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Author: Admin | 2025-04-27
Cause any additional reduction in efficiency, and the ROI classification accuracy represents the signal efficiency. For images containing only NB, only one in approximately 50 images is kept after ROI-finding.6. ^Profiling. Available online at: https://fastmachinelearning.org/hls4ml/api/profiling.html (accessed January 2, 2022).7. ^QKeras. Available online at: https://github.com/google/qkeras (accessed December 20, 2021).References Aad, G., Berthold, A.-S., Calvet, T., Chiedde, N., Fortin, E. M., Fritzsche, N., et al. (2021). Artificial neural networks on FPGAs for real-time energy reconstruction of the ATLAS LAr calorimeters. Comput. Softw. Big Sci. 5, 19. doi: 10.1007/s41781-021-00066-yCrossRef Full Text | Google Scholar Aarrestad, T., Loncar, V., Ghielmetti, N., Pierini, M., Summers, S., Ngadiuba, J., et al. (2021). Fast convolutional neural networks on FPGAs with hls4ml. Mach. Learn. Sci. Tech. 2, 045015. doi: 10.1088/2632-2153/ac0ea1CrossRef Full Text | Google Scholar Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., et al. (2020a). Deep underground neutrino experiment (DUNE), far detector technical design report, Volume I Introduction to DUNE. arXiv preprint arXiv:2002.02967. doi: 10.1088/1748-0221/15/08/T08008CrossRef Full Text | Google Scholar Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., et al. (2020b). Deep underground neutrino experiment (DUNE), far detector technical design report, Volume II: DUNE Physics. arXiv preprint arXiv:2002.03005. doi: 10.48550/arXiv.2002.03005CrossRef Full Text | Google Scholar Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., et al. (2020c). Deep underground neutrino experiment (DUNE), far detector technical design report, Volume III: DUNE Far Detector Technical Coordination. arXiv preprint arXiv:2002.03008. doi: 10.1088/1748-0221/15/08/T08009CrossRef Full Text | Google Scholar Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., et al. (2020d). Deep underground neutrino experiment (DUNE), far detector technical design report, Volume IV: Far Detector Single-phase Technology. arXiv preprint arXiv:2002.03010. doi: 10.1088/1748-0221/15/08/T08010CrossRef Full Text | Google Scholar Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., et al. (2020e). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Phys. Rev. D 102, 092003. doi: 10.1103/PhysRevD.102.092003CrossRef Full Text | Google Scholar Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., et al. (2021b). Supernova neutrino burst detection with the deep underground neutrino experiment. Eur. Phys. J. C 81, 423. doi: 10.1140/epjc/s10052-021-09166-wCrossRef Full Text | Google Scholar Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., et al. (2021a). Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon
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