This IMPRS course on “Making sense of data: introduction to statistics for gravitational wave astronomy” is a repeat of the statistics course in 2019 with some modifications. There will be four weeks of lectures. The first three weeks will run consecutively from November 8th to November 26th and will be lectured by Prof. Dr. Jonathan Gair, group leader in the Astrophysical and Cosmological Relativity department at the AEI. The final week (December 6th to December 10th) will be lectured by Dr. Stephen Green, postdoc in the Astrophysical and Cosmological Relativity department at the AEI.

Lectures will take place from 11am to 12pm on Monday, Wednesday, Thursday, and Friday of each week, with the exceptions of November 10th, on which the lecture will take place at 3.30pm and December 6th, on which the lecture will take place at 2pm.

Lectures can be attended in person in room 0.01 at the AEI Potsdam (hygiene restrictions apply), or via Zoom (link is available here).

When available, the lecture notes and lecture recordings can be found under “Course Materials” on the pages of the individual lectures.

Synopsis: Measurements of the properties of gravitational wave sources are imperfect due to the presence of noise in the gravitational wave interferometers used to detect them. Extracting useful scientific information from these observations therefore requires careful statistical analysis of the data in order to understand the significance of the observed events, the level of uncertainty in the parameter estimates and the implications of the observations for the population from which the sources are drawn. This lecture course will give an overview of some key statistical ideas and techniques that are essential for interpreting current and future gravitational wave observations.

Provisional plan for lecture topics

Week 1 (November 8th – 12th): Frequentist statistics

Week 2 (November 15th – 19th): Bayesian statistics

Week 3 (November 22nd – 26th): Sampling using Stan (python practicals) 

Week 4 (December 6th – 10th): Introduction to Machine Learning