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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 \}} \], 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 \[ R={{\sum_{i=1}^{n} (X_i - \overline{X})(Y_i - \overline{Y})} \over {\sqrt{\sum_{i=1}^{n} (X_i - \overline{X}) \sum_{i=1}^{n} (Y_i - \overline{Y})}}} \], where \[ \overline{X} \] and \[ \overline{Y} \] are the mean of X and Y, respectively |
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==== Pearson's Correlation Coefficient (PCC) ==== | ==== Kendall's tau Coefficient ==== |
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==== 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 \[ \tau =\frac{c-d}{{n \choose 2}} \], where c is the number of concordant pairs, and d is the number of discordant pairs ==== Flow chart ==== |
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TBA | [[TBA]] |
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==== Code version ==== | ==== Code versions ==== |
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* fixed memory issues * fixed minor bugs |
* fixed minor issues |
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5. CAGMon Etude Flat (latest version) | 5. CAGMon Etude Flat (current version) |
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==== Structure of scripts ==== |
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 * fixed minor issues and optimized scripts ==== Series of scripts ==== |
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* the script gathered functions medel required | * the script gathered functions the model required |
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* the script to make plots, such as coefficient trend, coefficient distribution trend, time-series, and scatter plots | * the script to make plots |
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* CAGMonEtudeFlat.py | * CAGMonEtude{Version}.py |
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==== User guide ==== * [[/userguide | How to use CAGMon]] ==== Needs of code development ==== * Fundamental critarian or guideline of the stride and its data-size * 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 || Date || GPS time || Stride || Sample rate || Data size || Summary page link || Remarks || || April 7 || 1270287158 - 1270328032 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 8 || 1270339218 - 1270425618 || || || || || Full Data is unavailable in the KISTI cluster || || April 9 || 1270425618 - 1270510167 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 10 || 1270513160 - 1270596544 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 11 || 1270598418 - 1270683904 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 12 || 1270684818 - 1270762046 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 14 || 1270909686 - 1270937768 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 15 || 1270945288 - 1271017582 || || || || || GRB200415 (08:48:05 UTC) / Full Data is unavailable in the KISTI cluster || || April 16 || 1271030433 - 1271112809 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 17 || 1271119833 - 1271186507 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 18 || 1271227441 - 1271288128 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 19 || 1271289618 - 1271364033 || 600s || 16Hz || about 10,000 || [[ | summary page]] || || || || || 300s || 32Hz || about 10,000 || [[ | summary page]] || || || April 20 || 1271377409 - 1271460608 || || || || || GRB200420A (2:32:58 UTC) / Full Data is unavailable in the KISTI cluster || 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 || Stride || Sample sata || Data size || Dada length || Summary page link || || 10s || 512Hz || about 5,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b5000%5d/ | summary page]] || || 10s || 1024Hz || about 10,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b10000%5d/ | summary page]] || || 10s || 2048Hz || about 20,000 || about 12m || [[ https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b20000%5d/| summary page]] || || 10s || 3072Hz || about 30,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b30000%5d/ | summary page]] || || 10s || 4096Hz || about 40,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b40000%5d/ | summary page]] || || 2s || 4096Hz || about 8,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b2s%5d/| summary page]] || || 5s || 4096Hz || about 20,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b5s%5d/| summary page]] || || 60s || 128Hz || about 7,500 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b60s%5d/ | summary page]] || || 150s || 64Hz || about 10,000 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b150s%5d/ | summary page]] || || 300s || 64Hz || about 20,000 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b300s%5d/ | summary page]] || || 600s || 16Hz || about 10,000 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b600s%5d/ | 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
- fixed minor issues and optimized scripts
Series of scripts
- Agrement.py
- the script gathered functions the model 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{Version}.py
- the script to run each script
User guide
Needs of code development
- Fundamental critarian or guideline of the stride and its data-size
- 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
Date
GPS time
Stride
Sample rate
Data size
Summary page link
Remarks
April 7
1270287158 - 1270328032
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 8
1270339218 - 1270425618
Full Data is unavailable in the KISTI cluster
April 9
1270425618 - 1270510167
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 10
1270513160 - 1270596544
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 11
1270598418 - 1270683904
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 12
1270684818 - 1270762046
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 14
1270909686 - 1270937768
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 15
1270945288 - 1271017582
GRB200415 (08:48:05 UTC) / Full Data is unavailable in the KISTI cluster
April 16
1271030433 - 1271112809
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 17
1271119833 - 1271186507
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 18
1271227441 - 1271288128
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 19
1271289618 - 1271364033
600s
16Hz
about 10,000
300s
32Hz
about 10,000
April 20
1271377409 - 1271460608
GRB200420A (2:32:58 UTC) / Full Data is unavailable in the KISTI cluster
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
Stride
Sample sata
Data size
Dada length
Summary page link
10s
512Hz
about 5,000
about 12m
10s
1024Hz
about 10,000
about 12m
10s
2048Hz
about 20,000
about 12m
10s
3072Hz
about 30,000
about 12m
10s
4096Hz
about 40,000
about 12m
2s
4096Hz
about 8,000
about 12m
5s
4096Hz
about 20,000
about 12m
60s
128Hz
about 7,500
whole iKAGRA data
150s
64Hz
about 10,000
whole iKAGRA data
300s
64Hz
about 20,000
whole iKAGRA data
600s
16Hz
about 10,000
whole iKAGRA data
Beyond
References
Presentation materials
Papers
Science.1518; Detecting Novel Associations in Large Data Sets