1. Detector Characterisation and Optimisation David Robertson University of Glasgow

2. Jan. 2001David Robertson2 Detector Diagnostics – Experimentalists View Using characterisation tools to improve detector performance main aim – identify then eliminate or reduce: spurious signals losses of lock impulsive events other noise coupling e.g. seismic, acoustic,... variations in performance e.g. variation of sensitivity with time where that fails produce suitable veto signals where that fails - candidate events

3. Jan. 2001David Robertson3 Detector Diagnostics – Experimentalists View I would like to be able to do: simple analysis of the type now done with oscilloscopes, FFT analysers etc. enhanced by the availability of long time series, flexible computing power to do filtering, matching Examples of: time domain analysis frequency domain analysis what else can you suggest?

4. Jan. 2001David Robertson4 Example Event – Time Domain Impulse “signal” in GW data stream trigger on the “signal” examine several other data streams simultaneously see strong correlation with “EOM feedback” signal could use this as a good veto signal But impulse occurs when mirror feedback saturates as a result of long term drift improve mirror control system to reduce the problem by either: improving the mirror control loop force a relock of the detector GW data Mirror feedback EOM feedback Mirror feedback Time

5. Jan. 2001David Robertson5 Time Domain - Impulsive Events Impulsive events range from loss of lock to small “glitches” in the data - timescales of 10 -3 – 1 seconds e.g. impulsive event in “GW” data stream trigger on this event different trigger types threshold crossing matched filter prefilter data – only look in certain frequency band e.g. excitation level of suspension wire resonance - bandwidth <1 Hz compare other data streams at the same time DAQ data and control system data zoom timescale and amplitude in and out GW data Mirror feedback EOM feedback Mirror feedback Time

6. Jan. 2001David Robertson6 Time Domain - Slow Changes Slow variation in the detector noise level - timescales of 0.1 seconds - many hours e.g. rms GW data stream, 50-150 Hz compare with other data streams e.g. mirror alignment rms GW data Mirror tilt compare time and number of impulsive GW events with other data streams e.g. laser temperature Time Impulsive “GW” events Laser temperature Time

7. Jan. 2001David Robertson7 Frequency Domain - Example Start with 2 data streams, GW data and seismic noise. Want to look at how seismic noise influences GW data, want to know: power spectra how much signal power is common to both data streams cross-spectrum, coherence? transfer function from seismic noise to GW data (amplitude and phase) zoom frequency band and amplitude in and out Common power (%) Transfer function Frequency GW Seismic

8. Jan. 2001David Robertson8 Frequency Domain For all of the previous measurements, how do they change with time overlay plots How to take advantage of applied excitation signals e.g. swept sine Other analysis tools? nonlinear coupling e.g. seismic noise and laser power noise to GW data ? Frequency GW