相关论文: Jet energy calibration with deep learning as a Kub…

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also…

In recent years, energy correlators have emerged as a powerful tool for studying jet substructure, with promising applications such as probing the hadronization transition, analyzing the quark-gluon plasma, and improving the precision of…

We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise…

Jet production in PbPb collisions at a nucleon-nucleon center-of-mass energy of 2.76 TeV was studied with the CMS detector at the LHC, using a data sample corresponding to an integrated luminosity of 6.7 inverse microbarns. Jets are…

We introduce the energy flow polynomials: a complete set of jet substructure observables which form a discrete linear basis for all infrared- and collinear-safe observables. Energy flow polynomials are multiparticle energy correlators with…

Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced in high-energy physics collisions. We train neural networks…

Jet substructure observables are crucial for exploring the effects of the hot, dense medium and differentiating between quark and gluon jets. In this paper, we investigate the modification of jet shape by calculating the differential girth…

The Mueller-Navelet di-jet production process represents an ultimate testfield of pQCD in the high-energy limit. Several experimental analyses carried out so far are in good agreement with theoretical predictions, based on collinear…

The particle flow approach to calorimetry can provide unprecedented jet energy resolution at a future high energy collider, such as the International Linear Collider (ILC). However, the use of particle flow calorimetry at the proposed…

Jet quenching has been established as one of the main tools to study the properties of the medium produced in heavy ion collisions. Most of the experimental effort has been, up to now, on the measurements of inclusive particle suppression.…

Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical…

Accurate jet charge identification is essential for precise electroweak and flavor measurements at the high-energy frontier. We propose a novel method called the Leading Particle Jet Charge method (LPJC) to determine the jet charge based on…

The CMS experiment makes use of a large variety of algorithms to identify the origin of particle jets measured in the detector. Through the study of jet substructure properties, jets originating from quarks, gluons, W/Z/Higgs bosons, top…

We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the…

We confront a hybrid strong/weak coupling model for jet quenching to data from LHC heavy ion collisions. The model combines the perturbative QCD physics at high momentum transfer and the strongly coupled dynamics of non- abelian gauge…

We use public data from the CMS experiment to study the 2-prong substructure of jets. The CMS Open Data is based on 31.8/pb of 7 TeV proton-proton collisions recorded at the Large Hadron Collider in 2010, yielding a sample of 768,687 events…

Very soon the LHC will provide beams for heavy ion collisions at 5.52 TeV/nucleon. This center-of-mass energy results in a large cross-section for producing high-$E_T$ ($>$ 50 GeV) jets that are distinct from the soft, underlying event.…

Jet Flavor Tomography is a powerful tool used to probe the properties of Quark Gluon Plasma formed in heavy ion collisions at RHIC and LHC. A new Monte Carlo model of jet quenching developed at Columbia University, CUJET, was applied to…

Energy correlators that describe energy-weighted distances between two or three particles in a jet are measured using an event sample of $\sqrt{s}$ = 13 TeV proton-proton collisions collected by the CMS experiment and corresponding to an…

Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC), where it has provided numerous innovative new ways to search for new physics and to probe the Standard Model in extreme regions of phase space. In this…