GWDAW11, Postdam 18th-21th December 2006 E. Cuoco, on behalf of Virgo collaboration 1 “Data...

GWDAW11, Postdam 18th-21th December 2006

E. Cuoco, on behalf of Virgo collaboration

1

“Data quality studies for burst analysis of Virgo data acquired during Weekly

Science Runs “

E.Cuoco, EGO

on behalf of Virgo Collaboration



2

WSR goals

To ease the transition between commissioning to data taking 2.5 days during week-end, roughly one per month

Provide data sets taken in stationary condition for :Commissioning studiesSearch analysis (online, offline and noise studies)

WSR1 08/09-11/09 2006 : duty cycle 87.7%WSR2 22/09-25/09 2006 : duty cycle 71.2% WSR3 06/10-09/10 2006 (failed)WSR4 13/10-16/10 2006 (failed)WSR5 10/11-13/11 2006 : duty cycle 64.2%WSR6 01/12-04/12 2006 : duty cycle 80.5%

best sensitivity reached (4 Mpc NS-NS horizon for an optimally oriented source, SNR=8)



3

Goal of this study

Study the “quality” of the Virgo dataDetect all kinds of glitches in the data which may spoil a burst search (loudest events study)

Understand the origin of these glitches, when possible …

Study the stationarity of the burst trigger rate

Study possible vetoes strategy to suppress loud triggers

Set up “tools” and provide information for the burst searches (DataQuality flags and vetoes)



4

DataQuality & Vetoes

• DQ flags: list of periods during which the ITF is badly functioning (photodiodes saturation, no calibration, noisy Second Stage Frequency Stabilization loop (SSFS), DAQ problem…). • These periods can last from 1 second up to several hours. • They are applied a priori or a posteriori on the triggers list produced

by a burst pipeline.• For the moment they concern only “obvious” problems but may

concern in the future environmental condition (weather, …) if useful for the burst search

• Vetoes: list of short periods during which a fake burst signal could be visible in the Dark Fringe channel due to any cause

except a genuine GW. • They are applied on triggers’ lists.



5

Virgo burst pipelines

Time domainMean Filter (MF)

Excess of Mean

Estimation on different

window size

Time -FrequencyPower Filter (PF)

Wavelet Detection Filter (WDF)

CorrelatorPeak Correlator (PC)

Exponential Gaussian Correlator (EGC)

Time-Frequency map

with different window

Wavelet TransformCoefficient thresholdEnergy estimation

Wiener filtering with gaussian peak templates

Wiener filtering with exponential gaussian

templates

Wor

k on

whi

tene

d

data



6

Data Quality flags study

Few problems identified so far:Dark fringe outport photodiodes saturation

NE/WE/BS/PR mirror coil driver saturation

Second Stage Frequency Stabilization (SSFS) correction saturation

SSFS electronics problem

Timing problems (DAQ) (WSR5)

Photodiode shutter opening (WSR2)

Data quality segments given by the h reconstruction processingWhite noise injection segments

Bad quality when the lines are not high enough

It has been checked that all these problems create loud events in the dark fringe

Definition of Data Quality segments to flag these periods

Most of these flags can be applied a posteriori on trigger lists



7

DQ segments application on MF triggers: WSR1

WSR1

photodiode saturation

SSFS saturation



8

DQ segments application on MF triggers:WSR5

WSR5

timing

SSFS saturation



9

DQ segments application on MF triggers:WSR6

Coil drivers saturation

WSR6

The loudest events are suppressedby using these DQ flags



10

Loudest glitches study

We found only a few categories in WSR data:Oscillations in control loops (longitudinal and angular dof):

“z” events (low frequency events <100 Hz)

Noise increase on the full bandwidth due to laser frequency noise coupling temporary increase (duration <few seconds):

that generates some “Burst of Burst” events (“BOB” events)In C6/C7 data set, the origin of the laser frequency noise coupling

increase has been identified to be due to the residual angular motions of the mirrors which were too loose

SSFS electronics saturation events

Study of environmental channels (seismometers, magnetometers, acoustic probes …)

We did not find so far any loud glitches in the dark fringe due to environmental noise …



11

Events in the control signals



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Example of a BOB event

• long duration events (up to few seconds),• large frequency band content• due to a coupling of the laser frequency noise and angular motion of the mirror



13

Example of a problem with the SSFS

2 seconds 2 seconds

Exclude 1 second before and 1 second after the event in the SSFS channel

Dark fringe



14

Veto for the SSFS saturation events WDF SNR of the triggers obtained

on the channel which “monitors” the SSFS



15

SSFS channel as Veto

SNR>85, Dt=0.1 More details in M.Delprete poster



16

Looking at the signal used to control the Power Recycling

cavity lengthz-events

Snr>30 ,Dt=0.1



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WSR5:Dark fringe WDF events distribution



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WSR5: Cleaning the Dark fringe

For SSFSSNR >85Dt=0.1

For z-events,SNR>30Dt=0.1



19

Enviromental channels: Magnetometers in North End

towerGlitches every 3-4secs



20

Glitches inWE-NE magnetometers



21

Lightning?

Dark fringe



22

Coalescing binaries horizon and seismic noise

Horizon fits quite well an empirical formula: 2.7 (4.3) Mpc – (wind+sea). “wind” is the North End tower top stage motion in the region 30-100 mHz, “sea” is the motion in the region 100 mHz-1 Hz

WSR1

WSR6



23

Burst trigger rate and weather condition ?

Study of the burst trigger rate as function of time (rate averaged on 10 minutes)

Horizon seems to follow the low frequency (<1Hz) seismic activity seen by the top of the suspensions

Comparison with the Common Mode Rejection Ratio which gives the coupling between the laser frequency noise and the dark fringe



24

Burst trigger rate and weather condition: WSR1

•The trigger rate follows the seismic noise <1Hz

•The trigger rate follows the evolution of the CMRR



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•The trigger rate follows the seismic noise (<1Hz)

•The trigger rate does notfollows the evolution of the CMRR



27

Conclusions

WSR data sets have been analyzed offline using glitches finders algorithms to provide useful information for the burst searches

Identification of all problems generating huge glitches DataQuality flags defined to suppress a posteriori these periods

Identification of the loudest remaining glitches. A few categories have been identified

Setup of veto strategy for the identified sources of glitches

We found correlation between the transient trigger rate and the weather condition



28

Spare slides



29

Horizon vs weather conditionSeismic noise: quite quiet during the week-end!



30

A bob event

Fre

quen

cy (

Hz)

Dar

k fr

inge

(w

hite

n)

Time (s)



31

BOB mechanism

Proposed Mechanism

• at high frequency, frequency noise dominates• Pr_B1_ACp = * related to Common Mode Rejection Ratio

varies with time by a significant amount (up to a factor 10) variations are mainly driven by angles



32

Dark fringe noise increase as function as the North End mirror θy angle

NE_ty

-2 -1 0 1 2

1.6

1.5

1.4

1.3

1.2

1.

1.1

0.9

0.8

0.7

x 10-8

Pr_B

1_A

Cp

RM

S

C7

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GWDAW11, Postdam 18th-21th December 2006 E. Cuoco, on behalf of Virgo collaboration 1 “Data...

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