Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Narayani Arora, Sophomore
Lead Presenter's Name
Narayani Arora
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Terry Oswalt
Abstract
The Transiting Exoplanet Survey Satellite (TESS) has transformed exoplanet discovery by collecting high-precision light curves from thousands of stars. In this study, a Python pipeline was created that uses LightKurve to cross-reference our team's TESS Cycle 3 dataset with NASA's Exoplanet Database. Light curves have been retrieved and normalized for 23 candidate stars to search for potential planetary transits. The pipeline also generated pixel files to determine source contamination and validate potential transits. This work demonstrates how cross referencing and automated light curve analysis might help refine exoplanet searches in TESS data.
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?
No
Identifying Exoplanetary Signals in TESS Cycle 3 Data Using Cross Referencing and Light Curve Analysis
The Transiting Exoplanet Survey Satellite (TESS) has transformed exoplanet discovery by collecting high-precision light curves from thousands of stars. In this study, a Python pipeline was created that uses LightKurve to cross-reference our team's TESS Cycle 3 dataset with NASA's Exoplanet Database. Light curves have been retrieved and normalized for 23 candidate stars to search for potential planetary transits. The pipeline also generated pixel files to determine source contamination and validate potential transits. This work demonstrates how cross referencing and automated light curve analysis might help refine exoplanet searches in TESS data.