PyMerger is a Python tool for detecting binary black hole mergers from the Einstein Telescope, based on a Deep Residual Neural Network model.
PyMerger is a Python tool for detecting binary black hole (BBH) mergers from the Einstein Telescope (ET), based on a Deep Residual Neural Network model (ResNet). ResNet was trained on data combined from all three proposed sub-detectors of ET (TSDCD) to detect BBH mergers. Five different lower frequency cutoffs (F_low): 5 Hz, 10 Hz, 15 Hz, 20 Hz, and 30 Hz, with match-filter Signal-to-Noise Ratio (MSNR) ranges: 4-5, 5-6, 6-7, 7-8, and >8, were employed in the data simulation. Compared to previous work that utilized data from single sub-detector data (SSDD), the detection accuracy from TSDCD has shown substantial improvements, increasing from 60%, 60.5%, 84.5%, 94.5% to 78.5%, 84%, 99.5%, 100%, and 100% for sources with MSNR of 4-5, 5-6, 6-7, 7-8, and >8, respectively. The ResNet model was evaluated on the first Einstein Telescope mock Data Challenge (ET-MDC1) dataset, where the model demonstrated strong performance in detecting BBH mergers, identifying 5,566 out of 6,578 BBH events, with optimal SNR starting from 1.2, and a minimum and maximum D_L of 0.5 Gpc and 148.95 Gpc, respectively. Despite being trained only on BBH mergers without overlapping sources, the model achieved high BBH detection rates. Notably, even though the model was not trained on BNS and BHNS mergers, it successfully detected 11,477 BNS and 323 BHNS mergers in ET-MDC1, with optimal SNR starting from 0.2 and 1, respectively, indicating its potential for broader applicability.
First install FrameCCP, required by GWpy, available only on Conda (or Mamba):
conda install -c conda-forge python-ldas-tools-framecppThen
pip install PyMergersOr
- Clone the repository:
git clone https://github.com/wathela/PyMerger.git
cd PyMerger- Install the required Python packages:
pip install -r requirements.txt PyMerger assumes that each sub-detector of ET will have a separate .gwf file in three separate directories (E1, E2, E3). The data input path should point to the folder where these three directories are located.
usage: pymergers [-h] [-r {8192,4096}] [-n NO_SEGMENT] [-c CHANNELS CHANNELS CHANNELS] [-t THRESHOLD] -i INPUT_FILE_DIR -f OUTPUT_DIR [--verbose]
optional arguments:
-h, --help show this help message and exit
-r {8192,4096}, --sampling-rate {8192,4096}
Sampling rate of the input data (either 8192 or 4096). Default is 8192.
-n NO_SEGMENT, --no-segment NO_SEGMENT
Number of data segments to be processed for each detector (i.e., number of .gwf files to be processed for each detector).
Files in the input directory will be sorted, and the first 'n' files up to the specified number of segments will be processed.
Default is 1 which means there are 1 unique file from each detector.
-c CHANNELS CHANNELS CHANNELS, --channels CHANNELS CHANNELS CHANNELS
List of the THREE channels to be processed. Default is ['E1:STRAIN', 'E2:STRAIN', 'E3:STRAIN'].
-t THRESHOLD, --threshold THRESHOLD
Threshold value for merger detection. A value between 0.5 and 0, where a smaller value will result in fewer detections but
a lower false positive rate. Default is 0.1 (accepting detection with at least 90\% confidence).
-i INPUT_FILE_DIR, --input-file-dir INPUT_FILE_DIR
Directory containing the input .gwf files.
-f OUTPUT_DIR, --output-dir OUTPUT_DIR
Directory to store the results.
--verbose Enable verbose mode to print update messages. Default is true.
Installed with pip: pymergers -r 8192 -n 2 -c E1:STRAIN E2:STRAIN E3:STRAIN -t 0.5 -i /path/to/input/files -f /path/to/output/dir Repo cloned: python pymerger.py -r 8192 -n 2 -c E1:STRAIN E2:STRAIN E3:STRAIN -t 0.5 -i /path/to/input/files -f /path/to/output/dirFor more information, see the associated research papers:
Email: wathelahamed (at) gmail.com