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|| 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 ||
|| GPS time || UTC time || Duration || Peak frequency || Central freq. || Bandwidth || SNR || GPS_start || GPS_end || Sam_Rate || TStride || FStride || # of Samples || Run Check ||
|| 1271311593.609 || April 19 2020 06:06:15.609 || 0.031 || 121.192 || 121.195 || 1.723 || 288.743 || 1271311586 || 1271311600 || 8192 || 2 || 7 || 16384/seg || (./) ||
|| 1271302217.998 || April 19 2020 03:29:59.998 || 0.004 || 111.850 || 112.298|| 20.032|| 221.521 || 1271302210 || 1271302224 || 8192 || 2 || 7 || 16384/seg || {X}||
|| 1271356741.438 || April 19 2020 18:38:43.437 || 0.125 || 41.141|| 41.142|| 0.585|| 202.916|| 1271356734 || 1271356748 || 8192 || 2 || 7 || 16384/seg ||{X}||
|| 1271337318.002 || April 19 2020 13:15:00.0|| 0.004|| 111.850|| 112.298|| 20.032|| 170.477|| 1271337311 || 1271337325 || 8192 || 2 || 7 || 16384/seg ||{X}||
|| 1271340918.001 || April 19 2020 14:15:00.000|| 0.002|| 133.756|| 134.291|| 23.955|| 166.644|| 1271340911 || 1271340925 || 8192 || 2 || 7 || 16384/seg ||{X}||
|| 1271325225.998|| April 19 2020 09:53:27.998|| 0.004|| 111.850|| 112.298|| 20.032|| 164.685|| 1271325220 || 1271325234 || 8192 || 2 || 7 || 16384/seg ||{X}||
|| 1271313018.002|| April 19 2020 06:30:00.001|| 0.004|| 111.850|| 112.298|| 20.032|| 162.386|| 1271313011 || 1271313025 || 8192 || 2 || 7 || 16384/seg ||{X}||
|| 1271303583.984|| April 19 2020 03:52:45.984|| 0.031|| 121.192|| 121.195|| 1.723|| 161.520|| 1271303576 || 1271303590 || 8192 || 2 || 7 || 16384/seg ||{X}||
|| 1271296947.422|| April 19 2020 02:02:09.421 ||0.031|| 121.192 || 121.195|| 1.723||161.284|| 1271296940 || 1271296954 || 8192|| 2 || 7 || 16384/seg ||{X}||
|| 1271336417.998|| April 19 2020 12:59:59.998 ||0.004 || 111.850 || 112.298 || 20.032 || 160.725|| 1271336410 || 1271336424 || 8192 || 2 || 7 || 16384/seg ||{X}||

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

  • John J. Oh (lead, NIMS)
  • Young-Min Kim (UNIST)
  • More....

Preliminaries

Methods

Pearson's Correlation Coefficient

  • PCC is a measure of a linear correlation between two random variables.
  • Pearson's r is defined as:
  • \[ r=\frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i-\bar{y})}{\sqrt{\sum_{i=1}^{n} (x_i-\bar{x})^2} \sqrt{\sum_{i=1}^{n} (y_i -\bar{y})^2}} \]

Kendall's tau Coefficient

  • \[ \tau = \frac{2(C-D)}{n(n-1)} \]

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~ )

  • O3 glitches and their witness channels : https://wiki.ligo.org/DetChar/GlitchesandWitnesses

  • list of glitches
    • Magnetometer set
      • gpstimes: /home/cavaglia/karoo_omicron_O2endxmag/data/gpstimes_endxmag_triggers_sorted_analyzed_ready_1-2049.txt @LLO (ldas-pcdev1.ligo-la.caltech.edu)
    • Air Compressor set
      • gpstimes: /home/cavaglia/karoo_omicron_O1aircompressor/data/gpstimes_O1aircompressor_sorted.txt @ LHO(ldas-pcdev1.ligo-wa.caltech.edu)

Scheme & Goal

alt SCIACCA Plan alt KAGMon

Preliminary Run Tests:

Observing Run Test of KAGRA O1

Trigger-based Analysis (2020.6~ )

  • Data: April 19 2020 SummaryPage

  • Observing Information:

Inspiral Range Plot

Detector Sensitivity

Omicron Glitch Gram

alt Inspiral Range

alt detector sensitivity

alt Omicron

  • Loudest events by SNR:
    • 10 loudest K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ (Omicron) events by SNR with minimum 8s separation. Launch omega scans

|| GPS time || UTC time || Duration || Peak frequency || Central freq. || Bandwidth || SNR || GPS_start || GPS_end || Sam_Rate || TStride || FStride || # of Samples || Run Check ||

1271311593.609

April 19 2020 06:06:15.609

0.031

121.192

121.195

1.723

288.743

1271311586

1271311600

8192

2

7

16384/seg

(./)

1271302217.998

April 19 2020 03:29:59.998

0.004

111.850

112.298

20.032

221.521

1271302210

1271302224

8192

2

7

16384/seg

{X}

1271356741.438

April 19 2020 18:38:43.437

0.125

41.141

41.142

0.585

202.916

1271356734

1271356748

8192

2

7

16384/seg

{X}

1271337318.002

April 19 2020 13:15:00.0

0.004

111.850

112.298

20.032

170.477

1271337311

1271337325

8192

2

7

16384/seg

{X}

1271340918.001

April 19 2020 14:15:00.000

0.002

133.756

134.291

23.955

166.644

1271340911

1271340925

8192

2

7

16384/seg

{X}

1271325225.998

April 19 2020 09:53:27.998

0.004

111.850

112.298

20.032

164.685

1271325220

1271325234

8192

2

7

16384/seg

{X}

1271313018.002

April 19 2020 06:30:00.001

0.004

111.850

112.298

20.032

162.386

1271313011

1271313025

8192

2

7

16384/seg

{X}

1271303583.984

April 19 2020 03:52:45.984

0.031

121.192

121.195

1.723

161.520

1271303576

1271303590

8192

2

7

16384/seg

{X}

1271296947.422

April 19 2020 02:02:09.421

0.031

121.192

121.195

1.723

161.284

1271296940

1271296954

8192

2

7

16384/seg

{X}

1271336417.998

April 19 2020 12:59:59.998

0.004

111.850

112.298

20.032

160.725

1271336410

1271336424

8192

2

7

16384/seg

{X}

Working Paper

  • JKPS Special Issue:

CAGMon (last edited 2020-06-20 20:01:40 by johnoh)