• CAGMon

CAGMon - A Detchar Tool for Noise Propagation using Correlation Analysis

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 and Virgo 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 possibility to apply three different correlation methods - maximal information coefficient, Pearson's correlation coefficient, and Kendall's tau coefficient - in the gravitational wave data from LIGO detector.

Participants

• John J. Oh (lead, NIMS)
• Young-Min Kim (UNIST)
• More....

Preliminaries

Old Link of CAGMon Project@KGWG Wiki

• https://kgwg.nims.re.kr/cbcwiki/CAGMon

Methods

Pearson's Correlation Coefficient

• PCC is a measure of a linear correlation between two random variables.
• Pearson's r is defined as:

• \[ r=\frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i-\bar{y})}{\sqrt{\sum_{i=1}^{n} (x_i-\bar{x})^2} \sqrt{\sum_{i=1}^{n} (y_i -\bar{y})^2}} \]

Kendall's tau Coefficient

• \[ \tau = \frac{2(C-D)}{n(n-1)} \]

where C and D are number of concordant and disconcordant pairs, respectively.

Maximal Information Coefficient

Basically, maximal information coefficient is defined using the mutual information score following the Ref. [1]. Formally, the mutual information of two discrete random variables X and Y can be defined as: \begin{align} I(X;Y) = \sum_{y\in Y} \sum_{x\in X} p(x, y) \log \left(\frac{p(x, y)}{p(x)p(y)}\right) \end{align}

where p(x,y) is the joint probability distribution function of X and Y, and p(x) and p(y) are the marginal probability distribution functions of X and Y respectively. Intuitively, mutual information measures the information that X and Y share: it measures how much knowing one of these variables reduces uncertainty about the other. For example, if X and Y are independent, then knowing X does not give any information about Y and vice versa, so their mutual information is zero [Wikipedia].

It measures non-linear correlation between two data samples while the PCC (Pearson correlation coefficient) and the Spearman coefficient are only for the linear relationship.

With this definition of mutual information, MIC is defined by [2] \[ MIC(D) = \max_{xy

Preliminary Knowledges

Previous Study Results

1. Klein-Welle Triggers in S6C

2. Omicron Triggers in S6C

3. Barkhausen Effect in S6B

References

1. CAGMon2.0 Guide

2. CAGMonLKR3 Guide : to be updated

3. GitLab: to be pushed

4. Minepy: https://minepy.readthedocs.io/en/latest/

5. D. N. Reshef, Y. A. Reshef, H. K. Finucane, S. R. Grossman, G. McVean, P. J. Turnbaugh, E. S. Lander, M. Mitzenmacher, P. C. Sabeti, Science, 334, 1518 (2011).

System Requirements for KAGMon

1. python 3
2. numpy
3. scipy
4. matplotlib
5. minepy
6. gwpy

Data & Code Preparation

KAGRA ER2 Data (2020.2-2020.5)

• location: ldg-ui @ sdfarm -- /data/kagra/archive/data/full/

• 1247024298 / 5 hours (2019. Jul. 13 12:38:00- 17:57:50) http://gwwiki.icrr.u-tokyo.ac.jp/JGWwiki/KAGRA/Subgroups/DET/RUN/ER190713

  • Summary page: Run Status -- https://www.icrr.u-tokyo.ac.jp/~yuzu/bKAGRA_summary/html/20190713_GlitchPlot.html

• TStride = 30, FStride = 8 / dur = 240 / 80 Jobs
• Main Channel: K1:CAL-CS_PROC_MICH_DISPLACEMENT_DQ
• Aux Channels: 464 DQ Channels (+ unsafe)

LIGO Data (2020.6~ )

• O3 glitches and their witness channels : https://wiki.ligo.org/DetChar/GlitchesandWitnesses

• list of glitches
  • Magnetometer set
    • gpstimes: /home/cavaglia/karoo_omicron_O2endxmag/data/gpstimes_endxmag_triggers_sorted_analyzed_ready_1-2049.txt @LLO (ldas-pcdev1.ligo-la.caltech.edu)
  • Air Compressor set
    • gpstimes: /home/cavaglia/karoo_omicron_O1aircompressor/data/gpstimes_O1aircompressor_sorted.txt @ LHO(ldas-pcdev1.ligo-wa.caltech.edu)

Scheme & Goal

Preliminary Run Tests:

• Summary of 19200s Run: KAGRA-CAGMonTest.pdf

• 64s Test Results

• 19200s Test Results

• CTrend Analysis

Observing Run Test of KAGRA O1

Trigger-based Analysis (2020.6~ )

• Data: April 19 2020 SummaryPage

• Channel List: Aux yuzu_dqchlist_20200419.txt | Main: K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ

• Observing Information:

• Loudest events by SNR:
  • 10 loudest K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ (Omicron) events by SNR with minimum 8s separation. Launch omega scans

Working Paper

• JKPS Special Issue: