Machine Learning and Gravitational Wave Detectors

Explore the application of machine learning techniques in gravitational wave detectors through this 40-minute conference talk presented by Gabriele Vajente from the California Institute of Technology. Delve into the potential of machine learning to enhance instrument science, focusing on improving sensitivity through noise subtraction and increasing detector robustness with advanced control systems. Examine the current approaches, reinforcement learning, and nonlinear state estimators used in control and sensing problems. Investigate the technical difficulties encountered in high sensitivity operations and the reasons behind avoiding neural networks in certain applications. Gain insights into brute force coherence and nonlinear coherence methods. Discover the emerging field of machine learning in gravitational wave detector instrument science and its promising results in this presentation from IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics.

Intro
Control and sensing problems
Current approach
Reinforcement learning
Nonlinear state estimator
Results
Technical difficulties
High sensitivity operation
Machine learning
Noise attraction
Why not a neural network
Brute Force Coherence
Nonlinear Coherence
Conclusions