We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show that it produces decision boundaries that change according to the cross sections of the processes involved. We find that the models trained with the new loss have higher signal efficiency for similar values of estimated signal significance compared to ones trained with a cross-entropy loss, showing promise to improve sensitivity of particle physics searches at colliders.

翻译：我们构建了一种替代损失函数，用于直接优化粒子物理学中使用的显著性度量指标。我们通过线性模型在一个简单的事件分类任务上评估了该损失函数，结果表明它能够根据相关过程的截面产生变化的决策边界。我们发现，与使用交叉熵损失训练的模型相比，在估计信号显著性相近的情况下，采用新损失函数训练的模型具有更高的信号效率，这为提升对撞机粒子物理搜索的灵敏度展现了潜力。