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The algorithm of Correlation Analysis based on Glitch Monitoring This model evaluates the independence of channels (CAGMon)

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The CAGMon is the tool that evaluates the dependence between the primary and auxiliary channels of Gravitational-Wave detectors.

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.

Status

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References

The CAGMon algorithm is described in

  • Piljong Jung, Sang Hoon Oh, Young-Min Kim, Edwin J. Son, John J. Oh, Optimizing Parameters of Information-Theoretic Correlation Measurement for Multi-Channel Time-Series Datasets in Gravitational Wave Detectors, arXiv:2107.03516
  • Piljong Jung, Sang Hoon Oh, Young-Min Kim, Edwin J. Son, John J. Oh, Identifying and diagnosing coherent associations and causalities between multi-channels of the gravitational wave detector, arXiv:2204.00370

Installation

$ git clone https://github.com/pjjung/cagmon.git
$ cd cagmon
$ python setup.py install

or, you can do:

$ pip install cagmon

Syntax of configuration files (.ini)

  • Example of full configurations

[GENERAL]
gps_start_time = 1234500000
gps_end_time = 1234599968
stride = 512

[PREPROSECCING]
datasize = 8192
filter_type = highpass (or low/bandpass)
frequency1 = 10 (if bandpass file is applied, two frequency conditions are required; frequncy1 and crequncy2)

[SEGMENT]
defined_condition = LSC_LOCK_STATE_CHANNEL == 10 (or segment_file_path = /path/to/segment/file/)

[CHANNELS]
main_channel = GW-STRAIN_CHANNEL
aux_channels_file_path = /path/to/channel/list/file

[INPUT AND OUTPUT PATHS]
frame_files_path = /path/to/frame/file/folder
output_path = /path/to/output/folder

  • Example of essential configurations

[GENERAL]
gps_start_time = 1234500000
gps_end_time = 1234599968
stride = 512

[SEGMENT]
defined_condition = LSC_LOCK_STATE_CHANNEL == 10 (or segment_file_path = /path/to/segment/file/)

[CHANNELS]
main_channel = GW-STRAIN_CHANNEL
aux_channels_file_path = /path/to/channel/list/file

[INPUT AND OUTPUT PATHS]
frame_files_path = /path/to/frame/file/folder
output_path = /path/to/output/folder

Syntax of Channel list files

  • Type 1

K1:AUX_CHANNEL_NAME_1
K1:AUX_CHANNEL_NAME_2
K1:AUX_CHANNEL_NAME_3
.
.
.

  • Type 1

K1:AUX_CHANNEL_NAME_1 SAMPLE_RATE
K1:AUX_CHANNEL_NAME_2 SAMPLE_RATE
K1:AUX_CHANNEL_NAME_3 SAMPLE_RATE
.
.
.

Execute the CAGMon etude

$ cagmon --config cagmon_config.ini

License

The CAGMon is following the GNU General Public License version 3. Under this term, you can redistribute and/or modify it. See the GNU free software license for more details.

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The algorithm of Correlation Analysis based on Glitch Monitoring This model evaluates the independence of channels (CAGMon)

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