S.Klimenko, LSC, Marcht 2005, G050192-00-Z BurstMon diagnostic of detector noise during S4 run S.Klimenko University of Florida l burstMon FOMs l S4 run.

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Presentation transcript:

S.Klimenko, LSC, Marcht 2005, G Z BurstMon diagnostic of detector noise during S4 run S.Klimenko University of Florida l burstMon FOMs l S4 run results l summary & plans

S.Klimenko, LSC, Marcht 2005, G Z burstMon run for S4 l Single detector burst monitor l Run 6 burstMon jobs  2 jobs per each detector 2 bands (<1kHz & <2kHz), time stride 1 minute l FOMs are available in real time in the control rooms  All information is recorded into trend frames  also daily summary web page: (thanks to L.Cadonati) l express detector performance in few burst FOMs  detector sensitivity: detected efficiency  noise non-stationarity & non-Gaussianity  Loudest trigger statistics

S.Klimenko, LSC, Marcht 2005, G Z l FOM: detected hrss amplitude of injections at 50% of efficiency and 1 Hz false alarm rate. l Method: real time injection of simulated bursts and their detection with WaveBurst-like algorithm l Injections during S4 run:  waveforms: SG100Q9, SG235Q9, SG555Q9  Uses Patrick’s EZcalibrate to get hrss in strain/sqrt(Hz)  200 injections/stride/waveform  repeat analysis 600 times  do injections only for 1kHz burstMon jobs

S.Klimenko, LSC, Marcht 2005, G Z noise

S.Klimenko, LSC, Marcht 2005, G Z Pixel rate/fraction l Select “black” pixels (1%), reconstruct clusters  Stationary G-noise  few pixels in a cluster  high cluster rate  Non-stationary noise  “fat” clusters  low cluster rate  Cluster rate is not a good noise characteristic at low threshold ! l Instead of clusters, look at rate of pixels (TF volume units).  select “fat” clusters (size>N)  rate of pixels in fat clusters  or pixel fraction: Large p-fraction – indication of “hot”/glitchy detector noise expected for “perfect” noise Fraction of glitchy TF volume

S.Klimenko, LSC, Marcht 2005, G Z Pixel rate/fraction FOM

S.Klimenko, LSC, Marcht 2005, G Z L1 pixel fraction before/after RFO fix Mar 12 Feb 27 2kH band

S.Klimenko, LSC, Marcht 2005, G Z H1 pixel fraction (1kHz) Mar 3 Mar 13 SNR TCS instability

S.Klimenko, LSC, Marcht 2005, G Z H2 pixel fraction (2kHz) Mar 10 Feb 27

S.Klimenko, LSC, Marcht 2005, G Z Most Significant Trigger (MST) l Select most significant trigger for each stride (1 min) l Shows dominant source of glitches l Save in trend files  Central frequency  Central time  SNR l Maybe need to look at the second significant trigger as well

S.Klimenko, LSC, Marcht 2005, G Z L1 Dominant noise source frequency after before

S.Klimenko, LSC, Marcht 2005, G Z H2 Dominant Noise Source frequency 1 KHz 2 KHz

S.Klimenko, LSC, Marcht 2005, G Z Dominant SNR (March 1) H2 2 KHz BM threshold

S.Klimenko, LSC, Marcht 2005, G Z L1 trends for March BM is pointing to a problem but does not tell what is a problem

S.Klimenko, LSC, Marcht 2005, G Z noise variabilty Amplitude Noise RMS l Variability - rms of normalized amplitudes calculated for short time intervals S2(L1): time interval 1/16 sec High noise variability main problem for burst S3 analysis

S.Klimenko, LSC, Marcht 2005, G Z S3/S4 Variability S3 data, H1 L1 S3 H1 S3 H2 S3 S4 DQ flags should clean up the tail

S.Klimenko, LSC, Marcht 2005, G Z S4 variability vs time L1 H1 H2 L1 fix

S.Klimenko, LSC, Marcht 2005, G Z Conclusion & Plans l Trouble-free operation during S4 run l Seems to be useful…. l Plans  Add MST hrss  Output MST reconstructed waveform (-0.5sec,0.5sec) to dmt-viewer  Update documentation  Run burstMon job for 0-256Hz band to look more closely at low frequency noise.  with few minor improvements intend to use for S5