LIGO Data Quality Monitoring Keith Riles University of Michigan

2 LIGO-G0600XX-00-Z Outline * Online data quality monitoring  Figures of merit  Real-time DQ flagging and DQ database Offline data quality monitoring  Teams / working groups  Offline DQ flagging  DQ infrastructure Use of DQ information in astrophysical analysis * Thanks to John Zweizig for some borrowed slides

3 LIGO-G0600XX-00-Z Figures of Merit – Spectra Control room wall (updates ~1/second) S5 web page

4 LIGO-G0600XX-00-Z Figures of Merit – DMT Most of the control room figures of merit for performance are produced by Data Monitoring Tool (DMT) programs running 24/7 in background With infrastructure (and many monitoring programs) provided by John Zweizig (Caltech), the DMT environment allows many different LSC scientists to contribute to online data quality monitoring Supports nearly real-time strip charts, spectra, and histograms (DMT viewer), archiving of second and minute trends (visible with the standard Data Viewer), triggers for the database, control room alarms, and web display of status and plots. Figures of merit and other status/performance measures monitored by operators (24/7) and scientific monitors (scimons – 20/7)

5 LIGO-G0600XX-00-Z Figures of Merit – DMT

6 LIGO-G0600XX-00-Z Figures of Merit – DMT aaaaa Seismic band-limited RMS noise Anthropogenic noise (car traffic) Blue spikes from trucks

7 LIGO-G0600XX-00-Z Figures of Merit – DMT State Vector: 0 -Down 1 -Mode cleaner locked 2 -Arms locked 3 -Full power 4 -Science mode

8 LIGO-G0600XX-00-Z Figures of Merit – DMT Binary neutron star range (SNR=8, averaged over location/orientation) Livingston 4-km (L1) Hanford 4-km (H1) Hanford 2-km (H2)

9 LIGO-G0600XX-00-Z Figures of Merit – DMT Calibration line strengths and unity gain frequencies

10 LIGO-G0600XX-00-Z Figures of Merit – DMT (FOM2 at LLO) Glitch rates (4σ, 6σ) Band-limited RMS Pulsar injection excitation Histogram (high- passed) Days to reach Crab pulsar spindown limit

11 LIGO-G0600XX-00-Z Figures of Merit – DMT (FOM3 at LHO) “Pixel fraction” (Canary) h rss at 50% efficiency (sine- Gaussians) Binary inspiral trigger SNR Stochastic Ω sensitivity (1 minute)

12 LIGO-G0600XX-00-Z Figures of Merit – DMT (Strain RMS for L1) Low freqs Mid freqs II Mid freqs I High freqs

13 LIGO-G0600XX-00-Z Figures of Merit – Online Flagging These online figures of merit allow rapid identification of problems (if operators & scimons are attentive) They also allow early identification of time intervals to be flagged as having questionable data quality  DQ Flags (E-Log) Handful of S5 flags are automatically inserted in the database: (John Zweizig) ASI_CORR_OVERFLOW Injection H1_Not_Locked PD_Overflow H2_Not_Locked Wind_Over_30MPH

14 LIGO-G0600XX-00-Z The S5 Web Page The S5 web page: serves as a central ‘clearinghouse’ for run-related information: Links to web-accessible real-time figures of merit Links to archives of summary plots Scimon instructions and checklists Links to instructions for software tools and infrastructure Links to web pages of investigation teams Links to inspiral/burst triggers

15 LIGO-G0600XX-00-Z Figures of Merit – Online Flagging Other DMT monitors provide raw information for DQ flags to be defined offline Minute trends Triggers Recent aircraft passage at Hanford (microphone time/frequency trajectory of excess power) Fitted parameters from PlaneMon

16 LIGO-G0600XX-00-Z Offline DQ Flagging Many DQ issues require more investigation: Investigation Teams [  Committee/Working Groups as of mid 2006 ] Calibration stabilityInterchannel correlations GlitchesUpconversion Timing stabilityEnvironmental disturbances Hardware injectionsData reduction Data quality Astrophysical Search Groups (“bottom line effects”) BurstsContinuous-Wave InspiralsStochastic

17 LIGO-G0600XX-00-Z 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 *Slide from J. Zweizig’s Elba DQ talk

18 LIGO-G0600XX-00-Z DQ Flags Storage Summary information on DQ flags stored in segment “repositories”: More detailed information on many investigations:

