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==== Basic structure ==== | ==== GitHub ==== TBA |
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* reproduced original CAGMon methods and idea | |
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---- /!\ '''Edit conflict - your version:''' ---- 4. CAGMon Etude Eta ---- /!\ '''End of edit conflict''' ---- |
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* fixed minor issues and optimized scripts |
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==== 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 |
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
- CAGMon Etude Alpha
- for the basic test and evaluation of the LASSO regression method developed by LIGO
- reproduced original CAGMon methods and idea
- 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
- CAGMon Etude Eta
- fixed memory issues
- fixed minor bugs
- added the range limitation of stride
- 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