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= CAGMon etude = == Descripstion == |
{{{ ,-----. ,---. ,----. ,--. ,--. ,--. ,--. ' .--./ / O \ ' .-./ | `.' | ,---. ,--,--, ,---. ,-' '-.,--.,--. ,-| | ,---. | | | .-. || | .---.| |'.'| || .-. || \ | .-. :'-. .-'| || |' .-. || .-. : ' '--'\| | | |' '--' || | | |' '-' '| || | \ --. | | ' '' '\ `-' |\ --. `-----'`--' `--' `------' `--' `--' `---' `--''--' `----' `--' `----' `---' `----' }}} == Description == |
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the Maximal Information coefficient(MIC) of a set D of two-variable data with sample size n and grid less than B(n) is given by | |
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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}} \] |
MIC(D)=\underset{xy<B(n)}{\max}{\left\{ \frac{I^{*}(D,x,y)}{\log \min \left\{x,y \right\}} \right \}} \], |
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where \[\omega(1)<B(n)\le O(n^{1-\epsilon}) \] for some \[ 0<\epsilon<1 \] ==== Pearson's Correlation Coefficient (PCC) ==== |
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==== Pearson's Correlation Coefficient (PCC) ==== | Pearson Correlation Coefficient(PCC) is a statistic that explains the amount of variance accounted for in the relationship between two (or more) variables by \[ R={{\sum_{i=1}^{n} (X_i - \overline{X})(Y_i - \overline{Y})} \over {\sqrt{\sum_{i=1}^{n} (X_i - \overline{X}) \sum_{i=1}^{n} (Y_i - \overline{Y})}}} \], where \[ \overline{X} \] and \[ \overline{Y} \] are the mean of X and Y, respectively |
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Kendall’s tau with a random samples n of observations from two variables measures the strength of the relationship between two ordinal level variables by \[ \tau =\frac{c-d}{{n \choose 2}} \], where c is the number of concordant pairs, and d is the number of discordant pairs ==== Flow chart ==== |
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==== Basic structure ==== | ==== GitHub ==== [[TBA]] |
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==== Code version ==== | ==== Code versions ==== |
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* reproduced original CAGMon methods and idea | |
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---- /!\ '''Edit conflict - other version:''' ---- |
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---- /!\ '''Edit conflict - your version:''' ---- 4. CAGMon Etude Eta ---- /!\ '''End of edit conflict''' ---- * fixed memory issues * fixed minor bugs |
* fixed minor issues |
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---- /!\ '''Edit conflict - other version:''' ---- 5. CAGMon Etude Flat (latest version) ---- /!\ '''Edit conflict - your version:''' ---- 5. CAGMon Etude Flat (latest version) ---- /!\ '''End of edit conflict''' ---- |
5. CAGMon Etude Flat (current version) * fixed minor issues and optimized scripts |
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6. CAGMon Etude Octave (development version) * remove some processes that make Time-series and Scatter plots. Even though it required tremendous memory, this information is not useful * adjust HTML code ==== Series 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 * Echo.py * the script to save the result as HTML web page * CAGMonEtude.py * the script to run each script ==== User guide ==== * [[/userguide | How to use CAGMon]] ==== Needs of code development ==== * Daily running on KAGRA |
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1. Earthquake effects during O3GK * Datetime: 19 April 2020 20:39 UTC * Purpose * Test to run CAGMon algorithm with a remarkable event * To figure out the cause of lock-loss in KAGRA * Results * stride 5 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078(5)/ | Summary page]] * stride 20 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078/ | Summary page]] * stride 30 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078(30)/ | Summary page]] 2. Skim through all obs-segments of O3GK * Purpose * Test for calculation time and required resources with all observation segments during O3GK * To figure out trigger events or abnormal behaviors * Results 3. With iKAGRA hardware injection data * Event * Phenomenon: the strain channel and seismometer channels in iKAGRA had a high correlation during the hardware injection test * Cause: still unknown * Hypothesis: the glitches have relatively the same behavior as the vacuum rotary pump * More detail analysis: [[https://www.dropbox.com/s/950vjc807sgz24u/hveto%20brief%20Report%20for%20K1.pdf?dl=0 | hveto brief Report for K1]] and [[https://www.dropbox.com/s/hb7rx93an8yluiq/PilJong%2C%20KGWG%20Face-to-Face%20Meeting.pdf?dl=0 | KGWG Face-to-Face Meeting]] * Purpose * To