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Description

The CAGMon etude is a study version of CAGMon that evaluates the dependence between the primary and auxiliary channels.

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, Virgo, and KAGRA 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 possible to apply three different correlation methods - maximal information coefficient, Pearson's correlation coefficient, and Kendall's tau coefficient - in the gravitational wave data from the KAGRA detector.

Participants

• John.J Oh (NIMS)
• Young-Min Kim (UNIST)
• Pil-Jong Jung (NIMS)

Methods and Frameworks

Maximal Information Coefficient (MIC)

the Maximal Information coefficient(MIC) of a set D of two-variable data with sample size n and grid less than B(n) is given by

\[ MIC(D)=\underset{xy<B(n)}{\max}{\left\{ \frac{I^{*}(D,x,y)}{\log \min \left\{x,y \right\}} \right \}} \],

where \[\omega(1)<B(n)\le O(n^{1-\epsilon}) \] for some \[ 0<\epsilon<1 \]

Pearson's Correlation Coefficient (PCC)

Pearson Correlation Coefficient(PCC) is a statistic that explains the amount of variance accounted for in the relationship between two (or more) variables by \[ R=} \],

where \[ \overline{X} \] and \[ \overline{Y} \] are the mean of X and Y, respectively

Kendall's tau Coefficient

Kendall’s tau with a random samples n of observations from two variables measures the strength of the relationship between two ordinal level variables by

\[ \tau =\frac{c-d} \],

where c is the number of concordant pairs, and d is the number of discordant pairs

Flow chart

Code development

GitHub

TBA

Code versions

1. CAGMon Etude Alpha
   • for the basic test and evaluation of the LASSO regression method developed by LIGO
   • reproduced original CAGMon methods and idea
2. CAGMon Etude Beta
   • added coefficient trend plots with LASSO beta, coherence, MIC, PCC, and Kendall's tau
3. CAGMon Etude Delta
   • fixed a critical problem that sucked enormous memory when it used the matplotlib module
4. CAGMon Etude Eta
   • fixed minor issues
   • added the range limitation of stride
5. CAGMon Etude Flat (current version)
   • fixed minor issues and optimized scripts
   • added the script of HTML summary page
   • added coefficient distribution plots
6. CAGMon Etude Octave (development version)
   • remove some processes that make Time-series and Scatter plots. Even though it required tremendous memory, this information is not useful
   • adjust HTML code

Series of scripts

• Agrement.py
  • the script gathered functions the medel required
• Melody.py
  • the script to calcutate each coefficient and to save trend data as csv
• Conchord.py
  • the script to make plots
• Echo.py
  • the script to save the result as HTML web page
• CAGMonEtude{Version}.py
  • the script to run each script

User guide

• How to use CAGMon

Needs of code development

• Fundamental critarian or guideline of the stride and its data-size
• Daily running on KAGRA

Exemplary results

1. Earthquake effects during O3GK

• Datetime: 19 April 2020 20:39 UTC
• Purpose
  • Test to run CAGMon algorithm with a remarkable event
  • To figure out the cause of lock-loss in KAGRA
• Results

  • stride 5 seconds Summary page

  • stride 20 seconds Summary page

  • stride 30 seconds Summary page

2. Skim through all obs-segments of O3GK

• Purpose
  • Test for calculation time and required resources with all observation segments during O3GK
  • To figure out trigger events or abnormal behaviors
• Results
  • April 7, 1270287158 - 1270328032
  • April 8, 1270339218 - 1270425618
    • Full Data is unavailable in the KISTI cluster
  • April 9, 1270425618 - 1270510167
  • April 10, 1270513160 - 1270596544
  • April 11, 1270598418 - 1270683904
  • April 12, 1270684818 - 1270762046
  • April 14, 1270909686 - 1270937768
  • April 15, 1270945288 - 1271017582
    • Event: GRB200415 (08:48:05 UTC)　
    • Full Data is unavailable in the KISTI cluster
  • April 16, 1271030433 - 1271112809
  • April 17, 1271119833 - 1271186507
  • April 18, 1271227441 - 1271288128
  • April 19, 1271289618 - 1271364033
  • April 20, 1271377409 - 1271460608
    • Event: GRB200420A (2:32:58 UTC)
    • Full Data is unavailable in the KISTI cluster

3. With iKAGRA hardware injection data

• Event
  • Phenomenon: the strain channel and seismometer channels in iKAGRA had a high correlation during the hardware injection test
  • Cause: still unknown
  • Hypothesis: the glitches have relatively the same behavior as the vacuum rotary pump

  • More detail analysis: hveto brief Report for K1 and KGWG Face-to-Face Meeting

• Purpose
  • To verify whether this model senses injected signals and abnormal glitches
  • To test noise resistance and data-size limitation
• Results

  • stride: 10 seconds with about 5000 data size during 12 minutes summary page

  • stride: 10 seconds with about 10000 data size during 12 minutes summary page

  • stride: 10 seconds with about 20000 data size during 12 minutes summary page

  • stride: 10 seconds with about 30000 data size during 12 minutes summary page

  • stride: 10 seconds with about 40000 data size during 12 minutes summary page

  • stride: 2 seconds with about 8000 data size during 12 minutes summary page

  • stride: 5 seconds with about 20000 data size during 12 minutes summary page

  • stride: 60 seconds with about 7500 data during whole iKAGRA data summary page

  • stride: 150 seconds with about 10000 data during whole iKAGRA data summary page

  • stride: 300 seconds with about 20000 data during whole iKAGRA data summary page

  • stride: 600 seconds during whole iKAGRA data summary page

Beyond

References

Presentation materials

JGW-G2112481-v1

Papers

Science.1518; Detecting Novel Associations in Large Data Sets