May 29, 2006 GWADW, Elba, May 27 - June 21 LIGO-G0200XX-00-M Data Quality Monitoring at LIGO John...

May 29, 2006 GWADW, Elba, May 27 - June 2 1LIGO-G0200XX-00-M

Data Quality Monitoring at LIGO

John ZweizigLIGO / Caltech


Good Data?

Before doing scientific analysis one must know how good are the data» Calibration stability (optical gain, etc.)» Interferometer state» Physical environment (seismic noise, wind, acoustic noise)» Control loop transients» Oops (you did What during science running?)» Astrophysical search sensitivities

Mechanism to do this finally set (after ~10 engineering runs, 4 science runs!)» 1.5-2 year run: Must keep up with data!» LSC in general and Detector Characterization (DetChar) group specifically

have dedicated many hours to concurrent understanding of data» Science monitor shifts, data quality investigations, DetChar group and

subgroup meetings.


How Does LSC Acquire and Use DQ Information?

On-line Data Monitoring» Constant automatic monitor of IFO state, sensitivity, calibration,

transients, environmental noise, etc.» Science monitors and operators note running conditions in eLog

Concurrent Data Quality Investigations» “Glitch group” has shifts, weekly meetings to run through noisiest

events» Calibration monitored, time variation parameterized.

Tabulate run epoch information » Define time segments that have specific (good or bad) properties.» Record segment in online database

Use in analysis» Analyse epochs determined to be “safe” for a particular analysis


Epochs or Vetoes?

In theory» Epochs used to handle exceptional conditions that are

– Long term several second to hours– Affect reliability or alter noise spectrum greatly– Disable analysis of data in time epoch.

» Vetoes used for transients (short term effects)– Analyse data, but reject any GW candidate.– Minimizes dead-time– Simplifies analysis job submission

In practice» Difficult to determine extent of effects (e.g. are signals really linear around

PD overflows?)» Epoch easier to use than vetoes (much better tools)» Most data quality flags used to define epochs (at discretion of analysis

groups)


Online Data Monitoring (DMT)

Real-time data monitoring software» Infrastructure & run support from LIGO Lab» Monitor code, configuration LSC/DC responsibility

Monitor environment/performance parameters, e.g.» Inspiral range» Lock State» Strain noise spectrum» Calibration Line sptrngts» Band-limited seismic noise

Display real-time results» Graphical output» html summary pages.


Online Monitoring (Graphics)


Real-Time Strain Noise Spectra


Online Monitoring (cont’d)

Record statistical quantities in “trend frames”» Machine readable record of performance/noise statistics

– 1.4 × 1.4 Msun binary NS inspiral range– Band-limited seismic noise

Record triggers» Transient noise

LSC Science Monitor (SciMon) Shifts » On shift 20 hours per day (two 10 hour shifts, every day)» Watch/summarize online data monitor display» Investigate source of any unusual noise» Several fall-back projects during smooth running.


Data Quality Investigations

DetChar group subdivided into teams. Investigate:» Calibration» Transients» Line features» Data quality

Example: Transients (Glitch) group:» Glitch shifts (1 person per week)

– Summarize electronic log notes– Summarize running conditions– Investigate loud single-IFO triggers from analysis pipelines

» Automatic displays of loudest triggers– Event display (S. Desai): Spectrograms of many channels– Q-Scan (S. Chatterji): Q-Transform, select channels with loud noise

clusters» Weekly discussion with DetChar Glitch group


Example: Calibration Line Errors

Calibration lines» Used to monitor IFO optical gain.» Inject three sinusoids (~50, ~550,

~1100Hz) into differential length control channel.

» Injected signals written to frames Several problem with injection

process discovered» Single sample drop-outs» 1-second dropouts» Repeated 1-second segments

Monitoring to detect future errors

» Calibrations notched out» 5σ excursions generate triggers» Trigger identified (offline script)

Segments produced to cover triggers


Q-Scan Display (snapshot)W

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Data Quality Segments

Segments:» Tag run periods with a given common property» Defined by automatically by DMT monitor or inserted manually from

tabulated segments. DB2 database contains:

» Segment data– Start, stop times– Type, Version– IFOs

» Provenance data– Program name, version– User ID


Segment Database

Database interfaces» LSCSegFind: Command line database query» Text files

– Available over web– Used by SegWizard and automated analysis pipelines

» SegWizard GUI– User selects single or multiple IFOs in science mode– Remove any combination of data quality segments (click on segment

name)– Prints a list of time ranges to be analysed

Example segment types» IFO states, e.g. Science or Injection mode» Environmental noise sources: Unusual seismic noise, High winds» IFO conditions: PD saturation, ADC overflows, Calib line dropouts


Segment type H1 H2 L1ASC_Overflow 1758(0.01) 1481(0.01) 102366(1.14)Calib_Bad_Coefs 4625(0.04) 2509(0.02) 5125(0.06)Calib_Dropouts 8586(0.08) 448(0.00) 165(0.00)Checksum 225773(1.91) 253924(1.93) -Hx_Not_locked 434268(3.67) 758595(5.70) -Injections 52033(0.44) 78584(0.60) 17087(0.19)Light_Dip (5%) 824(0.01) 24666(0.19) 27752(0.31)PD_Overflow 88156(0.75) 3174(0.02) 11165(0.12)Out_of_Lock 596(0.01) 699(0.01) 994(0.01)Pre_LockLoss (30m) aa16336(0.14) 17943(0.14) 24074(0.31iWind > 30MPH 11877(0.10) 16699(0.13) -AS_Trigger 0 0 2665(0.03)Bad_Sensing - - 21500(0.24)OSEM Glitch 0 112(0.00) 0All 824047(6.97) 1131062(8.58)

11.9Ms 13.2Ms 8.9Ms

Data Quality Segment Types


Use of Data Quality in Analyses

Segments defined with no guarentees» No guarantee of efficacy» Could cause some GW signals to self-veto

Analysis groups must» Decide which segments are appropriate» Test segment safety (does it veto loud injections?)» Decide whether to analyse data from segment, treat as a trigger veto

or ignore.


Summary

LIGO Detector Characterization group monitors data quality with online software and concurrent investigations

“Segments” define epochs of data with specific (good or bad) properties.

Analysis groups use run epochs as appropriate to their search

May 29, 2006 GWADW, Elba, May 27 - June 21 LIGO-G0200XX-00-M Data Quality Monitoring at LIGO John...

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