Related papers: Optimized Neural Networks to Search for Higgs Boso…
We present a search for the Standard Model Higgs boson produced in association with a W boson in $p\bar{p}$ collisions at a center-of-mass energy of $\sqrt{s} = 1.96$ TeV. The search is performed in the $WH\to \ell\nu b\bar{b}$ channel…
Machine Learning algorithms, such as Boosted Decisions Trees and Deep Neural Network, are widely used in High-Energy-Physics. The aim of this study is to apply Bayesian Optimization to tune the hyperparameters used in a machine learning…
A major challenge in designing neural network (NN) systems is to determine the best structure and parameters for the network given the data for the machine learning problem at hand. Examples of parameters are the number of layers and nodes,…
Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space. Typically the problem is solved by reducing the…
We show that neural network classifiers can be helpful to discriminate Higgs production from background at LHC in the Higgs mass range M= 200 GeV. We employ a common feed-forward neural network trained by the backpropagation algorithm for…
We present the results from the Tevatron on the direct searches for Standard Model Higgs boson produced in $p\bar{p}$ collisions at a center of mass energy of 1.96 TeV, using the data corresponding to the integrated luminosity of…
Efficient and robust optimization is essential for neural networks, enabling scientific machine learning models to converge rapidly to very high accuracy -- faithfully capturing complex physical behavior governed by differential equations.…
The CDF and D{\O} experiments at the Tevatron are currently the only capable of searching for the Standard Model Higgs boson. This article describes their most sensitive searches in the expected Higgs mass range, focusing on advanced…
Computing free energy is a fundamental problem in statistical physics. Recently, two distinct methods have been developed and have demonstrated remarkable success: the tensor-network-based contraction method and the neural-network-based…
The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been…
The application of Neural Networks in High Energy Physics to the separation of signal from background events is studied. A variety of problems usually encountered in this sort of analyses, from variable selection to systematic errors, are…
Search for single top quark and Higgs boson production is among the main goals for the upgraded Tevatron. We study the feasibility of single top quark and Higgs boson search at Tevatron both together since these processes have very similar…
The Nearest-Better Network (NBN) is a powerful method to visualize sampled data for continuous optimization problems while preserving multiple landscape features. However, the calculation of NBN is very time-consuming, and the extension of…
Convolutional Neural Networks (CNNs) have been successfully utilized in the medical diagnosis of many illnesses. Nevertheless, identifying the optimal architecture and hyperparameters among the available possibilities might be a substantial…
We summarize the status of Higgs boson searches at the upgraded Fermilab Tevatron performed by the DO and CDF collaborations. We report on three categories of searches, namely 1) the search for the Standard Model Higgs boson (p\bar{p} -->…
Neural networks are used extensively in classification problems in particle physics research. Since the training of neural networks can be viewed as a problem of inference, Bayesian learning of neural networks can provide more optimal and…
The use of neural networks for signal vs.~background discrimination in high-energy physics experiment has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top quark…
Vector boson fusion proposed initially as an alternative channel for finding heavy Higgs has now established itself as a crucial search scheme to probe different properties of the Higgs boson or for new physics. We explore the merit of…
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To address this issue, several highly flexible and scalable…
The sensitivity of Standard Model Higgs boson searches at the Tevatron experiments with a mass 135<MH<190 GeV using the channel H->WW->ll+MET (l=e,mu) is discussed. Three new event selections involving Higgs in association with one or two…