Speaker
Description
In this project you will carry out data analysis with simulated and real gravitational-wave data. You will first develop an algorithm called “matched filtering” used for detecting modelled gravitational-wave signals from noisy data. You will demonstrate its use on simple signals – with a leading order “Newtonian chirp” waveform injected within simulated “Gaussian white” noise. Next you study data stretches from public data from the recent observing runs of LIGO and Virgo. You will estimate the statistical properties of the data, and will study interesting stretches of data which include the signal. You will finally extend your algorithm to work with real data as best as possible. Ability to code in Python is a prerequisite. You will use publicly available Python libraries such as gwpy.
