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Continuing from our prior work (Alhassan et al. 2022), where a singledetector data of the Einstein Telescope (ET) was evaluated for the detection ofbinary black hole (BBHs) using deep learning (DL). In this work we explored thedetection efficiency of BBHs using data combined from all the three proposeddetectors of ET, with five different lower frequency cutoff (Flow): 5 Hz, 10Hz, 15 Hz, 20 Hz and 30 Hz, and the same previously used SNR ranges of: 4-5,5-6, 6-7, 7-8 and >8. Using ResNet model (which had the best overallperformance on single detector data), the detection accuracy has improved from60%, 60.5%, 84.5%, 94.5% and 98.5% to 78.5%, 84%, 99.5%, 100% and 100% forsources with SNR of 4-5, 5-6, 6-7, 7-8 and >8 respectively. The results show agreat improvement in accuracy for lower SNR ranges: 4-5, 5-6 and 6-7 by 18.5%,24.5%, 13% respectively, and by 5.5% and 1.5% for higher SNR ranges: 7-8 and >8respectively. In a qualitative evaluation, ResNet model was able to detectsources at 86.601 Gpc, with 3.9 averaged SNR (averaged SNR from the threedetectors) and 13.632 chirp mass at 5 Hz. It was also shown that the use of thethree detectors combined data is appropriate for near-real-time detection, andcan be significantly improved using more powerful setup.

Einstein Telescope: binary black holes gravitational wave signals detection from three detectors combined data using deep learning