3 may 2011 vesf da schoold. verkindt 1 didier verkindt virgo-lapp cnrs - université de savoie vesf...
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3 May 2011 VESF DA school D. Verkindt 1
Didier Verkindt
Virgo-LAPPCNRS - Université de Savoie
VESF Data Analysis School
Data Quality and vetos
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Outline
Why data analysis needs vetos
Some preliminary definitions
A simple data analysis tool: frequency band rms
Examples of glitches
Some useful tools
How to develop a quality flag
Performance of a DQ flag
Example of DQ flag used as veto
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Why data analysis needs vetos
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The Virgo interferometer is sensitive to several sources of noise: Most of them are non-stationary with various time scales and couplings short glitches or long transient noisy periods
• Seismic noise coupled to diffused light• Magnetic noise, powerline• Injection system• TCS system• OMC alignment• Etc…
Why data analysis needs vetos
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The Virgo interferometer is sensitive to several sources of noise: Most of them are non-stationary with various time scales and couplings short glitches or long transient noisy periods
• Seismic noise coupled to diffused light• Magnetic noise, powerline• Injection system• TCS system• OMC alignment• Etc…
Why data analysis needs vetos
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One interferometer like Virgo: High glitchiness, large SNR distribution tail difficulty to separate real GW events from noise background
Why data analysis needs vetos
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Several interferometers in coincidence (or in coherence): Lower glitchiness, but still highly non-gaussian SNR distribution tail Reduced statistical significance of GW events versus background
Why data analysis needs vetos
• freq < 200 Hz• freq > 200 Hz
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Several interferometers in coincidence (or in coherence): Apply vetos in each detector’s data set reduce the distribution tail improve statistical significance of remaining loud events
Why data analysis needs vetos
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Some preliminary definitions
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Some preliminary definitions• GPS timeAtomic time counted in seconds from January 1st 1980 00:00:00 UTCExample: 931220000 = July 10th 2009 00:13:05 UTC
• Dark fringe / GW channelThe channel which contains the GW signal (Pr_B1_Acp or h_16384Hz)
• Auxiliary channelAny channel recorded which contains information about ITF environment or ITF control
• SNR (Signal to Noise Ratio)Often defined by the power in the signal divided by the power in the noise background
• TriggerEvent in the analyzed signal that fulfill the criteria of the data analysis pipeline
• GlitchesTransient events in the analyzed signal whose origin is some noise source in the ITF
• VetoConditions applied to reject data or data analysis triggers
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Some preliminary definitions• GPS timeAtomic time counted in seconds from January 1st 1980 00:00:00 UTCExample: 931220000 = July 10th 2009 00:13:05 UTC
• Dark fringe / GW channelThe channel which contains the GW signal (Pr_B1_Acp or h_16384Hz)
• Auxiliary channelAny channel recorded which contains information about ITF environment or ITF control
• SNR (Signal to Noise Ratio)Often defined by the power in the signal divided by the power in the noise background
• TriggerEvent in the analyzed signal that fulfill the criteria of the data analysis pipeline
• GlitchesTransient events in the analyzed signal whose origin is some noise source in the ITF
• VetoConditions applied to reject data or data analysis triggers
• The environment channels• AC* : acoustic channels• SE* : seismic channels• MA* : magnetometers channels
• The ITF channels• Gx* : beam monitoring channels• Bs* : injection system channels (Beam source)• Sc_IB* : Suspension control of Injection Bench• Sc_NE* : Suspension control of North End mirror
• The detection photodiodes channels• Pr_B1* : Photodiodes signals for the B1 beam (output port after output MC)• Pr_B5* : Photodiodes signals for the B1 beam (output port at BS reflective face)• Pr_B7* : Photodiodes signals for the B1 beam (output port at North End)• Pr_B8* : Photodiodes signals for the B1 beam (output port at West End)
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Some preliminary definitions• GPS timeAtomic time counted in seconds from January 1st 1980 00:00:00 UTCExample: 931220000 = July 10th 2009 00:13:05 UTC
• Dark fringe / GW channelThe channel which contains the GW signal (Pr_B1_Acp or h_16384Hz)
• Auxiliary channelAny channel recorded which contains information about ITF environment or ITF control
• SNR (Signal to Noise Ratio)Often defined by the power in the signal divided by the power in the noise background
• TriggerEvent in the analyzed signal that fulfill the criteria of the data analysis pipeline
• GlitchesTransient events in the analyzed signal whose origin is some noise source in the ITF
• VetoConditions applied to reject data or data analysis triggers
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Some preliminary definitions
• Science mode segmentsTime periods where we consider the detector in good state for science data taking.
