D. Buskulic, ACAT 2002, Moscow The VIRGO experiment Data analysis software tools used during Virgo engineering runs Review and future needs.

D. Buskulic, ACAT 2002, Moscow The Virgo experiment French-Italian Collaboration, 11 laboratories Situated at Cascina, near Pisa in Italy Arms length : 3 km Sensitivity frequency domain : 4 Hz - few kHz Best sensitivity: Hz -1/2 (at 500 Hz) Full Virgo commissioning starts beginning 2003 LAPP Annecy IPN Lyon OCA Nice LAL Orsay ESPCI Paris Firenze-Urbino Frascati Napoli Perugia Pisa Roma

D. Buskulic, ACAT 2002, Moscow f (Hz) h (Hz -1/2 ) Keeping really quiet Reach this sensitivity : lower all the noises Data sampled at 20 kHz Continuous stream of data

D. Buskulic, ACAT 2002, Moscow Fighting the noise Mechanical systems : pendulums and resonating systems Optical systems : Ultra-stable laser Electronic systems : Fighting all the noises !  High Q Low Q The more energy is concentrated in the resonance (high quality factor Q), the better is the sensitivity outside the resonance Example : fighting thermal noise Most Virgo systems (mirrors, benches) are suspended to super- attenuators -> seismic noise attenuated by at least a factor 10 12

D. Buskulic, ACAT 2002, Moscow Suspended injection bench Laser lab Detection bench DAQ System Suspended mirror

D. Buskulic, ACAT 2002, Moscow The Central Interferometer (CITF) DAQ System Commissioning of the CITF : February 2001-July 2002 Try to lock, test systems, build tools

D. Buskulic, ACAT 2002, Moscow Engineering runs Already 4 engineering runs with the central part of the interferometer (CITF) 3 Days each 1 TB of collected data each time Learned a lot about the machine Locking procedure, stability of operation Collection of data Data Display/Analysis Analysis tools : Data Display Matlab VEGA A little bit of PAW

D. Buskulic, ACAT 2002, Moscow Data acquisition system and data rates Control signals Monitoring signals Interferometer output Reconstructed signals Trigger signals stable flux of 4 MB/s TB/year Fast Digitization Locking and Alignment Servo-Loops Fast Digitization or Local Servo-Loop Frame Processing Environment Monitoring < 1Hz Frame Building SMS format (Ethernet) Frame format (Ethernet) GPS signals On-line Processing Main Frame Builder Detector Monitoring Data Distribution Timing Information Timing System Timing Crate Slow Frame Builder Frame Builder, Local Control Fast Frame Builder Damping and Control suspensions Photodiodes Readout Data Archiving Global Control Calibration Slow Monitoring Station GPS H Reconstruction Data Quality DOL (Optical Fibers) Servo-Loop DOL (Optical Fibers) (9 MB/s uncompressed)

D. Buskulic, ACAT 2002, Moscow The Frame format GW data : continuous stream, divided into "Frames" 1 Frame = time slice of all interferometer data : ADC channels, Monitoring, reconstructed h, etc… Ability to preselect data : 1 Frame is then ~ 100 KB on average Common format needed for exchange of data Adopted by all GW experiments in the world, including bar experiments Time 1s 4 MB

D. Buskulic, ACAT 2002, Moscow Data collection and distribution DAQ collects Frame pieces and builds output frames Online processing produces digested data (trend, 50 Hz) Sent to various data displays and stored on disk After engineering runs, raw data transferred to Computing centers (CCIN2P3 Lyon and Bologna) by network

D. Buskulic, ACAT 2002, Moscow Offline Computing Size of data set -> distributed computing CCIN2P3 Lyon Computing Center Bologna VIRGO Experiment (Computing Resources Cascina) Central bookkeeping database

D. Buskulic, ACAT 2002, Moscow Data analysis challenges Pulsar searches Long quasi periodic signals Variation of frequency due to earth movement, earth-star relative movement and position Signal characteristics dominated by Star parameters (rotation period, quadrupolar moment, binary system ?) Position in the sky Rotating neutron stars that have a small dissymmetry (ellipticity of ) generate a gravitational wave Very weak signal (h ~ ) buried in noise But very long (10 5 years) Need to integrate very long signals (months) to extract signal from noise

D. Buskulic, ACAT 2002, Moscow It's a hard analysis : all sky pulsar search For one set of parameters (position in the sky, star period…) do a search in the output signal Number of different cells in parameter space as high as ! To keep with incoming data rate Brute force method would need TFlops of processing power! With hierarchical methods and Hough transforms, need 1 TFlops Still a huge processing power Able to distribute computing : each node will treat a frequency band and/or a subset of parameter space

D. Buskulic, ACAT 2002, Moscow Data analysis challenges Bursts (supernovae…) -> short signals Coalescing binary compact objects (neutron stars or black holes) Shape of a calculated signal: chirp with amplitude and frequency growing in time Depends mainly on the masses of the two stars Binary neutron stars that coalesce (merge) after a fall down inspiraling phase produce GW inspiraling + merger lasts from a few seconds to a minute h ~ still small Courtesy M. Ruffert, MPA Garching

D. Buskulic, ACAT 2002, Moscow Binary coalescence search The search has to be done For all possible theoretical signals (templates), i.e. for all possible physical parameters of the system templates (waveforms) in the parameter space of interest -> 300 Gflops needed to do a full search Even more if take into account more physical parameters Alignment of stars spins, ellipticity of orbits, etc… Easily distributed : a computing node may treat a subset of templates Theoretical signal (template) Experimental (noisy) signal Optimal filtering (weighted intercorrelation) Result : Theoretical signal present ? Optimal filtering technique

D. Buskulic, ACAT 2002, Moscow Data visualization : the Data Display Home build online display and monitoring tool Access files or remote frames Channel browser

D. Buskulic, ACAT 2002, Moscow Data visualization : the Data Display Read and display frame files content Receive and display frames sent over network Uses Xforms (GUI) ROOT Libs (plots, display)

D. Buskulic, ACAT 2002, Moscow Data analysis tools Matlab Many people in our community used to it Rich set of signal analysis functions/tools Difficulty to easily handle the size of data sets available Night-day seismic noise on site

D. Buskulic, ACAT 2002, Moscow Data analysis tool : VEGA VEGA Offline data handling/analysis environment based on ROOT Scripting : CINT Data visualization Adapted ROOT to handle time-dependent data (up to a few million points) Signal processing Interfaced to external libraries (FFTW, SigLib…) Home made signal processing lib

D. Buskulic, ACAT 2002, Moscow Data analysis tool : VEGA Data handling Meta information in one place, data in another Access frames through a "channel" Can build localy a metadatabase which is used as an index to handle a local set of files

D. Buskulic, ACAT 2002, Moscow Trend data visualization Trend (downsampled at 1Hz) data displayed on the web in quasi-realtime Uses Local metadatabase Display by VEGA analysis tool Shell scripts

D. Buskulic, ACAT 2002, Moscow Cooperative analyses and data exchange The same GW can a priori be seen by all detectors on earth Depends on the orientation and amplitude of the wave Cooperative analysis allows to extract more information from the signal Physical parameters Direction of propagation Need to exchange data -> same data format… OK, we have the Frame format Already exchanged some online monitoring data between LIGO and VIRGO in quasi-real time Wish to use GRID tools for data exchange Still problems for GRID middleware compatibility DataGrid (VIRGO) / GriPhyN-iVDGL (LIGO) interface

D. Buskulic, ACAT 2002, Moscow Developments around GRID Use of European DataGrid Test of a binary coalescence search Each job treats one subspace of all templates. Test of a periodic sources search Hierarchical approach which alternates an FFT step and a Hough transform step Each node analyzes a frequency band  Verified that multiple jobs can be submitted and the output retrieved with small overhead time  Computational grids seems suitable to perform data analysis for coalescing binaries and periodic sources searches See " A Grid Approach to Geographically Distributed Data Analysis for Virgo", Palomba, Tortone and al., GWADW 2002 Workshop

D. Buskulic, ACAT 2002, Moscow Summary GW data analysis needs : GW data analysis produces large amounts of data (in the 100 TB/year range) Data is continuous -> Frame format Needs a lot of computing power (TFlops) Data analysis tools used during VIRGO engineering runs Data Display Matlab VEGA Preparing the future : Needs to exchange data among experiments Some exchange already done Efforts on the way to use GRID