论文标题
通过机器学习优化可观察到的东西，以更好地展开
Optimizing Observables with Machine Learning for Better Unfolding
论文作者
论文摘要
粒子和核物理学中的大多数测量都使用基于基质的展开算法来校正检测器效应。在几乎所有情况下，可观察到的可观察到在粒子和检测器水平上类似地定义。我们指出，虽然粒子级可观察到的可观察到与理论联系起来，但探测器级别不需要并且可以优化。我们表明，使用深度学习来定义探测器级别可观察物具有与标准展开方法结合使用时改进测量值的能力。
Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods.
