Quark jet versus gluon jet: fully-connected neural networks with high-level features

Abstract

Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images with the benefit that essentially no physics input is required. Inspired by a recent work by Datta and Larkoski, we study the classification of quark/gluon-initiated jets based on fully-connected neural networks (FNNs), where expertdesigned physical variables are taken as input. FNNs are applied in two ways: trained separately on various narrow jet transverse momentum p T J bins; trained on a wide region of p T J ∈ [200, 1000] GeV. We find their performances are almost the same. The performance is better when the p T J is larger. Jet discrimination with FNN is studied on both particle and detector level data. The results based on particle level data are comparable with those from deep convolutional neural networks, while the significance improvement characteristic (SIC) from detector level data would at most decrease by 15%. We also test the performance of FNNs with full set or subsets of jet observables as input features. The FNN with one subset consisting of fourteen observables shows nearly no degradation of performance. This indicates that these fourteen expert-designed observables could have captured the most necessary information for separating quark and gluon jets.

Searching for new physics with deep autoencoders

“…Deep learning is a hot topic in high energy physics. It has been applied to tagging boosted jets of various kinds [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], to quark/gluon discrimination [16][17][18], and full event classification [19,20]. These are all examples of supervised learning where the training sets are labeled with truth information.…”

Reweighting a parton shower using a neural network: the final-state case

“…NN techniques have been successfully applied or used exploratively in a number of topics in collider phenomenology, often with much more complex NN architectures than what we employ in this work. The topics include jet tagging/particle identification [17][18][19][20][21][22][23][24][25], event classification [26][27][28], phase-space integration [29], pile-up mitigation [30], simulating electromagnetic showers in a calorimeter [31], parameter space scans for New Physics searches [32], and of course PDF fitting [1]. Moreover, a deep NN has been proposed to mimic a parton shower algorithm [33].…”

Investigating the topology dependence of quark and gluon jets

“…Therefore, gluon jets tend to have more constituents and a broader radiation pattern than quark jets 1 . Recent developments in quark versus gluon jet tagging have resulted from advances in the theoretical [14][15][16], phenomenological [17,18], and experimental [19][20][21][22][23][24][25] understanding of quark-versus-gluon jet tagging as well as the development of powerful machine learning techniques that can utilize the entire jet internal radiation pattern [24][25][26][27][28][29][30][31].…”