基于变差稀疏化的粒子识别算法
Variational Dropout Sparsification for Particle Identification speed-up
摘要：
精确的粒子识别（PID）是LHCb实验的重要方面之一。现代机器学习技术，如神经网络（NN），有效地应用于这个问题，并集成到LHCb软件。在这项研究中，我们讨论了神经网络加速技术的新应用，以实现更快的PID在LHC升级条件。我们发现，最好的结果是使用变分丢弃稀疏化，它提供了一个预测（前馈通过）的速度增加了16倍，即使与浅网络模型相比。
英文摘要：
Accurate particle identification (PID) is one of the most important aspects of the LHCb experiment. Modern machine learning techniques such as neural networks (NNs) are efficiently applied to this problem and are integrated into the LHCb software. In this research, we discuss novel applications of neural network speed-up techniques to achieve faster PID in LHC upgrade conditions. We show that the best results are obtained using variational dropout sparsification, which provides a prediction (feedforward pass) speed increase of up to a factor of sixteen even when compared to a model with shallow networks.
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。
