eclipsing binary system in question. There is significant data captured by NASA and curated by Villanova University, which enabled the investigation of eclipsing binary stars and the attributes of which may help identify the O’Connell effect. Two sets of data from Villanova were used, the Kepler and Transiting Exoplanet Survey Satellite eclipsing binary star catalogs. From these, morphology, period, and separation were used as attributes of the system to help identify the O’Connell effect. Handpicked Kepler data was taken and used to train a machine learning model that was then applied to Transiting Exoplanet Survey Satellite data, in order to make determinations if the targets therein exhibited the O’Connell effect. The machine learning models' determinations were compared against labels rendered by evaluating the light curve for asymmetries in the maxima.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Thesis for Master of Science in Computer Science, presented July 25, 2024 at Kutztown University of PA
Included in
Data Science Commons, Instrumentation Commons, Stars, Interstellar Medium and the Galaxy Commons
