Difference between revisions of "Detector Control"
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[[https://iopscience.iop.org/article/10.1088/1361-6382/aaa4aa Controls adjustments at LIGO to ride out earthquakes (2018)]] | [[https://iopscience.iop.org/article/10.1088/1361-6382/aaa4aa Controls adjustments at LIGO to ride out earthquakes (2018)]] | ||
| + | Improved prediction of lock loss was achieved with machine-learning techniques: | ||
[[https://iopscience.iop.org/article/10.1088/1361-6382/ab28c1 Ground-motion prediction with machine learning using archival earthquake data (2019)]] | [[https://iopscience.iop.org/article/10.1088/1361-6382/ab28c1 Ground-motion prediction with machine learning using archival earthquake data (2019)]] | ||
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| + | = 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. | ||
Revision as of 13:14, 5 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
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.
