SPINUP用神经网络解耦探测器效应，无需依赖模拟数据先验。
Simulation-Prior Independent Neural Unfolding Procedure
- 基于神经网络建模正向映射，实现与模拟先验无关的解卷积。
- 通过神经重要性采样提升效率，集成方法可评估正向过程的信息损失。
- 适用于喷注结构和希格斯/单顶夸克产生事例的本征态重构。
机器学习使在大型强子对撞机（LHC）上无需分箱即可解卷高维空间成为可能。新方法SPINUP基于神经网络编码的正向映射，提取未卷积分布，且不依赖训练数据中的先验分布。通过神经重要性采样实现高效计算，集成方法可用于估计正向过程中信息丢失的影响。本文展示了SPINUP在解卷探测器效应对喷注亚结构可观测量的影响，以及在关联希格斯与单顶夸克产生事例中解卷至部分子能级的应用。
原文摘要 · Abstract (English)
Machine learning allows unfolding high-dimensional spaces without binning at the LHC. The new SPINUP method extracts the unfolded distribution based on a neural network encoding the forward mapping, making it independent of the prior from the simulated training data. It is made efficient through neural importance sampling, and ensembling can be used to estimate the effect of information loss in the forward process. We showcase SPINUP for unfolding detector effects on jet substructure observables and for unfolding to parton level of associated Higgs and single-top production.
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