• DQ quality flag segmentTime periods where we consider that detector in science mode may be affected by some transient noise source
• PaddingTime extensions to be applied before and after each DQ flag segment.
• Dead-timePercentage of science mode time vetoed because of a DQ flag
• EfficiencyPercentage of data analysis triggers vetoed by a DQ flag
• Use percentagePercentage of veto segments which covered at least one data analysis trigger
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Some preliminary definitions
• Science mode segmentsTime periods where we consider the detector in good state for science data taking.
• DQ quality flag segmentTime periods where we consider that detector in science mode may be affected by some transient noise source
• PaddingTime extensions to be applied before and after each DQ flag segment.
• Dead-timePercentage of science mode time vetoed because of a DQ flag
• EfficiencyPercentage of data analysis triggers vetoed by a DQ flag
• Use percentagePercentage of veto segments which covered at least one data analysis trigger
All Omega triggers with SNR>8Omega triggers with SNR>8within science mode segments
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Some useful toolsfor visualization and investigation
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Some useful tools: wscan
The wscans: for each loud Omega trigger, look for coincident glitches in a predefined list of auxiliary channels and show associated time-frequency plots
See https://wwwcascina.virgo.infn.it/DataAnalysis/Burst/wscan/V1/2007/08/
Bs_IMC_DR_AC1Pr_B1_ACp
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Some useful tools: wscan
The wscans gives also visual indication about glitch families
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Some useful tools: spectrogramsDaily spectrograms gives also indications on the origin of some glitches
See http://wwwcascina.virgo.infn.it/MonitoringWeb/Spectro
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Some useful tools: dataDisplaydataDisplay allows you to visualize the frame formated data(vesf_rds.ffl, trend.ffl or brmsmon.ffl)And to see various channels in time or frequency domainType dataDisplay
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Examples of glitches
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• Some loud glitches periods well localized in time• Many low SNR glitches associated to non-stationary frequency lines
Example of glitches
Local time (day/month)
Fre
qu
en
cy
(H
z)
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Example of glitches
Local time (day/month)
Fre
qu
enc
y (H
z)
And more various types of glitches…
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Some glitches vetoed by seismic DQ flags
Example of glitches
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Some glitches on which you will work
Example of glitches
Local time (day/month)
Fre
qu
en
cy
(H
z)
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DQ flag production
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How is made a veto: general scheme
Dark fringe signalPr_B1_ACp
Omega data analysis pipeline
Omega triggers
Auxiliary channelEm_SEBDNE01
Brms monitor
Data Quality segments
Coincidence
Keep only Omega triggers which are out
of DQ segments
Auxiliary channelEm_SEBDNE01
KW pipeline
KW triggers
Or Keep only Omega triggers which are coincident with KW triggers of some well chosen aux. channels
Two examples: Brms monitor and KW pipeline
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A simple data analysis tool: Frequency band rms
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• Compute FFT of a signal
• Compute bandrms = FFT2 integrated from fmin to fmax
• Compare bandrms to a threshold (fixed or adaptative)
Frequency band RMS
Bandrms16-64 Hz
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• Compute FFT of a signal
• Compute bandrms = FFT2 integrated from fmin to fmax
• Compare bandrms to a threshold (fixed or adaptative)
• Do this in a sliding time window
Brmsmon algorithm
Bandrms16-64 Hz
0.23 4.77 1.03 0.07
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How to develop a DQ flag
• Using the various tools previously presented, you choose some auxiliary channels and frequency bands useful for veto
