Detecting and Characterizing RR Lyrae and Other Variable Stars in KELT

Project Information

astrophysics, machine-learning, python
Project Status: Recruiting
Project Region: Kentucky
Submitted By: Vikram Gazula
Project Email: deleenm@nku.edu
Project Institution: Northern Kentucky University
Project Address: Kentucky

Mentors: Recruiting
Students: Recruiting

Project Description

The Kilodegree Extremely Little Telescope (KELT) survey is a transiting planet finding survey that can also be used to find and characterize RR Lyrae (RRL) stars in the disk and inner halo of the Milky Way galaxy. RRL stars are of particular interest because they are standard candles and can be used to map out structure in the galaxy. The KELT survey represents a new generation of surveys that has many epochs over a large portion of the sky. KELT samples over 60% of the sky in both northern and southern hemispheres, and has a long-time-baseline of 4-10 years with a very high cadence rate of less than 20 minutes. This translates into 4,000 to 10,000+ epochs per lightcurve with completeness out to 3 kpc from the Sun. The computational side of this project involves determining ways to separate the RRL stars from all the other variable objects. We are exploring a number of avenues to accomplish this including doing cuts in photometric color space, limiting period and amplitude space, and using machine learning techniques.

Project Information

astrophysics, machine-learning, python
Project Status: Recruiting
Project Region: Kentucky
Submitted By: Vikram Gazula
Project Email: deleenm@nku.edu
Project Institution: Northern Kentucky University
Project Address: Kentucky

Mentors: Recruiting
Students: Recruiting

Project Description

The Kilodegree Extremely Little Telescope (KELT) survey is a transiting planet finding survey that can also be used to find and characterize RR Lyrae (RRL) stars in the disk and inner halo of the Milky Way galaxy. RRL stars are of particular interest because they are standard candles and can be used to map out structure in the galaxy. The KELT survey represents a new generation of surveys that has many epochs over a large portion of the sky. KELT samples over 60% of the sky in both northern and southern hemispheres, and has a long-time-baseline of 4-10 years with a very high cadence rate of less than 20 minutes. This translates into 4,000 to 10,000+ epochs per lightcurve with completeness out to 3 kpc from the Sun. The computational side of this project involves determining ways to separate the RRL stars from all the other variable objects. We are exploring a number of avenues to accomplish this including doing cuts in photometric color space, limiting period and amplitude space, and using machine learning techniques.