Modeling Stellar Companions in SDSS

Project Information

big-data, astrophysics
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 Sloan Digital Sky Survey (SDSS) is an international collaboration of astronomers including faculty at University of Kentucky (e.g. Renbin Yan and Ron Wilhelm). In this project, we are exploring how well we can model and detect stellar companions using SDSS data. Stellar companions include stars, brown dwarfs, and planets. One important aspect of this modeling is to understand how well we can recover simulated companions that are designed to look like data taken from the first and second generation of the Apache Point Observatory Galactic Evolution Experiment (APOGEE and APOGEE-2) which are sub-surveys of SDSS. We are testing a variety of different algorithms to figure out which ones provide the best balance of speed versus accuracy. When exploring the chi-squared space of a fit to radial velocity orbital data, the orbital period is one of the most important orbital elements. As a result, we are using period finding algorithms including Lomb-Scargle, AoV, and Fast Chi-squared. We are also trying Markov-Chain Monte Carlo rejection sampling techniques which are slower, but tend to be more accurate.

Project Information

big-data, astrophysics
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 Sloan Digital Sky Survey (SDSS) is an international collaboration of astronomers including faculty at University of Kentucky (e.g. Renbin Yan and Ron Wilhelm). In this project, we are exploring how well we can model and detect stellar companions using SDSS data. Stellar companions include stars, brown dwarfs, and planets. One important aspect of this modeling is to understand how well we can recover simulated companions that are designed to look like data taken from the first and second generation of the Apache Point Observatory Galactic Evolution Experiment (APOGEE and APOGEE-2) which are sub-surveys of SDSS. We are testing a variety of different algorithms to figure out which ones provide the best balance of speed versus accuracy. When exploring the chi-squared space of a fit to radial velocity orbital data, the orbital period is one of the most important orbital elements. As a result, we are using period finding algorithms including Lomb-Scargle, AoV, and Fast Chi-squared. We are also trying Markov-Chain Monte Carlo rejection sampling techniques which are slower, but tend to be more accurate.