CAGMon etude

Descripstion

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)

\[ r=\frac{\sum_{i=1}^{{n} (x_i - \bar{x})(y_i-\bar{y})}{\sqrt{\sum_{i=1}}{n} (x_i-\bar{x})^{2} \sqrt{\sum_{i=1}}{n} (y_i -\bar{y})^2}} \]

Pearson's Correlation Coefficient (PCC)

Kendall's tau Coefficient

Code development

GitHub

TBA

Code version

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 memory issues
   • fixed minor bugs
   • added the range limitation of stride
5. CAGMon Etude Flat (latest version)
   • fixed minor issues and optimized scripts
   • added the script of HTML summary page
   • added coefficient distribution plots

Structure of scripts

• Agrement.py
  • the script gathered functions medel required
• Melody.py
  • the script to calcutate each coefficient and to save trend data as csv
• Conchord.py
  • the script to make plots, such as coefficient trend, coefficient distribution trend, time-series, and scatter plots
• Echo.py
  • the script to save the result as HTML web page
• CAGMonEtudeFlat.py
  • the script to run each script

Exemplary results

Beyond

References

Presentation materials

Papers