Extract the energy scale of anomalous γγ → W+W− scattering in the vector boson scattering process using artificial neural networks

Abstract

As a model independent approach to search for the signals of new physics (NP) beyond the Standard Model (SM), the SM effective field theory (SMEFT) draws a lot of attention recently. The energy scale of a process is an important parameter in the study of an EFT such as the SMEFT. However, for the processes at a hadron collider with neutrinos in the final states, the energy scales are difficult to reconstruct. In this paper, we study the energy scale of anomalous γγ → W+W− scattering in the vector boson scattering (VBS) process pp → jjℓ+ℓ−ν$$ \overline{\nu} $$ ν ¯ at the large hadron collider (LHC) using artificial neural networks (ANNs). We find that the ANN is a powerful tool to reconstruct the energy scale of γγ → W+W− scattering. The factors affecting the effects of ANNs are also studied. In addition, we make an attempt to interpret the ANN and arrive at an approximate formula which has only five fitting parameters and works much better than the approximation derived from kinematic analysis. With the help of ANN approach, the unitarity bound is applied as a cut on the energy scale of γγ → W+W− scattering, which is found to has a significant suppressive effect on signal events. The sensitivity of the process pp → jjℓ+ℓ−ν$$ \overline{\nu} $$ ν ¯ to anomalous γγWW couplings and the expected constraints on the coefficients at current and possible future LHC are also studied.

Measuring the anomalous quartic gauge couplings in the $W^+W^-\to W^+W^-$ process at muon collider using artificial neural networks

“…Then, further assuming the mass of the W boson is negligible compared with ŝ, the situation is then approximately the same as the situation investigated in Refs. [38,95], and ŝ can be approximated given by…”

“…Using kinematic analysis, there is another approximation which is ŝap in Refs. [38,95]. ŝap is even less accurate than s, therefore is not discussed in this paper.…”

“…In addition, the muon collider can reach both high energy and high luminosity, which will be of great help to precisely measure aQGCs, since the cross-section induced by dimension-8 operators increases significantly with energy. Meanwhile, high luminosity is considered as one of the keys to solve the "EFT triangle" problem [35][36][37][38].…”

“…energy is necessary information to apply unitarity bounds. To solve the problem of reconstructing ŝ in the processes with multiple (anti-)neutrinos, a machine learning approach has been introduced into high energy physics (HEP) community [38]. The machine learning methods have been widely used, and are being rapidly developed in HEP [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60].…”

Reconstruction of Missing Resonances Combining Nearest Neighbors Regressors and Neural Network Classifiers

“…Another approach could be using a regression algorithm to predict the neutrinos four-momenta or some variable of interest from the observed information. One might tackle tasks of that type by training an algorithm to parameterize a multivalued function f : R n → R m , with a neural network, for example [23][24][25][26]. Methods of density estimation [27] might also be useful 1 .…”