Reconstructing the calibrated strain signal in the Advanced LIGO detectors. (4th April 2018)

• Record Type: Journal Article
• Authors: Viets, A D; Wade, M; Urban, A L; Kandhasamy, S; Betzwieser, J; Brown, Duncan A; Burguet-Castell, J; Cahillane, C; Goetz, E; Izumi, K; Karki, S; Kissel, J S; Mendell, G; Savage, R L; Siemens, X; Tuyenbayev, D; Weinstein, A J
• Is Part Of: Classical and quantum gravity. Volume 35:Number 9(2018:May)
• Publication Date: 2018-04-04
• Subjects: gravitational waves -- LIGO -- calibration -- digital signal processing -- interferometry -- general relativity -- astrophysics; Quantum gravity -- Periodicals; Gravitation -- Periodicals; Relativity (Physics) -- Periodicals; Space and time -- Periodicals; Periodicals; 521.1
• DOI: 10.1088/1361-6382/aab658
• Languages: English
• ISSNs: 0264-9381

Abstract

Abstract: Advanced LIGO's raw detector output needs to be calibrated to compute dimensionless strain h ( t ) . Calibrated strain data is produced in the time domain using both a low-latency, online procedure and a high-latency, offline procedure. The low-latency h ( t ) data stream is produced in two stages, the first of which is performed on the same computers that operate the detector's feedback control system. This stage, referred to as the front-end calibration, uses infinite impulse response (IIR) filtering and performs all operations at a 16 384 Hz digital sampling rate. Due to several limitations, this procedure currently introduces certain systematic errors in the calibrated strain data, motivating the second stage of the low-latency procedure, known as the low-latencygstlal calibration pipeline. Thegstlal calibration pipeline uses finite impulse response (FIR) filtering to apply corrections to the output of the front-end calibration. It applies time-dependent correction factors to the sensing and actuation components of the calibrated strain to reduce systematic errors. Thegstlal calibration pipeline is also used in high latency to recalibrate the data, which is necessary due mainly to online dropouts in the calibrated data and identified improvements to the calibration models or filters.