Detector Control - Revision history

Jan.harms at 19:58, 18 April 2020

2020-04-18T19:58:43Z

2020-04-18T19:57:46Z

2020-04-15T20:37:32Z

2020-04-05T13:14:47Z

2020-04-05T13:12:09Z

2020-04-05T12:04:59Z

2020-04-05T12:03:03Z

2020-04-05T12:02:21Z

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[[https://iopscience.iop.org/article/10.1088/1361-6382/aaa4aa Controls adjustments at LIGO to ride out earthquakes (2018)]]

@@ Line 8: / Line 8: @@
 Details of the controls modification at LIGO can be found here:
-[[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 Biscans et al (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 Mukund et al (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.

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 = Earthquake early warning for GW detectors and earthquake controls modifications =
-Based on real-time earthquake parameter estimation ([[https://usgs.github.io/pdl/ 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.
+Gravitational-wave detectors rely on careful control of their mechanics including the suspended test masses to make sure that optics are properly aligned and distances between suspended optics do not change by more than a small fraction of the laser wavelength (1064nm) due to low-frequency forces like tides or ground motion. This way, the detector can be continuously operated and in a state of high light power inside the arm cavities.
 Details of the controls modification at LIGO can be found here:

← Older revision		Revision as of 20:37, 15 April 2020
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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~~.	+	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.

← Older revision		Revision as of 13:14, 5 April 2020
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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)]]
		+
		+	= 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.

← Older revision		Revision as of 13:12, 5 April 2020
Line 1:		Line 1:
		+	= Earthquake early warning for GW detectors and earthquake controls modifications =
		+
		+	Based on real-time earthquake parameter estimation ([[https://usgs.github.io/pdl/ 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:
	[[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)]]

	[[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)]]

← Older revision		Revision as of 12:04, 5 April 2020
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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)]]

−	[[https://iopscience.iop.org/article/10.1088/1361-6382/ab28c1 Ground-motion prediction 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)]]

← Older revision		Revision as of 12:03, 5 April 2020
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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)]]
		+
		+	[[https://iopscience.iop.org/article/10.1088/1361-6382/ab28c1 Ground-motion prediction using archival earthquake data (2019)]]