LIGO-G M LIGO I Science Run Barry Barish PAC Meeting - LHO December 13, 2000

LIGO-G M 2 The LIGO I Science Run Data & Computing Group Operations Plan 9 th Meeting of the LIGO PAC LIGO Livingston Observatory Livingston, Louisiana 13 December 2000 Albert Lazzarini LIGO Laboratory Caltech

LIGO-G M 3 LIGO Plans schedule 1996Construction Underway (mostly civil) 1997Facility Construction (vacuum system) 1998Interferometer Construction (complete facilities) 1999Construction Complete (interferometers in vacuum) 2000Detector Installation (commissioning subsystems) 2001 Commission Interferometers (first coincidences) 2002Sensitivity studies (initiate LIGO I Science Run) LIGO I data run (one year integrated data at h ~ ) 2005+Begin ‘advanced’ LIGO installation

LIGO-G M 4 Revised Schedule As proposed to the NSF – May 2000

LIGO-G M 5 Significant Events

LIGO-G M 6 operating as a Michelson with Fabry-Perot arms reduced input laser power (about 100 mW) without recycling noise level is a factor of above the final specification sources of excess noise are under investigation Strain Sensitivity Nov km Hanford Interferometer

LIGO-G M 7 LIGO I steps to science run  commissioning interferometer »robust locking »three interferometers »sensitivity »duty cycle  interleave engineering runs »implement and test acquisition and analysis tools »characterization and diagnostics studies »reduced data sets »merging data streams »upper limits

LIGO-G M 8 LIGO/LSC Data Analysis Model  Now: »Initial engineering runs starting to set the stage for how science, research is done »Data being archived at Caltech in HPSS »Access from archive according to LIGO Laboratory MOUs »“Stress testing” of software and hardware systems - both LDAS and GDS/DAQS/CDS »Initial data analyses focus on –sorting out commissioning issues, –understanding environment, –Calibrations, data conditioning, pre-processing

LIGO-G M 9 LIGO/LSC Data Analysis Model  Near-term (2Q2001): »LIGO science will focus on using engineering runs to extract meaningful first upper limits »Organized around 4 upper limits papers using ~1 week of data in 2Q2001 »Opportunity to set current best upper limits on these classes of sources »Provides a basis to "exercise" the LSC data analysis groups »Provides a basis for future organization of the LIGO I Science Run search teams –groups will expand as interest grows in LIGO science.  Problems: »LDAS readiness to support the engineering run goal »Strategy is to limit scope primarily to LDAS supported goals

LIGO-G M 10 Astrophysical Signatures data analysis  Compact binary inspiral: “chirps” »NS-NS waveforms are well described »BH-BH need better waveforms »search technique: matched templates  Supernovae / GRBs: “bursts” »burst search algorithms – eg. excess power; time-frequency patterns »burst signals in coincidence with signals in electromagnetic radiation »prompt alarm (~ one hour) with neutrino detectors  Pulsars in our galaxy: “periodic” »search for observed neutron stars (frequency, doppler shift) »all sky search (computing challenge) »r-modes  Cosmological Signals“stochastic background”

LIGO-G M 11 LIGO/LSC Data Analysis Model  LIGO I Science Run (2Q2002): »Key astrophysical searches follow the LSC Data Analysis White Paper plan: »Organized around teams, as in near-term upper limit studies –Open to all who are willing to work »LIGO Lab LDAS resources to be used for searches will be shared among the teams »LSC member institutional resources used by individual researchers »Longer term: establish 5 LIGO/LSC Tier 2 centers (“University Research Centers” or URCs) to provide additional computational, data distribution resources across collaboration

LIGO-G M 12 Inspiral Sources LSC Upper Limit Group Inspiral Sources Co-chair Patrick Brady, Gabriela Gonzalez Bruce Sukanta Douglas Patrick Duncan Jordan Nelson Jolien S.V. Gabriela Andri Gregg Syd Tom David B.S. Peter

LIGO-G M 13 Interferometers astrophysical sources Compact binary mergers Sensitivity to coalescing binaries Binary inspiral ‘chirp’ signal future

