Difference between revisions of "Detector Control"

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= Nonlinear feedback control =
 
= Nonlinear feedback control =
This is a new activity of the group at GSSI to use machine-learning algorithms for improved controls of GW detectors with the goal to lower controls noise in the observation band. This work is key to open the low-frequency band to ground-based GW detectors.
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The goal of nonlinear feedback control is to improve robustness of a detector to external disturbances and to reduce controls noise relative to the current, linear controls methods applied at the detectors. Improvements can be expected in the presence of nonstationary data, or when the system has important nonlinearities in its dynamics. Our approach is to develop machine-learning techniques for feedback control, with current focus on reinforcement learning.

Revision as of 20:37, 15 April 2020

Earthquake early warning for GW detectors and earthquake controls modifications

Based on real-time earthquake parameter estimation ([USGS PDL client]), we realized an earthquake early warning for GW detectors, which is used today at LIGO and Virgo detectors. The purpose is not only to alert the operators and commissioners of an incoming earthquake, but also to predict the probability that an earthquake will interrupt the operation of the detector. If such a "lock loss" is predicted, the operators can decide to modify the controls into a more robust (and slightly noisier) state with the goal to ride out the earthquake and remain operative. The gain is to increase the duty cycle of a detector.

Details of the controls modification at LIGO can be found here: [Controls adjustments at LIGO to ride out earthquakes (2018)]

Improved prediction of lock loss was achieved with machine-learning techniques: [Ground-motion prediction with machine learning using archival earthquake data (2019)]

Nonlinear feedback control

The goal of nonlinear feedback control is to improve robustness of a detector to external disturbances and to reduce controls noise relative to the current, linear controls methods applied at the detectors. Improvements can be expected in the presence of nonstationary data, or when the system has important nonlinearities in its dynamics. Our approach is to develop machine-learning techniques for feedback control, with current focus on reinforcement learning.