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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

Description:

Explore gravitational wave parameter estimation techniques in this 42-minute lecture by Scott Field from the University of Massachusetts Dartmouth. Delve into the challenges of Bayesian parameter estimation in gravitational wave astronomy and learn about Reduced Order Quadratures (ROQs) as a solution for fast and accurate likelihood evaluations. Discover the key algorithms and software needed to build ROQ rules, and gain insights into their applications for various gravitational wave models. Examine the computational benefits of ROQs, including their exponential convergence and potential to significantly accelerate inference runs. Investigate new challenges and opportunities for ROQs in upcoming detectors, and understand their importance in advancing gravitational wave research.

Gravitational Wave Parameter Estimation with Compressed Likelihood Evaluations

Institute for Pure & Applied Mathematics (IPAM)

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#Science #Astronomy #Cosmology #Gravitational Waves #Gravitational Wave Astronomy #Mathematics #Statistics & Probability #Bayesian Statistics #Bayesian Inference #Numerical Analysis #Numerical Integration

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