Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Lindsay Spence, Senior
Lead Presenter's Name
Lindsay Spence
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Tomomi Otani
Abstract
A double star, also known as a binary star, is a system in which two stars orbit around a shared center of mass. According to NASA, more than half of all stars in the sky belong to such multi star systems. Detecting binary stars is essential for understanding stellar evolution and the broader universe. Astronomers use satellites and ground-based telescopes to collect data used to discover binary stars. This research poster focuses on the Observed Minus Calculated (O-C) Method, which is primarily used for stars that vary in brightness over time. The method works by comparing observed brightness variations to calculated values based on how the star’s brightness should change if it were pulsating steadily. A periodic difference between the observed and calculated values may indicate the presence of an unseen companion star. This variation occurs because the star's radial distance from Earth changes as the binary stars orbit each other, causing shifts in brightness, referred to as a wobble. To improve efficiency, we developed software that analyzes stellar data to identify potential binary star systems. However, a common issue with the O-C Method is false positives caused by two or more beat frequencies—small variations in brightness with a similar variation period that can mimic the presence of a second star. Two or more beat frequencies can result from factors such as changing magnetic fields, a star’s rotation, or inherent stellar properties. Because of this, the O-C Method is not widely used for binary detection. To increase reliability of the O-C Method, we updated our software to incorporate a new process that reduces false positives by removing beat frequencies. We tested this improvement using artificial datasets, where the expected results are known, allowing us to assess the software’s accuracy. Our findings suggest that the updated software significantly enhances binary star detection while minimizing false positives caused by beat frequencies.
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
Double star detection software - False positive elimination
A double star, also known as a binary star, is a system in which two stars orbit around a shared center of mass. According to NASA, more than half of all stars in the sky belong to such multi star systems. Detecting binary stars is essential for understanding stellar evolution and the broader universe. Astronomers use satellites and ground-based telescopes to collect data used to discover binary stars. This research poster focuses on the Observed Minus Calculated (O-C) Method, which is primarily used for stars that vary in brightness over time. The method works by comparing observed brightness variations to calculated values based on how the star’s brightness should change if it were pulsating steadily. A periodic difference between the observed and calculated values may indicate the presence of an unseen companion star. This variation occurs because the star's radial distance from Earth changes as the binary stars orbit each other, causing shifts in brightness, referred to as a wobble. To improve efficiency, we developed software that analyzes stellar data to identify potential binary star systems. However, a common issue with the O-C Method is false positives caused by two or more beat frequencies—small variations in brightness with a similar variation period that can mimic the presence of a second star. Two or more beat frequencies can result from factors such as changing magnetic fields, a star’s rotation, or inherent stellar properties. Because of this, the O-C Method is not widely used for binary detection. To increase reliability of the O-C Method, we updated our software to incorporate a new process that reduces false positives by removing beat frequencies. We tested this improvement using artificial datasets, where the expected results are known, allowing us to assess the software’s accuracy. Our findings suggest that the updated software significantly enhances binary star detection while minimizing false positives caused by beat frequencies.