论文标题
赤字鹰队：具有未知背景的强大新物理搜索
Deficit hawks: robust new physics searches with unknown backgrounds
论文作者
论文摘要
搜索新物理学通常会面临未知的背景，导致虚假检测或上限弱。本文介绍了赤字鹰技术，该技术通过测试数据削减的多种选项，例如基准体积或能量阈值来减轻未知的背景。将似然比的力量与间隔搜索技术的鲁棒性相结合，赤字鹰可以通过对具有部分或投机背景知识的实验的两个因子来改善新物理学上的平均上限。赤字鹰队非常适合使用机器学习或其他多维歧视技术的分析，并且可以扩展以允许在没有未知背景的区域中发现。
Searches for new physics often face unknown backgrounds, causing false detections or weakened upper limits. This paper introduces the deficit hawk technique, which mitigates unknown backgrounds by testing multiple options for data cuts, such as fiducial volumes or energy thresholds. Combining the power of likelihood ratios with the robustness of the interval-searching techniques, deficit hawks could improve mean upper limits on new physics by a factor two for experiments with partial or speculative background knowledge. Deficit hawks are well-suited to analyses that use machine learning or other multidimensional discrimination techniques, and can be extended to permit discoveries in regions without unknown background.
