Boosted W and Z Tagging with Jet Charge and Deep Learning
Total Page：16
File Type：pdf, Size：1020Kb
PHYSICAL REVIEW D 101, 053001 (2020) Boosted W and Z tagging with jet charge and deep learning Yu-Chen Janice Chen ,1 Cheng-Wei Chiang ,1,2,3 Giovanna Cottin,1,4,5 and David Shih6 1Department of Physics, National Taiwan University, Taipei 10617, Taiwan 2Institute of Physics, Academia Sinica, Taipei 11529, Taiwan 3Department of Physics and Center of High Energy and High Field Physics, National Central University, Chungli 32001, Taiwan 4Instituto de Física, Pontificia Universidad Católica de Chile, Avenida Vicuńa Mackenna 4860, Santiago, Chile 5Departamento de Ciencias, Facultad de Artes Liberales, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640 Santiago, Chile 6NHETC, Dept. of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA (Received 30 August 2019; accepted 20 February 2020; published 17 March 2020) We demonstrate that the classification of boosted, hadronically decaying, weak gauge bosons can be significantly improved over traditional cut-based and boosted decision tree-based methods using deep learning and the jet charge variable. We construct binary taggers for Wþ vs W− and Z vs W discrimination, as well as an overall ternary classifier for Wþ=W−=Z discrimination. Besides a simple convolutional neural network, we also explore a composite of two simple convolutional neural networks, with different numbers of layers in the jet pT and jet charge channels. We find that this novel structure boosts the performance particularly when considering the Z boson as a signal. The methods presented here can enhance the physics potential in Standard Model measurements and searches for new physics that are sensitive to the electric charge of weak gauge bosons. DOI: 10.1103/PhysRevD.101.053001 I. INTRODUCTION deep learning to construct extremely powerful taggers, vastly improving on previous methods. Boosted heavy resonances play a central role in the study So far, most of the attention has focused on distinguish- of physics at the Large Hadron Collider (LHC). These ing various boosted resonances from QCD background in a include both Standard Model particles such as W’s, Z’s, binary classification task. Less attention has been paid to tops and Higgses, as well as hypothetical new physics (NP) multiclass classification, i.e., a tagger that would categorize particles such as Z0’s. The decay products of the boosted jets in a multitude of possibilities. (Notable exceptions heavy resonance are typically collimated into a single “fat include Refs. [23,24].) In this work, we will examine an jet” with a nontrivial internal substructure. Avast amount of þ important multiclass classification task: distinguishing W , effort has been devoted to the important problem of W− Z W=Z “ ” , and bosons. Having a classifier that can also tagging (i.e., identifying and classifying) boosted reso- recognize charge could have many interesting applications. nances through the understanding of jet substructure. (For – For instance, one could use such a classifier to measure recent reviews and original references, see, e.g., [1 3].) charge asymmetries and same-sign diboson production at Recently, there has been enormous interest in the the LHC. Or, there are many potential applications to NP application of modern deep learning techniques to boosted – scenarios, such as the reconstruction of doubly charged resonance tagging [4 24]. By enabling the use of high- Higgs bosons from its like-sign diboson decay in models dimensional, low-level inputs (such as jet constituents), with an extended scalar sector. deep learning automates the process of feature engineering. Since we are interested in distinguishing Wþ and W− Many works have demonstrated the enormous potential of bosons from each other, a key element in our work will be the jet charge observable Qκ. It was first introduced in Ref. [25] and its theoretical potential was discussed further in Ref. [26]. Such an observable has also been measured at Published by the American Physical Society under the terms of the LHC [27,28]. When used in conjunction with other the Creative Commons Attribution 4.0 International license. M Further distribution of this work must maintain attribution to quantities, such as the invariant mass , it can help the author(s) and the published article’s title, journal citation, distinguish between hadronically decaying W from Z and DOI. Funded by SCOAP3. bosons [29]. Moreover, its performance as a charge tagger 2470-0010=2020=101(5)=053001(18) 053001-1 Published by the American Physical Society CHEN, CHIANG, COTTIN, and SHIH PHYS. REV. D 101, 053001 (2020) 0.05 → TABLE I. Selections imposed on the jet sample used in our H5 VV, m = 800 GeV H5 analyses. R = 0.7 0.04 pT ∈ ð350; 450Þ GeV, jηj ≤ 1 mW+ Jet sample Jets with anti-kT and R ¼ 0.7 mZ V − V merging: ΔRðV1;V2Þ < 0.6 0.03 V-jet matching: ΔRðV;jÞ < 0.1 0.02 was assessed in Ref. [30] for jets produced in semileptonic Fraction / (0.60 GeV) ¯ tt, W þ jets, and dijet processes. Most recently, the authors 0.01 of Ref. [31] incorporated jet charge into various machine learning quark/gluon taggers, including boosted decision 0 trees (BDTs), convolutional neural networks (CNNs), and 60 70 80 90 100 110 120 recurrent neural networks. They showed that including jet Mass [GeV] charge in the input channels improved quark/gluon dis- FIG. 1. Reconstructed jet mass of W and Z samples. crimination and up vs down quark discrimination. In our study of Wþ=W−=Z tagging, we will compare a number of techniques, from simple cut-based methods, to In Sec. III, we describe the different taggers studied in this BDTs, to deep learning methods based on CNNs and jet paper. These include cut-based and BDT taggers used as images. As in Ref. [31], we will include jet charge as one of baselines for comparison, as well as two different taggers based on convolutional neural networks. We show results the input channels and examine the gain in performance for a binary W−=Wþ classification problem in Sec. IV and from including this additional input. We will go beyond compare our performance with the recent work in Ref. [31]. Refs. [15,29,31] and construct a ternary classifier that can þ þ − In Sec. V, we discuss the Z=W discrimination problem, discriminate among W , W ,andZ, depending on the focusing on the benefit from including jet charge, and physics process of interest. We will study the overall compare our performance with the ATLAS boson tagger in performance of our ternary tagger as well as its specialization Ref. [29]. Finally in Sec. VI, we extend our results to the to binary classification. For the latter we will compare its full ternary Z=Wþ=W− classification problem, and com- performance to specifically trained binary classifiers and ment on the reduction from a three-class tagger to a two- show that the ternary classifier reproduces their performance, class one. In Sec. VII, we attempt to shed some light on and in this sense it is optimal. Overall, we will demonstrate what the deep neural networks learned. We summarize our that deep learning with jet charge offers a significant boost in findings and conclude in Sec. VIII. performance, around ∼30%–40% improvement in back- ground rejection rate at fixed signal efficiency. II. JET SAMPLES AND INPUTS In addition to a simple CNN, we will also develop a novel composite algorithm consisting of two CNNs, one for For this study, we use MADGRAPH5v2.6.1 [32] at leading each of the Qκ and pT channels, combined in a merge layer, order to simulate events at the 13 TeV LHC for vector boson 2 ÆÆ which we refer to as CNN . This allows us to separately fusion production of doubly charged Higgses H5 and ÆÆ Æ Æ optimize the hyperparameters of the CNNs for the two heavy neutral Higgses H5, with decays H5 → W W → 2 input channels. We show that this new CNN architecture jjjj and H5 → ZZ → jjjj, respectively. We take the further boosts the performance for most combinations. spectrum of the exotic Higgs bosons in the Georgi- The rest of the paper is organized as follows. In Sec. II Machacek model generated by GMCalc [33] as the input. m ¼ 800 p we describe the jet samples and jet images used in this For simplicity, we fix H5 GeV, so that the T of study, and review the definition of the jet charge variable. each vector boson is typically ∼400 GeV.1 All events are further processed in PYTHIA 8.2.19 [34] for showering and hadronization and passed onto DELPHES 3.4.1 [35] with the TABLE II. Summary of the jet sample sizes used for training 2 and testing, after the selections in Table I. The entries in the sum default CMS card for detector simulation. Jets are recon- þ − k correspond to the (W ;W ;Z) samples, respectively. In the structed with FASTJET 3.1.3 [36] using the anti- T clustering training stage, the training set is further divided into two subsets: algorithm [37] with the jet radius parameter R ¼ 0.7. This jet 9=10 is the actual training set and 1=10 serves as the validation set. All the receiver operating characteristic (ROC) and signifi- 1 m ¼ 2 We have also studied the scenario where H5 TeV, cance improvement characteristic (SIC) curves shown in this which leads to the pT of each boson around 1 TeV, and found paper are evaluated using the testing sets described here. all results qualitatively the same. 2 A dataset based on HERWIG showering and hadronization is Training set Testing set also generated for the purpose of checking the reliability of the Jet sample size 188k þ 198k þ 175k 38k þ 40k þ 35k deep learning jet-tagging results.