论文标题
在使用贝叶斯概率模型的对撞机事件中发现隐藏的图案
Uncovering hidden patterns in collider events with Bayesian probabilistic models
论文作者
论文摘要
像LHC这样的高能量山着壁上的个别事件可以通过一系列测量值或可观察到的空间中的“点模式”来表示。从这些数据表示开始，我们构建了一个简单的贝叶斯概率模型，用于在超越标准模型（BSM）研究的无监督事件分类中有用的事件测量。为了到达此模型，我们假设事件测量值是可交换的（并应用了Finetti的表示定理），数据是离散的，并且测量是从多个“潜在”分布（称为“主题”）生成的。对撞机事件的最终概率模型是一种混合成员模型，称为潜在的Dirichlet分配（LDA），该模型在自然语言处理应用中广泛使用。通过对初级隆德平面中的点模式进行训练，我们证明了两个主题LDA模型可以学会区分未标记的Dijet事件中，由BSM签名产生的隐藏的新物理模式从更大的QCD背景中产生。基于1904.04200和2005.12319。
Individual events at high-energy colliders like the LHC can be represented by a sequence of measurements, or 'point patterns' in an observable space. Starting from this data representation, we build a simple Bayesian probabilistic model for event measurements useful for unsupervised event classification in beyond the standard model (BSM) studies. In order to arrive to this model we assume that the event measurements are exchangeable (and apply De Finetti's representation theorem), the data is discrete, and measurements are generated from multiple 'latent' distributions, called 'themes'. The resulting probabilistic model for collider events is a mixed-membership model known as Latent Dirichlet Allocation (LDA), a model extensively used in natural language processing applications. By training on point patterns in the primary Lund plane, we demonstrate that a two-theme LDA model can learn to distinguish in unlabelled dijet events the hidden new physics patterns produced by a BSM signature from a much larger QCD background. Based on 1904.04200 and 2005.12319.
