Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Krystian Confeiteiro, senior Carina Shanahan, senior Sima Vaidya, senior Sam Lutzel, senior, Petar Grigorov, graduate student
Lead Presenter's Name
Krystian Confeiteiro
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Terry Dean Oswalt
Abstract
Gyrochronology—the empirical relation between rotation and age among lower main sequence stars—has the potential to provide useful age estimates in regimes where other techniques break down. However, its usefulness depends on the precision with which stellar rotation periods can be determined. To quantify the uncertainties associated with several period-finding algorithms, our team visually assessed a sample of almost 4000 main sequence binaries observed by NASA’s Transiting Exoplanet Survey Satellite (TESS) mission. Machine learning and deep learning algorithms were then employed to assess the quality of the TESS light curves, using the visually analyzed light curves as a training set. We present a status report and a comparison of various gyrochronology models.
Acknowledgments: Support for this project from the NSF AAG grant AST-1910396 and NASA ADAP grant 80NSSC22K0622 to Embry-Riddle Aeronautical University is gratefully acknowledged.
Funding for the TESS mission is provided by NASA’s Science Mission directorate.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
Testing the Gyrochronology Paradigm Using Wide Coeval Binary Stars
Gyrochronology—the empirical relation between rotation and age among lower main sequence stars—has the potential to provide useful age estimates in regimes where other techniques break down. However, its usefulness depends on the precision with which stellar rotation periods can be determined. To quantify the uncertainties associated with several period-finding algorithms, our team visually assessed a sample of almost 4000 main sequence binaries observed by NASA’s Transiting Exoplanet Survey Satellite (TESS) mission. Machine learning and deep learning algorithms were then employed to assess the quality of the TESS light curves, using the visually analyzed light curves as a training set. We present a status report and a comparison of various gyrochronology models.
Acknowledgments: Support for this project from the NSF AAG grant AST-1910396 and NASA ADAP grant 80NSSC22K0622 to Embry-Riddle Aeronautical University is gratefully acknowledged.
Funding for the TESS mission is provided by NASA’s Science Mission directorate.