group
What campus are you from?
Daytona Beach
Authors' Class Standing
Kassidy Myers, Senior Anthony James Fabrega, Senior Anthony Gilbert, Sophomore William Punches, Senior Bailey Wolf, Senior
Lead Presenter's Name
Kassidy Myers
Faculty Mentor Name
SM Caballero-Nieves
Abstract
The evolution of Wolf-Rayet (WR) stars is influenced by the star’s mass loss channel, either through stellar winds or binary evolution. Studying WR stars provides insight into the formation and evolution of massive stars. Binary WR systems, which are comprised of the WR star and a companion O-star, are of particular interest because they allow researchers to determine the mass of the WR star. The Large Magellanic Cloud (LMC) is an ideal hunting ground for such WR stars due to its low metallicity. In this work, we use NASA’s Transiting Exoplanet Survey Satellite (TESS) data to train a supervised machine learning algorithm to classify binary WR stars in the LMC using photometric signatures in their light curves and highly comparative time-series analysis (see work by R. Gilbert et al.). We applied a Python pipeline (see work by B. Wolf et al.) to extract TESS data for 137 WR stars brighter than 16 magnitudes in the V-band. Additionally, we built upon his supervised model being applied to galactic WR stars. For our training sample, we have selected 44 possible binary WR systems in the LMC, of which 27 are confirmed binaries and the rest are candidate binary systems. These 27 known systems will be used to determine the accuracy of the machine learning algorithm in classifying WR stars as binaries and the results are presented in this poster. Building on the results of this project, we aim to expand this study to include classification of intrinsic photometric variability of WR stars and demonstrate TESS’s applications beyond exoplanet identification.
Did this research project receive funding support from the Office of Undergraduate Research.
No
Wolf-Rayet Astrophysics and Pulsations (WRAP): A Machine Learning approach to TESS observations for the LMC
The evolution of Wolf-Rayet (WR) stars is influenced by the star’s mass loss channel, either through stellar winds or binary evolution. Studying WR stars provides insight into the formation and evolution of massive stars. Binary WR systems, which are comprised of the WR star and a companion O-star, are of particular interest because they allow researchers to determine the mass of the WR star. The Large Magellanic Cloud (LMC) is an ideal hunting ground for such WR stars due to its low metallicity. In this work, we use NASA’s Transiting Exoplanet Survey Satellite (TESS) data to train a supervised machine learning algorithm to classify binary WR stars in the LMC using photometric signatures in their light curves and highly comparative time-series analysis (see work by R. Gilbert et al.). We applied a Python pipeline (see work by B. Wolf et al.) to extract TESS data for 137 WR stars brighter than 16 magnitudes in the V-band. Additionally, we built upon his supervised model being applied to galactic WR stars. For our training sample, we have selected 44 possible binary WR systems in the LMC, of which 27 are confirmed binaries and the rest are candidate binary systems. These 27 known systems will be used to determine the accuracy of the machine learning algorithm in classifying WR stars as binaries and the results are presented in this poster. Building on the results of this project, we aim to expand this study to include classification of intrinsic photometric variability of WR stars and demonstrate TESS’s applications beyond exoplanet identification.