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= 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 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, 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 ==== |