Gravitational Wave Parameter Estimation with Compressed Likelihood Evaluations
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Intro
Gravitational wave datasets
Bayesian inference of GW datasets
Likelhood computations are too slow
Parameter estimation challenges
Approaches to faster PE (non-exhausthe list)
Reduced order quadratures (ROQS) in use
Outline
Numerical integration (quadrature)
Do I need a low-order quadrature rule for noisy data?
Probler Formulation
Step 1: Compressing the model
Best approximation space X
Example basis generation
Waveform compression application (ex: 1.2040)
Summary of step 1
Where are the good points for integrating in X.?
Empirical interpolation method
Example: Points for polynomial interpolation integratior
The ROQ approximation
Using ROQ
Building ROQ
Startup a signal has been detected!
How much faster?
Accelerating tests of GR
BNS events with third generation observatories
Taught by
Institute for Pure & Applied Mathematics (IPAM)