论文标题
选择低悬挂的水果：通过积极学习进行大规模测试新物理
Picking the low-hanging fruit: testing new physics at scale with active learning
论文作者
论文摘要
自从发现希格斯玻色子以来，测试标准模型的许多可能扩展已成为粒子物理学的关键挑战。本文讨论了一种新的方法，用于预测新物理学理论与粒子collider的现有实验数据的兼容性。使用机器学习，该技术仅用其计算资源的一小部分获得了与以前的方法（> 90％精度和回忆）的可比结果（<10％）。这使得可以测试以前无法探测的模型，并允许对新物理学理论进行大规模测试。
Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories with existing experimental data from particle colliders. Using machine learning, the technique obtained comparable results to previous methods (>90% precision and recall) with only a fraction of their computing resources (<10%). This makes it possible to test models that were impossible to probe before, and allows for large-scale testing of new physics theories.
