用扩散模型做变分推断，从强子对撞机数据中清除堆叠干扰。
Variational inference for pile-up removal at hadron colliders with diffusion models
- 用生成模型直接预测无堆叠的硬散射喷注成分。
- 在多种堆叠场景下性能优于软降落法，接近PUPPIML。
- 特别适合探测器效率不完美时的堆叠清理任务。
本文提出一种基于变分推断与扩散模型的新方法vipr，用于$pp$碰撞中堆叠事件的去除。不同于传统分类方法判断粒子来源，vipr采用生成模型直接预测去除堆叠后的硬散射喷注组分，从而获得硬散射喷注成分的完整后验分布，这是堆叠去除领域尚未探索的思路，尤其在探测器效率不理想时优势明显。我们在叠加了堆叠污染的模拟$t\bar{t}$事件喷注样本上评估了vipr性能，结果表明其在各类堆叠场景下均优于软降落法，且在硬散射喷注子结构预测上与PUPPIML表现相当。
原文摘要 · Abstract (English)
In this paper, we present a novel method for pile-up removal of $pp$ interactions using variational inference with diffusion models, called vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior over hard-scatter jet constituents, which has not yet been explored in the context of pile-up removal, yielding a clear advantage over existing methods especially in the presence of imperfect detector efficiency. We evaluate the performance of vipr in a sample of jets from simulated $t\bar{t}$ events overlain with pile-up contamination. vipr outperforms softdrop and has comparable performance to puppiml in predicting the substructure of the hard-scatter jets over a wide range of pile-up scenarios.
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