alt=KAG alt=KAGs

CAGMon - A Detchar Tool for Noise Propagation using Correlation Analysis

Project Goal

The goal of this project is to find a systematic way of identifying the abnormal glitches in the gravitational-wave data using various methods of correlation analysis. Usually the community such as LIGO and Virgo uses a conventional way of finding glitches in auxiliary channels of the detector - Klein-Welle, Omicron, Ordered Veto Lists, etc. However, some different ways can be possible to find and monitor them in a (quasi-) realtime. Also the method can point out which channel is responsible for the found glitch. In this project, we study its possibility to apply three different correlation methods - maximal information coefficient, Pearson's correlation coefficient, and Kendall's tau coefficient - in the gravitational wave data from LIGO detector.

Participants

Preliminaries

Methods

Pearson's Correlation Coefficient

Kendall's tau Coefficient

where C and D are number of concordant and disconcordant pairs, respectively.

Maximal Information Coefficient

Basically, maximal information coefficient is defined using the mutual information score following the Ref. [1]. Formally, the mutual information of two discrete random variables X and Y can be defined as: \begin{align} I(X;Y) = \sum_{y\in Y} \sum_{x\in X} p(x, y) \log \left(\frac{p(x, y)}{p(x)p(y)}\right) \end{align}

where p(x,y) is the joint probability distribution function of X and Y, and p(x) and p(y) are the marginal probability distribution functions of X and Y respectively. Intuitively, mutual information measures the information that X and Y share: it measures how much knowing one of these variables reduces uncertainty about the other. For example, if X and Y are independent, then knowing X does not give any information about Y and vice versa, so their mutual information is zero [Wikipedia].

It measures non-linear correlation between two data samples while the PCC (Pearson correlation coefficient) and the Spearman coefficient are only for the linear relationship.

With this definition of mutual information, MIC is defined by [2] \[ MIC(D) = \max_{xy

Preliminary Knowledges

Previous Study Results

  1. Klein-Welle Triggers in S6C

  2. Omicron Triggers in S6C

  3. Barkhausen Effect in S6B

References

  1. CAGMon2.0 Guide

  2. CAGMonLKR3 Guide : to be updated

  3. GitLab: to be pushed

  4. Minepy: https://minepy.readthedocs.io/en/latest/

  5. D. N. Reshef, Y. A. Reshef, H. K. Finucane, S. R. Grossman, G. McVean, P. J. Turnbaugh, E. S. Lander, M. Mitzenmacher, P. C. Sabeti, Science, 334, 1518 (2011).

System Requirements for KAGMon

  1. python 3
  2. numpy
  3. scipy
  4. matplotlib
  5. minepy
  6. gwpy

Data & Code Preparation

KAGRA ER2 Data (2020.2-2020.5)

LIGO Data (2020.6~ )

Scheme & Goal

alt SCIACCA Plan alt KAGMon

Preliminary Run Tests:

Observing Run Test of KAGRA O1

Trigger-based Analysis (2020.6~ )

Inspiral Range Plot

Detector Sensitivity

Omicron Glitch Gram

alt Inspiral Range

alt detector sensitivity

alt Omicron

GPS time

UTC time

Duration

Peak frequency

Central freq.

Bandwidth

SNR

GPS_start

GPS_end

Sam_Rate

TStride

FStride

# of Samples

1271311593.609

April 19 2020 06:06:15.609

0.031

121.192

121.195

1.723

288.743

1271311589

1271311597

1kHz

1

8

1000/sec

1271302217.998

April 19 2020 03:29:59.998

0.004

111.850

112.298

20.032

221.521

1271302213

1271302221

1kHz

1

8

1000/sec

1271356741.438

April 19 2020 18:38:43.437

0.125

41.141

41.142

0.585

202.916

1271356737

1271356745

1kHz

1

8

1000/sec

1271337318.002

April 19 2020 13:15:00.0

0.00|4

111.850

112.298

20.032

170.477

1271337314

1271337322

1kHz

1

8

1000/sec

1271340918.001

April 19 2020 14:15:00.000

0.002

133.756

134.291

23.955

166.644

1271340914

1271340922

1kHz

1

8

1000/sec

1271325225.998

April 19 2020 09:53:27.998

0.004

111.850

112.298

20.032

164.685

1271325223

1271325229

1kHz

1

8

1000/sec

1271313018.002

April 19 2020 06:30:00.001

0.004

111.850

112.298

20.032

162.386

1271313014

1271313022

1kHz

1

8

1000/sec

1271303583.984

April 19 2020 03:52:45.984

0.031

121.192

121.195

1.723

161.520

1271303579

1271303587

1kHz

1

8

1000/sec

1271296947.422

April 19 2020 02:02:09.421

0.031

121.192

121.195

1.723

161.284

1271296943

1271296951

1kHz

1

8

1000/sec

1271336417.998

April 19 2020 12:59:59.998

0.004

111.850

112.298

20.032

160.725

1271336413

1271336421

1kHz

1

8

1000/sec

Working Paper