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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

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 \[ R=\sum_{i=1}^{n} (X_i - \overline{X})(Y_i - \overline{Y})} \over {\sqrt{\sum_{i=1}^{n} (X_i - \overline{X}) \sum_{i=1}^{n} (Y_i - \overline{Y})} \],

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

\[ \tau =\frac{c-d}n \choose 2 \],

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
    • fixed minor issues and optimized scripts

Series of scripts

User guide

Needs of code development

Exemplary results

1. Earthquake effects during O3GK

2. Skim through all obs-segments of O3GK

3. With iKAGRA hardware injection data

Beyond

References

Presentation materials

JGW-G2112481-v1

Papers

Science.1518; Detecting Novel Associations in Large Data Sets