Jet flavor classification in high-energy physics with deep neural networks

Abstract

Classification of jets as originating from light-flavor or heavy-flavor quarks is an important task for inferring the nature of particles produced in high-energy collisions. The large and variable dimensionality of the data provided by the tracking detectors makes this task difficult. The current state-of-the-art tools require expert data-reduction to convert the data into a fixed low-dimensional form that can be effectively managed by shallow classifiers. We study the application of deep networks to this task, attempting classification at several levels of data, starting from a raw list of tracks. We find that the highest-level lowest-dimensionality expert information sacrifices information needed for classification, that the performance of current state-of-the-art taggers can be matched or slightly exceeded by deep-network-based taggers using only track and vertex information, that classification using only lowest-level highest-dimensionality tracking information remains a difficult task for deep networks, and that adding lower-level track and vertex information to the classifiers provides a significant boost in performance compared to the state-of-the-art.

Exploring anomalous couplings in Higgs boson pair production through shape analysis

“…While the benchmark points representing a cluster are isolated points in the parameter space, the procedure we propose here allows us to associate certain shapes more straightforwardly with distinct regions in the parameter space. The application of machine learning techniques in high energy physics, in particular to constrain the EFT/new physics parameter space, has been brought forward already some time ago [68][69][70][71], with successful applications in jet and top quark identification [72][73][74][75][76][77][78][79][80][81], new physics searches [70,71,[82][83][84][85][86][87][88][89][90] and PDFs [91]. Shape analysis with machine learning has been applied already to constrain anomalous Higgs-vector boson couplings in HZ production [92].…”

Deep-learning top taggers or the end of QCD?

“…Artificial networks with more than one or two hidden layers are referred to as deep neural networks (DNN). ANNs and DNNs are frequently used in LHC analyses [34][35][36][37][38][39].…”

What is the machine learning?

“…In what follows, as we study planing we will also utilize a technique (see [2][3][4][5]11,12]) which we refer to as "saturation," that compares a network trained on only low-level inputs with networks trained after adding higher-level variables. Saturation provides a tool to ensure that our networks are sufficiently deep, by checking that the new network's performance does not improve by much.…”

“…The authors of [2][3][4][5] emphasized the ability of deep learning to outperform physics inspired high-level variables. We use the "uniform phase space" scheme to flatten discriminating variables, which was introduced in [6] to quantify the information learned by deep neural networks.…”