Gravitational Wave Detection

Title: A Comprehensive Approach to Gravitational Wave Data Analysis
Faculty Mentor: Dr. Joey Shapiro Key

The LIGO BayesWave algorithm is capable of fitting the non-stationary LIGO noise, identifying glitch events, and calculating the Bayes factor, or odds ratio, for the identification of a marginal gravitational wave signal. Participants will have the opportunity to use BayesWave techniques to study simulated LIGO data in this comprehensive approach. Parameter estimation techniques using Markov Chain Monte Carlo (MCMC) machinery will be used to determine LIGO noise characteristics and the parameters of glitches in the data. The participants will then search data with injected burst sources of gravitational waves to demonstrate the detection capability of the BayesWave algorithm.

Title: Applications of Particle Swarm Optimization in Astronomical Data Analysis
Faculty Mentor: Dr. Soumya Mohanty

Particle Swarm Optimization (PSO) is a method for finding the global optimum of high dimensional rugged functions. Since challenging optimization problems are ubiquitous in every field of science and engineering, methods like PSO have made a broad impact across a wide range of application areas. The same is true for gravitational wave (GW) data analysis, where the basic optimization problem consists of finding the signal model that best fits some given noisy data. In this project, participants will learn about PSO and develop its applications to optimization problems in astronomical (including GW) data analysis problems. This project is quite feasible for undergraduate students having a good background in programming and it can lead to publishable results. (Some student authored publications based on PSO are cited below.)

Calvin Leung (Harvey Mudd), Estimation of Unmodeled Gravitational Wave Transients with Spline Regression and Particle Swarm Optimization, (2014).

Joseph Hill (Brigham Young University), Senior thesis. (2013).

Yan Wang (Nanjing University) and Soumya D. Mohanty, Phys. Rev. D, 81, 063002 (2010).

Title: Characterization of background noise in LIGO
Faculty Mentor: Dr. Soma Mukherjee

One of the major areas of research in LIGO is called Detector Characterization. This involves looking and characterizing the real data coming out of the LIGO detectors both in real time, as well as off-line. Noise analysis provides feedback to the experimentalists so that the instrument can be diagnosed for spurious behavior. This provides a great platform for under-graduate students to learn both about the detector physics as well as about the data analysis techniques. Students will work on developing and testing methods to detect correlation between time series from different detector channels. Specifically students will learn about LIGO data, methods of storage and extraction of LIGO data, MATLAB as a data analysis tool, methods of statistical data analysis and methods of looking at glitches seen in the data for understanding their possible origin in the detector sub- systems.