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Data Characterization in Gravitational Waves

Data Characterization in Gravitational Waves. Soma Mukherjee Max Planck Institut fuer Gravitationsphysik Golm, Germany. Talk at University of Texas, Brownsville. March 26, 2003. Soma Mukherjee 26/3/03 What data do we have ?.

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Data Characterization in Gravitational Waves

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  1. Data Characterization in Gravitational Waves Soma Mukherjee Max Planck Institut fuer Gravitationsphysik Golm, Germany. Talk at University of Texas, Brownsville. March 26, 2003

  2. Soma Mukherjee 26/3/03What 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.

  3. Soma Mukherjee 26/3/03What 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

  4. Soma Mukherjee 26/3/03Computational Aspects Very large data volume demands • Automation • Speed • Parallel processing • Efficient database Systems available : LDAS, DMT, DCR, GODCS • Data Mining

  5. Soma Mukherjee 26/3/03Aspects 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

  6. Robust Detection of Noise Floor Drifts in Interferometric Data

  7. Soma Mukherjee, 26/3/03Why : • 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.

  8. Soma Mukherjee, 26/3/03Method : • MNFT : • Bandpass and resample given timeseries x(k). • Construct FIR filter than whitens the noise floor. Resulting timeseries : w(k) • Remove lines using notch filter. Cleaned timeseries : c(k) • Track variation in second moment of c(k) using Running Median*. • Obtain significance levels of the sampling distribution via Monte Carlo simulations. * Mohanty S.D., 2002, CQG

  9. Soma Mukherjee 26/3/03Sequence : Design FIR Whitening filter. Whiten data. Estimate spectral noise floor using Running Median Low pass and resample Clean lines. Highpass. Thresholds set by Simulation. Compute Running Median of the squared timeseries.

  10. Soma Mukherjee, 26/3/03Data : Locked segments from : • LIGO S1 : L1 and H2 • LIGO S2 : L1 and H1

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  19. Soma Mukherjee 26/3/03 With transients added

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  21. 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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  23. Soma Mukherjee 26/3/03Comments : • 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.

  24. Soma Mukherjee 26/3/03Questions wrt Astrophysical Search • Threshold and tolerance. … being worked up on.

  25. Work in the area of Burst Upper Limits

  26. Soma Mukherjee 26/3/03 Components of a Burst search pipeline Event database Conditioned Data Search Filter Generate Veto Production of list of events Coincidence

  27. 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…) Classification Instrumental Source Identification

  28. Soma Mukherjee 26/3/03Classification 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.

  29. Soma Mukherjee 26/03/03More 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.

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