This module develops an understanding of basic concepts and offers practical experience with the techniques of quantitative data analysis. Beginning with fundamental concepts of probability theory and random variables, practical techniques are developed for using quantitative observational data to answer questions and test hypothesis about models of the physical world. Students develop their computer programming skills, build a data analysis toolkit, and gain practical experience by analysing real data sets. The two projects involve periodogram analysis of quasi-periodic oscillations in the HST light curve of an eclipsing dwarf nova, and a cross-correlation radial velocity analysis and mass estimation for a black hole binary, based on spectra from the Keck 10m telescope.
This course is for students interested in the physics of gravitational wave detection. Starting from the fundamentals of Einstein’s General Theory of Relativity, the wave nature of weak field spacetime curvature perturbations will be derived in the transverse traceless gauge. Interactions of gravitational radiation with matter will be explored, leading to the basic principles of gravitational wave detectors. A full description of currently operating detectors will include instrumental noise sources, such as thermal, seismic, optical, and the standard quantum limit. Current topics discussed will include squeezing, and other non-classical light techniques for reducing optical noise in interferometric systems.
Astrophysical sources of gravitational waves will be discussed including expectations for source strengths from coalescing compact binary systems, pulsars, etc. together with a discussion of the data analysis techniques that are required for signal extraction and parameter estimation. An update will be given on the new astrophysics that has been deduced from the gravitational wave signals so far observed, and the promise of future “multi-messenger astronomy” will be explored. Plans for future detectors on the ground and in space will also be presented.
