The primary goal at the Large Hadron Collider (LHC) is now to discover new physics, often termed Beyond the Standard Model (BSM) physics, and machine learning techniques could prove essential to this discovery. In this talk I will illustrate jet substructure tools, and explain the need for more powerful algorithms to better understand the complex signatures that arise in the LHC data. I will briefly review some of the most important applications of machine learning tools used in studying LHC data, and discuss their advantages and disadvantages. The precise focus of the talk will be on unsupervised searches for BSM physics using Bayesian generative modelling, in particular the Latent Dirichlet Allocation (LDA) algorithm. I will motivate the use of these techniques with an approximate mapping between the process through which particle collisions at the LHC evolve into measurements in the detectors, and the process of document generation described by the LDA model. The goal in using this technique is to extract ‘topics’ from the data, which describe the physics underlaying the signals that have been measured in the collider. With these topics in hand, we can then use them to classify individual signals as having arisen from different underlaying processes. Two advantages of this technique are (i) it is unsupervised and hence insensitive to modelling inaccuracies, (ii) the extraction of topics allows the user to analyse what has been learned by the algorithm. I will conclude the talk with two applications of this technique; the first is in uncovering a pair-produced top quark signal, and the second is in uncovering a W′ signal.
