Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments.

中文翻译：

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从数据中学习新物理学：对称方法


数千人年被投入到寻找新物理学 （NP） 中，其中大部分是出于理论考虑。然而，没有发现超越标准模型物理学的证据。这表明，与模型无关的搜索可能是探索 NP 的重要关键，并有助于发现可以激发未来理论发展的意外现象。此类搜索的一种可能策略是识别在标准模型中预期对称的数据样本之间的不对称性。我们建议利用神经网络 （NN） 快速拟合和统计测试两个样本之间的差异。我们的方法基于早期的工作，最初设计用于推断观察到的数据集与更大的参考数据集的偏差。我们提出了一种对称形式，概括了原始形式，避免了 NN 参数的微调和对样本相对大小的任何限制。我们的形式主义可用于检测微小的对称性违规，扩展当前和未来粒子物理学实验的发现潜力。