The VIRGO experiment

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The VIRGO experiment

• Data analysis software tools used during Virgo
  engineering runs Review and future needs.

2
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 3.10-23 Hz-1/2 (at 500
  Hz)
• Full Virgo commissioning starts beginning 2003

• Firenze-Urbino
• Frascati
• Napoli
• Perugia
• Pisa
• Roma

• LAPP Annecy
• IPN Lyon
• OCA Nice
• LAL Orsay
• ESPCI Paris

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Keeping really quiet

• Reach this sensitivity lower all the noises

• Data sampled at 20 kHz
• Continuous stream of data

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Fighting the noise

• Mechanical systems pendulums and resonating
  
  systems
• Optical systems Ultra-stable laser
• Electronic systems Fighting all the noises !

Most Virgo systems (mirrors, benches) are
suspended to super-attenuators - seismic noise
attenuated by at least a factor 1012
Example fighting thermal noise
High Q
The more energy is concentrated in the resonance
(high quality factor Q), the better is the
sensitivity outside the resonance
Low Q
w
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DAQ System
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The Central Interferometer (CITF)
Commissioning of the CITF February 2001-July
2002 Try to lock, test systems,build tools
DAQ System
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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

8
Data acquisition system and data rates
Fast Digitization Locking and Alignment
Servo-Loops
Environment Monitoring Fast Digitization or Local Servo-Loop
Damping and Control suspensions
Frame Builder, Local Control
Photodiodes Readout
Global Control
Calibration
Slow Monitoring Station
Frame Building
Timing System
GPS
Timing Crate
Main Frame Builder Detector Monitoring
Slow Frame Builder
Fast Frame Builder
Frame Processing
On-line Processing
Data Archiving
H Reconstruction
Data Quality
GPS signals
Timing Information
Servo-Loop DOL (Optical Fibers)
DOL (Optical Fibers)
Data Distribution
SMS format (Ethernet)
Frame format (Ethernet)

• Control signals
• Monitoring signals
• Interferometer output
• Reconstructed signals
• Trigger signals

stable flux of 4 MB/s
(9 MB/s uncompressed)
100 - 125 TB/year
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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
4 MB
1s
Time
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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

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Offline Computing

• Size of data set
• - distributed computing

CCIN2P3 Lyon
Computing CenterBologna
Centralbookkeeping database
VIRGO Experiment (Computing Resources Cascina)
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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 10-6 ) generate a
  gravitational wave
• Very weak signal (h 10-25 ) buried in noise
• But very long (105 years)
• Need to integrate very long signals (months)
  to extract signal from noise

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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 1029 !
• To keep with incoming data rate
• Brute force method would need 1015 TFlops of
  processing power!
• With hierarchical methods and Hough transforms,
  need1 TFlops
• Still a huge processing power
• Able to distribute computing each node will
  treat a frequency band and/or a subset of
  parameter space

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Data analysis challenges

• Bursts (supernovae) - short signals
• Coalescing binary compact objects(neutron stars
  or black holes)
• Shape of a calculated signalchirp with
  amplitude and frequencygrowing in time
• Depends mainly onthe 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 10-23 - 10 -22 still small

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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
• 5.105 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)
Result Theoretical signalpresent ?
Optimal filtering(weighted intercorrelation)

• Optimal filtering technique

Experimental (noisy) signal
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Data visualization the Data Display

• Home build online display and monitoring tool
• Access files or remote frames
• Channel browser

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Data visualization the Data Display

• Read and display frame files content
• Receive and display frames sent over network

Uses
ROOT Libs(plots, display)
Xforms (GUI)
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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

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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

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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

http//wwwlapp.in2p3.fr/virgo/vega
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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

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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

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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

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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