论文标题
Lepton风味违规识别（$τ^{ - } \rightarrowμ^{ - }μ^{ - }μ^{+} $）使用人工智能
Lepton Flavour Violation Identification in Tau Decay ($τ^{-} \rightarrow μ^{-}μ^{-}μ^{+}$) Using Artificial Intelligence
论文作者
论文摘要
中微子振荡的发现证明了中微子确实有质量，揭示了当前标准模型（SM）理论中粒子的不适。从理论上讲，具有质量的中微子可能会导致Lepton的风味不是一种称为Lepton风味的对称性（LFV）。尽管SM理论扩展允许LFV过程，但它们的分支部分太小，即使最新设备最新的设备也无法观察。因此，近年来，科学家从合并的LHCB和Monte-Carlo模拟的数据中产生了类似LFV的过程，以尝试使用人工智能（AI），特别是机器学习（ML）和深度学习（DL）来识别LFV。在本文中，已经介绍了AI中几种算法的性能，例如XGBoost，LightGBM，自定义的1-D密集块神经网络（DBNNS）和自定义的1-D卷积神经网络（CNN），以识别LFV信号，具体识别$模仿上述衰减的签名的LHCB和蒙特卡洛模拟的数据的衰减。还进行了Kolmogorov-Smirnov（KS）和Cramer-Von Mises（CVM）测试，以验证每种训练有素的算法的预测有效性。结果表明，除了LightGBM外，算法之间的表现不错，因为CVM测试失败了，并且由于记录了相当低的AUC而获得了20层的CNN。同时，XGBoost和10层DBNN的最高AUC为0.88。本文的主要贡献是涉及不同层中自定义DBNN和CNN算法的广泛实验，与GBM和基于树的算法不同，在过去几年中，所有这些算法都很少用于识别LFV样签名，这些算法在所述任务中更受欢迎。
The discovery of neutrino oscillation, proving that neutrinos do have masses, reveals the misfits of particles in the current Standard Model (SM) theory. In theory, neutrinos having masses could result in lepton flavour not being a symmetry called Lepton Flavour Violation (LFV). While SM theory extensions allowed LFV processes, their branching fractions are too small, making them unobservable even with the strongest equipment up-to-date. With that, scientists in recent years have generated LFV-like processes from the combined LHCb and Monte-Carlo-Simulated data in an attempt to identify LFV using Artificial Intelligence (AI), specifically Machine Learning (ML) and Deep Learning (DL). In this paper, the performance of several algorithms in AI has been presented, such as XGBoost, LightGBM, custom 1-D Dense Block Neural Networks (DBNNs), and custom 1-D Convolutional Neural Networks (CNNs) in identifying LFV signals, specifically $τ^{-} \rightarrow μ^{-}μ^{-}μ^{+}$ decay from the combined LHCb and Monte-Carlo-Simulated data that imitates the signatures of the said decay. Kolmogorov-Smirnov (KS) and Cramer-von Mises (CvM) tests were also conducted to verify the validity of predictions for each of the trained algorithms. The result shows decent performances among algorithms, except for the LightGBM, for failing the CvM test, and a 20-layered CNN for having recorded a considerably low AUC. Meanwhile, XGBoost and a 10-layered DBNN recorded the highest AUC of 0.88. The main contribution of this paper is the extensive experiment involving custom DBNN and CNN algorithms in different layers, all of which have been rarely used in the past years in identifying LFV-like signatures, unlike GBMs and tree-based algorithms, which have been more popular in the said task.
