|
Size: 1355
Comment:
|
Size: 2205
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 7: | Line 7: |
| == Project Goal == | == Project goal == |
| Line 22: | Line 22: |
\[ r=\frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i-\bar{y})}{\sqrt{\sum_{i=1}^{n} (x_i-\bar{x})^2} \sqrt{\sum_{i=1}^{n} (y_i -\bar{y})^2}} \] |
|
| Line 26: | Line 32: |
| == Code development == | |
| Line 27: | Line 34: |
| ==== Basic structure ==== | |
| Line 28: | Line 36: |
| == Exemplary Results == | ==== Code version ==== 1. CAGMon Etude Alpha * for the basic test and evaluation of the LASSO regression method developed by LIGO 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 == Exemplary results == |
| Line 34: | Line 58: |
| ==== Presentation Materials ==== | ==== Presentation materials ==== |
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
- 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
Exemplary results
Beyond
References
Presentation materials