Data Characterization in Gravitational Waves · 2020. 1. 22. · Soma Mukherjee 26/3/03 What data...
Transcript of Data Characterization in Gravitational Waves · 2020. 1. 22. · Soma Mukherjee 26/3/03 What data...
Data Characterization in Gravitational Waves
Soma MukherjeeMax Planck Institut fuer Gravitationsphysik
Golm, Germany.
Talk atUniversity of Texas, Brownsville.March 26, 2003
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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What does data analysis involve ?
Detector Characterization :Looking at ALL channels all the time for detector diagnosticCalibration
Data Characterization :Checking the stability of the dataData decomposition
Astrophysical Searches :Algorithm developmentPost search analysis
Vetoes Upper limits
Simulations
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Computational Aspects
Very large data volume demands AutomationSpeedParallel processingEfficient database
Systems available : LDAS, DMT, DCR, GODCS
Data Mining
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Aspects that I work on
Data stabilityNon-stationarity – detection and measureImplications in Astrophysics
Burst Upper Limit*Post detection analysis – Data Mining
ExploratoryClassificationCoincidence
Externally Triggered Search Association with Gamma Ray Bursts
* http://www.aei.mpg.de/~soma/bursts.html
Robust Detection of Noise Floor Drifts in InterferometricData
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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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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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Sequence :Sequence :
Low pass and resample
Estimate spectralnoise floor using Running Median
Design FIR Whitening filter.
Whiten data.
Clean lines.Highpass.
Compute RunningMedian of the
squared timeseries.
Thresholds set by Simulation.
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Data :Data :
Locked segments from :
LIGO S1 : L1 and H2LIGO S2 : L1 and H1
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With transients added
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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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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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Questions wrt Astrophysical Search
Threshold and tolerance.… being worked up on.
Work in the area of Burst Upper Limits
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Components of a Burst search pipeline
Conditioned Data Search Filter Event database
Generate VetoProduction of list of eventsCoincidence
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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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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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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.