LIGO-G M 14 Interferometer Data 40 m Real interferometer data is UGLY!!! (Gliches - known and unknown) LOCKING RINGING NORMAL ROCKING

LIGO-G M 15 The Problem How much does real data degrade complicate the data analysis and degrade the sensitivity ?? Test with real data by setting an upper limit on galactic neutron star inspiral rate using 40 m data

LIGO-G M 16 “Clean up” data stream Effect of removing sinusoidal artifacts using multi-taper methods Non stationary noise Non gaussian tails

LIGO-G M 17 Inspiral ‘Chirp’ Signal Template Waveforms “matched filtering” 687 filters 44.8 hrs of data 39.9 hrs arms locked 25.0 hrs good data sensitivity to our galaxy h ~ mHz -1/2 expected rate ~10 -6 /yr

LIGO-G M 18 Detection Efficiency Simulated inspiral events provide end to end test of analysis and simulation code for reconstruction efficiency Errors in distance measurements from presence of noise are consistent with SNR fluctuations

LIGO-G M 19 Setting a limit Upper limit on event rate can be determined from SNR of ‘loudest’ event Limit on rate: R < 0.5/hour with 90% CL  = 0.33 = detection efficiency An ideal detector would set a limit: R < 0.16/hour

LIGO-G M 20  Two Sites - Three Interferometers »Single Interferometernon-gaussian level ~50/hr »Hanford (Doubles) correlated rate (x1000) ~1/day »Hanford + Livingston uncorrelated (x5000) <0.1/yr Coincidences between LLO & LHO

LIGO-G M 21 Burst Souces LSC Upper Limit Group Burst Sources Co-chair Sam Finn, Peter Saulson Warren Barry Biplab Jim Eric Kent Ed Ronald Sam Ray Ken Joe Gabriela Andri Bill Warren Masahiro Ito S. Al Szabi Genakh Soumya Benoit Soma Fred Ravha Rahkola Peter

LIGO-G M 22 pulsar proper motions Velocities -  young SNR(pulsars?)  > 500 km/sec Supernovae asymmetric collapse?

LIGO-G M 23 LIGO I science run  Strategy »initiate science run when good coincidence data can be reliably taken and straightforward sensitivity improvements have been implemented (~ 7/02) »Then, interleave periods of science running with periods of sensitivity improvements  Goals »obtain 1 year of integrated data at h ~ »searches in coincidence with astronomical observations (eg. supernovae, gamma ray bursts) »searches for known sources (eg. neutron stars) »stand alone searches for compact binary coalescence, periodic sources, burst sources, stochastic background and unknown sources at h ~ sensitivities  Exploit science at h ~ before initiating ‘advanced’ LIGO upgrades

LIGO-G M 24 LIGO/LSC Data Analysis Model  Throughout Engineering & Science Runs, the Laboratory’s Data & Computing Group fulfills the following roles: »LIGO science, data analysis: scientific staff are actively engaged in the astrophysics searches »Simulation & Modeling: detector support, data analysis »Continuous management and movement of large volumes of data »Maintaining pipeline analyses running, archive running »Software maintenance/improvements/enhancements »LSC support, visitors »LIGO Laboratory-wide IT support

LIGO-G M 25 Data & Computing Group Principal LDAS activities during operations

LIGO-G M 26 Data & Computing Group Principal Modeling & Simulation activities during operations

LIGO-G M 27 Data & Computing Group Principal General Computing activities during operations

LIGO-G M 28 LDAS Operations Statistics derived from actual experience

LIGO-G M 29 * MIT, LHO, and LLO have local General Computing staff * LHO, and LLO have local LDAS staff

LIGO-G M 30 Data and Computing Budget Breakdown

LIGO-G M 31 LDAS Operations Budget Hardware Support

LIGO-G M 32 Requested Increment - Operations

LIGO-G M 33 Conclusions science run  Short term -- »implement LDAS –4 sites; computing; archiving »engineering runs –data handling and access, reduced data sets –diagnostics; characterize instrument and data –algorithms; statistics  Longer Term »LIGO Lab support for Science Run  Support Required »LDAS procurement and implementation »incremental resources requested –manpower –maintenance and networking –support of LSC