data characterization in gravitational waves soma mukherjee max planck institut fuer...
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Data Characterization in Gravitational Waves
Soma Mukherjee Max Planck Institut fuer Gravitationsphysik Golm, Germany.
Talk at University of Texas, Brownsville.March 26, 2003
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Soma Mukherjee 26/3/03
What data do we have ?
Consists of information from the main gravitational wave channel and ~1000 auxiliary channels.
Science run data (S1 and S2) from the
three LIGO and GEO interferometers.
Several (E1-E9) Engineering run data.
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Soma Mukherjee 26/3/03
What does data analysis involve ?
Detector Characterization : Looking at ALL channels all the time for detector
diagnostic Calibration
Data Characterization : Checking the stability of the data Data decomposition
Astrophysical Searches : Algorithm development Post search analysis
Vetoes Upper limits
Simulations
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Soma Mukherjee 26/3/03
Computational Aspects
Very large data volume demands AutomationSpeedParallel processingEfficient database
Systems available : LDAS, DMT, DCR, GODCS
Data Mining
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Soma Mukherjee 26/3/03
Aspects that I work on
Data stability Non-stationarity – detection and measure Implications in Astrophysics
Burst Upper Limit* Post detection analysis – Data Mining
Exploratory Classification Coincidence
Externally Triggered Search Association with Gamma Ray Bursts
* http://www.aei.mpg.de/~soma/bursts.html
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Robust Detection of Noise Floor Drifts in Interferometric Data
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Soma Mukherjee, 26/3/03Soma Mukherjee, 26/3/03
Why :Why :
Interferometric data has three components : Lines, transients, noise floor.
Study of a change in any one of these without elimination of the other two will cause interference.
Lines dominate.
Presence of transients change the central tendency.
“SLOW” nonstationarity of noise floor interesting in the analysis of several astrophysical searches, e.g. Externally triggered search.
To be able to simulate the non-stationarity to test the efficiencies of various algorithms.
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Soma Mukherjee, 26/3/03
Method :Method :
MNFT :1. Bandpass and resample given timeseries x(k).2. Construct FIR filter than whitens the noise
floor. Resulting timeseries : w(k)3. Remove lines using notch filter. Cleaned
timeseries : c(k)4. Track variation in second moment of c(k)
using Running Median*. 5. Obtain significance levels of the sampling
distribution via Monte Carlo simulations. * Mohanty S.D., 2002, CQG
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Soma Mukherjee, GWDAW7, Kyoto, Japan, 19/12/02
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Soma Mukherjee 26/3/03
Sequence :Sequence :
Low pass and
resample
Estimate spectral
noise floor using
Running Median
Design FIR
Whitening filter.
Whiten data.
Clean lines.
Highpass.
Compute Running
Median of the
squared timeseries.
Thresholds set by
Simulation.
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Soma Mukherjee,
26/3/03
Data :Data :
Locked segments from :
LIGO S1 : L1 and H2LIGO S2 : L1 and H1
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Soma Mukherjee, 26/3/03
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Soma Mukherjee, 26/3/03
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Soma Mukherjee, 26/3/03
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Soma Mukherjee, 26/3/03
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Soma Mukherjee 26/3/03
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Soma Mukherjee 26/3/03
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Soma Mukherjee 26/3/03
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Soma Mukherjee 26/3/03
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Soma Mukherjee 26/3/03
With transients added
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Soma Mukherjee 26/3/03
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Soma Mukherjee 26/3/03
Computation of :
G(m)=V(Z t+m – Z t)/V(Z t+1 – Z t)
Z t : t th sample of a timeseries.
m: Lag.
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Soma Mukherjee 26/3/03
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Soma Mukherjee 26/3/03
Comments :Comments :
Threshold setting by single simulation.Discussions underway for incorporation in the externally triggered burst search analysis.Automation.Use MBLT for line removal.C++ codes underway.Incorporation in the DCR in near future.
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Soma Mukherjee 26/3/03
Questions wrt Astrophysical Search
Threshold and tolerance.… being worked up on.
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Work in the area of Burst Upper Limits
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Soma Mukherjee 26/3/03
Components of a Burst search pipeline
Conditioned Data Search Filter Event database
Generate VetoProduction of list of eventsCoincidence
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Soma Mukherjee 26/3/03
Veto generation – Classification
Data from main (h(t))channel
Data from Auxiliary Channels
……….
Triggers
Trigger characterization (amplitude, frequency, shape information, duration, time of arrival…)
ClassificationInstrumental Source
Identification
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Soma Mukherjee 26/3/03
Classification continued …
Future plans
Construction of a distance measure in multi-parameter space.Identification of non-redundant parameters.Discover statistically significant clusters.Correlate bursts from different sources that fall into the same cluster.
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Soma Mukherjee 26/03/03
More future plans
Continue analysis of Science data.More emphasis on injection and simulation in the burst analysis.Suitable modification to the existing algorithms to accommodate non-stationarity.Development of efficient post-detection algorithms.