This report is based on the presentation in the Detchar meeting on 2024/05/29. Further details can be found here: https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/private/DocDB/ShowDocument?docid=15808
We pick the segments in science mode (K1:GRD_SCIENCE_MODE:1) from 1368835218 (2023/05/23 00:00:00 UTC) ~ 1387065618 (2023/12/20 00:00:00 UTC) and apply Omicron with several different parameter settings on the KAGRA strain data (K1:CAL-CS_PROC_DARM_STRAIN_DBL_DQ). Only the segments without IPC glitches and greater than 124 s are chosen.
There are 3,551,222 triggers with SNR >= 5, 38,766 triggers with SNR >= 10, 680 triggers with SNR >= 50. Part of the Q-transform spectrograms of the triggered glitches with SNR >= 50 are shown in the slides. The plots of Q-transform spectrogram can be found here: https://git.ligo.org/agoodmanjerry/k1-o4a-glitches/-/tree/main/O4a-no-ipc/K1?ref_type=heads.
We will apply unsupervised machine learning method on classifying the Q-transform spectrogram of the 38766 triggers with SNR >= 10. Then apply the same methods to the triggers with 6 >= SNR > 10.