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arXiv 2201.02523hep-exphysics.data-anphysics.ins-det
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中微子-核相互作用中的顶点发现：模型结构比较

Vertex finding in neutrino-nucleus interaction: A Model Architecture Comparison

F. Akbar, A. Ghosh, S. Young, S. Akhter, Z. Ahmad Dar, V. Ansari, M. V. Ascencio, M. Sajjad Athar, A. Bodek, J. L. Bonilla, A. Bravar, H. Budd, G. Caceres, T. C… 展开作者

F. Akbar, A. Ghosh, S. Young, S. Akhter, Z. Ahmad Dar, V. Ansari, M. V. Ascencio, M. Sajjad Athar, A. Bodek, J. L. Bonilla, A. Bravar, H. Budd, G. Caceres, T. Cai, M. F. Carneiro, G. A. Díaz, J. Felix, L. Fields, A. Filkins, R. Fine, P. K. Gaura, R. Gran, D. A. Harris, D. Jena, S. Jena, J. Kleykamp, A. Klustová, D. Last, A. Lozano, X. G. Lu, E. Maher, S. Manly, W. A. Mann, K. S. McFarland, B. Messerly, J. Miller, O. Moreno, J. G. Morfín, J. K. Nelson, C. Nguyen, A. Olivier, V. Paolone, G. N. Perdue, K. J. Plows, M. A. Ramírez, D. Ruterbories, H. Su, V. S. Syrotenko, A. V. Waldron, B. Yaeggy, L. Zazueta

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摘要：

我们比较了不同的神经网络架构的机器学习（ML）算法，旨在确定中微子相互作用的顶点位置的MINERvA探测器。将手工开发和优化的架构与使用橡树岭国家实验室（ORNL）开发的“用于深度学习的多节点进化神经网络”（MENNDL）包以自动化方式开发的架构进行比较。这两种架构导致了类似的性能，这表明与优化的网络架构相关的系统性很小。此外，我们发现，虽然领域专家手动调整的网络是最好的表现，差异是可以忽略不计的，自动生成的网络表现良好。在网络优化的人力和计算机资源之间总是存在权衡，这项工作表明，假设资源可用，自动优化提供了一种引人注目的方式来节省大量的专家时间。

英文摘要：

We compare different neural network architectures for Machine Learning (ML) algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package "Multi-node Evolutionary Neural Networks for Deep Learning" (MENNDL), developed at Oak Ridge National Laboratory (ORNL). The two architectures resulted in a similar performance which suggests that the systematics associated with the optimized network architecture are small. Furthermore, we find that while the domain expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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