• Now, you need to setup the parameters (frequency band and threshold) used by the brmsmon algorithm for each channel
• And to define what is the best way to use the results of each channel to define a DQ flag
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Keyword input file start GPS time duration FDIN_FILE /virgoData/ffl/vesf_rds.ffl 931220000 540000
Keyword list of input channels neededFDIN_TAG "Em_SEBDNE* Alp_Main_LOCK_STEP_STATUS Qc_Moni_ScienceMode“
Keyword output files nbr of frames per file channels to write in output fileFDOUT_FILE data/V-BRSMonRepro 10000 "V1:BRMSMon*"
Keyword flag name number of signals needed over thDQ_NAME SE_NE_16_256 2
Keyword channel name FFTlength fmin fmax th adaptative th time scaleBRMS_CHANNEL Em_SEBDNE01 1 16 256 8 1000BRMS_CHANNEL Em_SEBDNE02 1 16 256 8 1000BRMS_CHANNEL Em_SEBDNE03 1 16 256 8 1000
brmsmon algorithm
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How to develop a DQ flag
• You have produced the DQ flag values
• Now, you need to build the DQ segments (time periods where DQ flag is 1)
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Keyword input file start GPS time duration FDIN_FILE brmsmon.ffl 931220000 540000
Keyword list of input channels neededFDIN_TAG “BRMSMon*“
Keyword output dir period of segments file updateSEG_ASCII /users/vesfuser 30
Keyword flag name segments file name commentSEG_FLAG BRMSMon_SE_NE_16_256 V1:SE_NE_16_256 “seismic activity"
segonline algorithm
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DQ flag performances
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DQ flag performances
First information about a DQ flag: its flagging rateIt shows sometimes that quality flags are associated to well defined periods (for instance: seismic activity)
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DQ flag performances
For each DQ flag produced, we then need to know some figure of merits:
Deadtime: Does it veto too much data?
Efficiency: Does it veto a good fraction of the glitches?
Use percentage: Does it uses more segments than what would be useful?
Eff/deadtime: Does it veto suitably or randomly?(low use percentage and efficiency/deadtime ~ 1 means that it vetoes randomly)
DQ segment Science mode segment Omega trigger
Efficiency=8/16=50%Use percentage=6/9=66%Deadtime=20s/90s=22%Eff./deadtime=50/22=2.3
100 s0 s
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DQ flag performances
Padding of DQ segments: • According to the type of data analysis triggers you want submit to veto• Or according to the type of glitches you expect to veto
• You need to search for an optimal padding of the DQ flag segments
• The padding is an extension of each DQ segment.
• For instance padding [-3,+2] applied to segment 931200010 931200015 produces segment 931200007 931200017
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DQ flag performances
One DQ flag result: • Several loud triggers removed• Good use percentage• Low deadtime
Efficiency Use Percentage Eff/deadtimeSNR > 5 45/2736874=0.00164421% 11/12=91.6667% 1.02367SNR > 8 8/68389 =0.0116978% 1/12=8.33333% 7.28293SNR > 15 2/1058 =0.189036% 0/12=0% 117.692SNR > 30 0/124 =0% 0/12=0% 0Deadtime: 21/1307440 = 0.00160619%
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DQ flag performances
Combine DQ flags: • You can then choose the DQ flags of interest for your data analysis triggers and apply a OR of them.
• Resulting deadtime must not be too high (<10%)
• Resulting efficiency for loud triggers (SNR>15) must be above 50%
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VSR3 DQ flags performances
Efficiency Use percentage Efficiency/deadtimeSNR> 5 13.793 % 91.55 % 1.56SNR> 8 27.163 % 40.22 % 3.08SNR> 15 70.745 % 10.93 % 8.01SNR> 30 92.720 % 4.64 % 10.50Deadtime: 8.833 %
Example of combined DQ flags performances
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Last words
• Other types of vetoes: • Event y event vetoes (like KW vetoes)• Signal based vetoes (like waveform consistency check)• Followup tests (check of detector status, check event’s pattern, coherence with aux. channels, etc…)
•The veto developments are a long-term (never-ending) activity
• It needs strong updates each time the interferometer’s configuration is changed.
• Vetos are not of marginal use even for coincidence/coherent analyses