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 ?

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.

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

Soma Mukherjee 26/3/03

Computational Aspects

Very large data volume demands AutomationSpeedParallel processingEfficient database

Systems available : LDAS, DMT, DCR, GODCS

Data Mining

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

Robust Detection of Noise Floor Drifts in Interferometric Data

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.

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

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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26/3/03

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.

Soma Mukherjee 26/3/03

Questions wrt Astrophysical Search

Threshold and tolerance.… being worked up on.

Work in the area of Burst Upper Limits

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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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

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.

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.