Related papers: Spectral Clustering for Jet Reconstruction
We compare the abilities of the cluster-type jet algorithm, KtJet, and a mid-point iterating cone algorithm to reconstruct the top mass at the LHC. We discuss the information contained in the merging scales of cluster-type algorithms, and…
Spectral clustering is a powerful unsupervised machine learning algorithm for clustering data with non convex or nested structures. With roots in graph theory, it uses the spectral properties of the Laplacian matrix to project the data in a…
A new class of jet clustering algorithms is introduced. A criterion inspired by successful mass-drop taggers is applied that prevents the recombination of two hard prongs if their combined jet mass is substantially larger than the masses of…
Spectral Clustering is a popular technique to split data points into groups, especially for complex datasets. The algorithms in the Spectral Clustering family typically consist of multiple separate stages (such as similarity matrix…
This paper presents a new method of constructing physical models in a geophysical inverse problem, when there are only a few possible physical property values in the model and they are reasonably known but the geometry of the target is…
Experimental High-Energy Physics (HEP), especially the Large Hadron Collider (LHC) programme at the European Organization for Nuclear Research (CERN), is one of the most computationally intensive activities in the world. This demand is set…
We introduce a new jet-finding algorithm for a hadron collider based on maximizing a J_{E_T} function for all possible combinations of particles in an event. This function prefers a larger value of the jet transverse energy and a smaller…
We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element - parton shower matching for large jet multiplicity, and efficient event generation of jets in complex,…
We develop a novel wave imaging scheme for reconstructing the shape of an inhomogeneous scatterer and we consider the inverse acoustic obstacle scattering problem as a prototype model for our study. There exists a wealth of reconstruction…
Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph. This non-linear transformation of the data is both the key of…
We examine collider physics via the spectral power H_l, which utilizes much more detector information than sequential jet reconstruction. We use H_l to define a novel jet definition and use it to reconstruct both e+e- > qqg and e+e > tt…
This article describes the FFTJet software package designed to perform jet reconstruction in the analysis of high energy physics experimental data. A two-stage approach is adopted in which pattern recognition is performed first, utilizing…
There has recently been much interest in analytical computations of jet mass distributions with and without vetos on additional jet activity [1-6]. An important issue affecting such calculations, particularly at next-to-leading logarithmic…
The study of standard QCD jets produced along with fat jets, which may appear as a result of the decay of a heavy particle, has become an essential part of collider studies. Current jet clustering algorithms, which use a fixed radius…
Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. In this paper, we find that the spectral graph theory…
Spectral clustering methods have gained widespread recognition for their effectiveness in clustering high-dimensional data. Among these techniques, constrained spectral clustering has emerged as a prominent approach, demonstrating enhanced…
We define jet transition values for the anti-$k_{\bot}$ algorithm for both hadron and $e^+e^-$ colliders. We show how these transition values can be computed and how they can be used to improve the performance of clusterization when jet…
Subjet distributions were measured in neutral current deep inelastic ep scattering with the ZEUS detector at HERA using an integrated luminosity of 81.7 pb-1. Jets were identified using the kT cluster algorithm in the laboratory frame.…
We focus on spectral clustering of unlabeled graphs and review some results on clustering methods which achieve weak or strong consistent identification in data generated by such models. We also present a new algorithm which appears to…
Measurements of strong suppression of inclusive hadron distributions and di-hadron correlations at high $p_{T}$, while providing evidence for partonic energy loss, also suffer from geometric biases due to the competition of energy loss and…