论文标题
混合理论培训：我们可以通过混合物理理论来更好地发现新的物理和异常吗？
Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?
论文作者
论文摘要
近年来，与模型无关的搜索策略越来越多，因为一方面没有明确的新物理信号，另一方面缺乏标准模型的高度可能且无参数的扩展。由于这些原因，到目前为止还没有简单的搜索目标。在这项工作中，我们试图迈出一个新的方向并提出一个问题：考虑到我们有大量的新物理理论超出了标准模型并可能包含一定的真理，我们可以通过“结合使用”来改善未知信号的搜索策略？特别是，我们表明，基于许多不同理论信号模型的大型，相互融合的集合的信号假设可能是找到未知BSM信号的出色方法。应用于最近的数据挑战，我们表明“理论训练”优于通过单个BSM模型以及大多数无监督的策略来优化信号区域的策略。这项工作的应用包括在寻找新物理学信号中的异常检测和信号区域的定义。
Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them "in combination"? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that "mixture-of-theories training" outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.