19 LIGO-G0600XX-00-Z Offline Feedback to Control Room For ongoing data runs, operations can benefit from offline analysis First success: Glitch team feedback in S4 (and pre-S4 eng. run) For the S5 run, weekly S5 run coordination and detector characterization telecons have alerted commissioners and observatory scientists to problems to repair in weekly maintenance periods or 2- week commissioning periods. Rapid feedback has been especially useful from Glitch studiesLine-findingData quality studies Environmental/upconversion studies Burst and inspiral trigger studies

20 LIGO-G0600XX-00-Z Offline Feedback to Control Room Very strong effort from the Glitch Group: Large detchar team drawn from Burst and Inspiral Groups (Team led at different times in S5 by Laura Cadonati, Erik Katsavounidis, Alessandra Di Credico, Gaby Gonzalez, Shourov Chatterji) Formal “shifts” using remote access to online / offline diagnostics, including “Event Display” (Shantanu Desai) and “Q Scan” (Shourov Chatterji) Investigation of loud inspiral / burst triggers Weekly telecons (initially twice-weekly) and reports Archive of summary informationArchive of summary information and of daily/weekly/monthly reports (most recent example)(most recent example)

21 LIGO-G0600XX-00-Z Q-Scan Display (snapshot) * Whitened Spectrograms Whitened Time Series *Slide from J. Zweizig’s Elba DQ talk

22 LIGO-G0600XX-00-Z How are flags recorded / retrieved? Two recording schemes used to date: S2-S4: Manual flag insertion with segments/flags on disk S5: Automatic/manual flag insertion of segments/flags in IBM DB2 database (Duncan Brown) Two retrieval schemes used to date: S2-S4: segwizard and segments utilities (Peter Shawhan) S5: Ditto + LSCsegFind (Duncan)

23 LIGO-G0600XX-00-Z How are flags recorded / retrieved? Previous scheme: Aperiodic releases of new DQ versions all at once, with KR as common bottleneck (6 releases for S2, 6 for S3, and 10 for S4) New scheme: Flags have individual version numbers and can be updated at any time (KR still a bottleneck for manual updates, but threshold for making changes much reduced; Chris Messenger serving corresponding role for GEO) Handling of version numbers needs improvement (problems became apparent only recently)

24 LIGO-G0600XX-00-Z 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) *Slide from J. Zweizig’s Elba DQ talk

25 LIGO-G0600XX-00-Z Segment Database * Database interfaces  LSCSegFind: Command line database query  Text files –Available over web –Used by SegWizard and automated analysis pipelines  SegWizard GUI (or command line interface) –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 *Slide from J. Zweizig’s Elba DQ talk

26 LIGO-G0600XX-00-Z Data Quality Segment Types * (not exhaustive list) *Slide from J. Zweizig’s Elba DQ talk

27 LIGO-G0600XX-00-Z Use of Data Quality in Analyses * Segments defined with no guarantees  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. *Slide from J. Zweizig’s Elba DQ talk

28 LIGO-G0600XX-00-Z Remarks Data quality evaluation benefits from attacks on several fronts:  A priori investigation of generic “glitchiness” seen in online monitors  “Near-bottom-line” investigation of loud inspiral / burst triggers  Ditto for weaker triggers with time slides  Ditto for stochastic analysis correlations (identifying bad epochs)  Ditto for spectral lines found by pulsar analyses  But occasional surprises from “real bottom line” (infamous S2 plane) Participation by astrophysics search groups is critical in identifying the problems that hurt us most

29 LIGO-G0600XX-00-Z Remarks Instrumental expertise not necessary in order to identify problems  Commissioners and observatory scientists truly welcome targeted diagnostics where astrophysics sensitivity can be improved and there is a “meter” See example from last Friday’s Glitch Report

30 LIGO-G0600XX-00-Z Loud-glitch rate had been climbing – Now back to normal H1 glitch improvement after recent pre-MC fix (L. Cadonati) Days in S5

31 LIGO-G0600XX-00-Z Remarks As part of ongoing LSC reorganization, the LIGO detchar working groups are requesting official GEO liaisons:  Report on artifacts seen in GEO data and on useful GEO characterization methods  Report back to GEO analysts on LIGO problems and methods We can learn much from each other!