Improving the measurement of the Higgs boson-gluon coupling using convolutional neural networks at e+e− colliders

Abstract

In this paper we propose to use Convolutional Neural Networks (CNN) to improve the precision measurement of Higgs boson-gluon effective coupling at lepton colliders. The CNN is employed to recognize the Higgs boson and a Z boson associated production process, with the Higgs boson decaying to a gluon pair and the Z boson decaying to a lepton pair at the center-of-mass energy 250 GeV and integrated luminosity 5 ab −1 . By using CNN the uncertainty of effective coupling measurement can be decreased from 1.94% to about 1.28% using the PYTHIA data and from 1.82% to about 1.22% using the HERWIG data in the MC simulation. Moreover, the performance of CNN using different final state constituents shows that the leading and subleading jets play a major role on the identification and the optimal uncertainty of effective coupling using CNN is reduced by about 35% compared to that using conventional method.

Learning physics at future e−e+ colliders with machine

“…These complexities could be addressed in several ways with the ML techniques. The first one is to image the events and then apply the ML techniques of image recognition, such as Convolutional Neural Network (CNN) [25,50,51], for their classification. In this case, the pixel intensity in each image represents the total contribution of the visible particles hitting this pixel to some kinematic variables such as E. The dimension of the input parameters is thus determined not by the particle number, but by the pixel number.…”

“…In this case, the pixel intensity in each image represents the total contribution of the visible particles hitting this pixel to some kinematic variables such as E. The dimension of the input parameters is thus determined not by the particle number, but by the pixel number. Similar techniques of image recognition have been applied to tagging light jets [28], boosted W boson [52] and top quark [53], selecting events [25,49,[54][55][56], mitigating pileups at the LHC [57], etc. For its directviewing and effectiveness, we will take this method below.…”

“…The jet substructure manifests color, electric charge, flavor, etc. of the jet ancestral partons and hence is useful in discriminating, e.g., quark/gluon jets [25,26,[26][27][28][29][30]. The jet superstructure is usually formulated if the jet ancestral partons share the same parent particle, where they tend to be showered in a correlated way [31].…”