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Learn gravitational wave parameter inference through hands-on exercises in this comprehensive lecture from the Prospects in Theoretical Physics 2025 program. Explore practical techniques for extracting physical parameters from gravitational wave signals detected by observatories like LIGO and Virgo. Work through computational methods used to analyze waveform data and determine properties of merging black holes and neutron stars, including their masses, spins, and orbital characteristics. Gain experience with Bayesian inference frameworks commonly employed in gravitational wave astronomy to quantify uncertainties in parameter estimation. Practice implementing statistical sampling techniques and likelihood functions essential for interpreting gravitational wave observations. Develop skills in data analysis workflows that bridge theoretical predictions with experimental measurements in this rapidly advancing field of physics.
