Differences between revisions 2 and 8 (spanning 6 versions)

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

Basic structure

Code version

CAGMon Etude Alpha
- for the basic test and evaluation of the LASSO regression method developed by LIGO
CAGMon Etude Beta
- added coefficient trend plots with LASSO beta, coherence, MIC, PCC, and Kendall's tau
CAGMon Etude Delta
- fixed a critical problem that sucked enormous memory when it used the matplotlib module

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CAGMon Etude Eta

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CAGMon Etude Eta

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fixed memory issues
fixed minor bugs
added the range limitation of stride