Data Characterization in Gravitational Waves

Transcription

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 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.

3 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

4 Computational Aspects Very large data volume demands Automation Speed Parallel processing Efficient database Systems available : LDAS, DMT, DCR, GODCS Data Mining

5 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 *

6 Robust Detection of Noise Floor Drifts in Interferometric Data

7 Soma Mukherjee,, 26/3/03 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.

8 26/3/03 Soma Mukherjee, 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

9 Sequence : Low pass and resample Estimate spectral noise floor using Running Median Design FIR Whitening filter. Whiten data. Thresholds set by Simulation. Compute Running Median of the squared timeseries. Clean lines. Highpass. Soma Mukherjee, GWDAW7, Kyoto, Japan, 19/12/02 9

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

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19 With transients added

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21 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 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.

24 Questions wrt Astrophysical Search Threshold and tolerance. being worked up on.

25 Work in the area of Burst Upper Limits

26 Components of a Burst search pipeline Conditioned Data Search Filter Event database Coincidence Production of list of events Generate Veto

27 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 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.

29 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 nonstationarity. Development of efficient post-detection algorithms.