Reconstructing the calibrated strain signal in the Advanced LIGO detectors
Reconstructing the calibrated strain signal in the Advanced LIGO detectors
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DOI：
10.1088/1361-6382/aab658
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发表时间：
2017-10
影响因子：
3.5
通讯作者：
A. Viets;A. Viets;M. Wade;A. Urban;S. Kandhasamy;J. Betzwieser;Duncan A. Brown;J. Burguet-Castell;C. Cahillane;E. Goetz;K. Izumi;S. Karki;J. Kissel;G. Mendell;R. Savage;X. Siemens;D. Tuyenbayev;A. Weinstein
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文献类型：
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作者：
A. Viets;A. Viets;M. Wade;A. Urban;S. Kandhasamy;J. Betzwieser;Duncan A. Brown;J. Burguet-Castell;C. Cahillane;E. Goetz;K. Izumi;S. Karki;J. Kissel;G. Mendell;R. Savage;X. Siemens;D. Tuyenbayev;A. Weinstein
高级LIGO的原始探测器输出需要校准以计算无因次应变h(t)。校准后的应变数据在时域内产生，同时使用低延迟在线程序和高延迟离线程序。低延迟的h(t)数据流分两个阶段产生，第一个阶段在操作探测器反馈控制系统的同一台计算机上执行。这一阶段称为前端校准，使用无限脉冲响应（IIR）滤波，并以16 384 Hz的数字采样率执行所有操作。由于一些限制，该程序目前在校准的应变数据中引入了某些系统误差，从而激发了低延迟程序的第二阶段，即低延迟gstlal校准管道。gstlal校准管道使用有限脉冲响应（FIR）滤波对前端校准的输出应用校正。它对校准应变的传感和驱动元件应用时变校正因子，以减少系统误差。gstlal校准管道也用于高延迟时重新校准数据，这是必要的，主要是由于校准数据中的在线辍学和对校准模型或滤波器的识别改进。
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-latency gstlal calibration pipeline. The gstlal 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. The gstlal 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.