verify whether this model senses injected signals and abnormal glitches * To test noise resistance and data-size limitation *Results * data size: about 5,000 during 12 minutes [[ | summary page]] * data size: about 10,000 during 12 minutes [[ | summary page]] * data size: about 20,000 during 12 minutes [[ | summary page]] * data size: about 40,000 during 12 minutes [[ | summary page]] * data size: about 60,000 during 12 minutes [[ | summary page]] * stride: 60 seconds during whole iKAGRA data [[ | summary page]] * stride: 150 seconds during whole iKAGRA data [[ | summary page]] * stride: 300 seconds during whole iKAGRA data [[ | summary page]] * stride: 600 seconds during whole iKAGRA data [[ | summary page]] |
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[[https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/private/DocDB/ShowDocument?docid=12481|JGW-G2112481-v1]] | |
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[[https://science.sciencemag.org/content/334/6062/1518 | Science.1518; Detecting Novel Associations in Large Data Sets]] |
,-----. ,---. ,----. ,--. ,--. ,--. ,--. ' .--./ / O \ ' .-./ | `.' | ,---. ,--,--, ,---. ,-' '-.,--.,--. ,-| | ,---. | | | .-. || | .---.| |'.'| || .-. || \ | .-. :'-. .-'| || |' .-. || .-. : ' '--'\| | | |' '--' || | | |' '-' '| || | \ --. | | ' '' '\ `-' |\ --. `-----'`--' `--' `------' `--' `--' `---' `--''--' `----' `--' `----' `---' `----'
Description
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)
the Maximal Information coefficient(MIC) of a set D of two-variable data with sample size n and grid less than B(n) is given by
\[ MIC(D)=\underset{xy<B(n)}{\max}{\left\{ \frac{I^{*}(D,x,y)}{\log \min \left\{x,y \right\}} \right \}} \],
where \[\omega(1)<B(n)\le O(n^{1-\epsilon}) \] for some \[ 0<\epsilon<1 \]
Pearson's Correlation Coefficient (PCC)
Pearson Correlation Coefficient(PCC) is a statistic that explains the amount of variance accounted for in the relationship between two (or more) variables by \[ R=} \],
where \[ \overline{X} \] and \[ \overline{Y} \] are the mean of X and Y, respectively
Kendall's tau Coefficient
Kendall’s tau with a random samples n of observations from two variables measures the strength of the relationship between two ordinal level variables by
\[ \tau =\frac{c-d} \],
where c is the number of concordant pairs, and d is the number of discordant pairs
Flow chart
Code development
GitHub
Code versions
- 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 minor issues
- added the range limitation of stride
- CAGMon Etude Flat (current version)
- fixed minor issues and optimized scripts
- added the script of HTML summary page
- added coefficient distribution plots
- CAGMon Etude Octave (development version)
- remove some processes that make Time-series and Scatter plots. Even though it required tremendous memory, this information is not useful
- adjust HTML code
Series 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
- Echo.py
- the script to save the result as HTML web page
- CAGMonEtude.py
- the script to run each script
User guide
Needs of code development
- Daily running on KAGRA
Exemplary results
1. Earthquake effects during O3GK
- Datetime: 19 April 2020 20:39 UTC
- Purpose
- Test to run CAGMon algorithm with a remarkable event
- To figure out the cause of lock-loss in KAGRA
- Results
stride 5 seconds Summary page
stride 20 seconds Summary page
stride 30 seconds Summary page
2. Skim through all obs-segments of O3GK
- Purpose
- Test for calculation time and required resources with all observation segments during O3GK
- To figure out trigger events or abnormal behaviors
- Results
3. With iKAGRA hardware injection data
- Event
- Phenomenon: the strain channel and seismometer channels in iKAGRA had a high correlation during the hardware injection test
- Cause: still unknown
- Hypothesis: the glitches have relatively the same behavior as the vacuum rotary pump
More detail analysis: hveto brief Report for K1 and KGWG Face-to-Face Meeting
- Purpose
- To verify whether this model senses injected signals and abnormal glitches
- To test noise resistance and data-size limitation
- Results
data size: about 5,000 during 12 minutes summary page
data size: about 10,000 during 12 minutes summary page
data size: about 20,000 during 12 minutes summary page
data size: about 40,000 during 12 minutes summary page
data size: about 60,000 during 12 minutes summary page
stride: 60 seconds during whole iKAGRA data summary page
stride: 150 seconds during whole iKAGRA data summary page
stride: 300 seconds during whole iKAGRA data summary page
stride: 600 seconds during whole iKAGRA data summary page
Beyond
References
Presentation materials
Papers
Science.1518; Detecting Novel Associations in Large Data Sets