PyMerger: Detecting Binary Black Hole merger from Einstein Telescope Using Deep Learning

Understand

We present PyMerger, 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 ($F_{\text{low}}$): 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.