Related papers: Machine learning-based jet and event classificatio…
Heavy flavor production at the future Electron-Ion Collider (EIC) will allow us to precisely determine the quark/gluon fragmentation processes in vacuum and the nuclear medium especially within the poorly constrained kinematic region. Heavy…
The future Electron-Ion Collider (EIC) will operate a series of high-luminosity high-energy electron+proton ($e+p$) and electron+nucleus ($\textit{e + A}$) collisions to study several fundamental questions in the high energy and nuclear…
We study nonperturbative flavor correlations between pairs of leading and next-to-leading charged hadrons within jets at the Electron-Ion Collider (EIC). We introduce a charge correlation ratio observable $r_c$ that distinguishes same- and…
We explore the potential of jet observables in charged-current deep-inelastic scattering (CC DIS) events at the future Electron-Ion Collider (EIC). Tagging jets with a recoiling neutrino, which can be identified by the event's missing…
Heavy-flavor hadron production, in particular bottom hadron production, is difficult to study in deep-inelastic scattering (DIS) experiments due to small production rates and branching fractions. To overcome these limitations, a method for…
The future Electron-Ion Collider will utilize high-luminosity high-energy electron+proton ($e+p$) and electron+nucleus ($e+A$) collisions to solve several fundamental questions in the high energy nuclear physics field. Heavy flavor products…
The proposed high luminosity high energy Electron-Ion Collider (EIC) will explore the proton/nuclear structure in a wide Bjorken-x ($x_{BJ}$) and $Q^{2}$ phase space. Heavy flavor products are generated in initial collisions and have their…
The ECCE detector has been recommended as the selected reference detector for the future Electron-Ion Collider (EIC). A series of simulation studies have been carried out to validate the physics feasibility of the ECCE detector. In this…
In this work, we present an overview of experimental considerations relevant to the utilization of jets at a future Electron-Ion Collider (EIC), a subject which has been largely overlooked up to this point. A comparison of jet-finding…
The future Electron-Ion Collider (EIC), which is expected to start construction at Brookhaven National Laboratory in 2025, will utilize high-luminosity high-energy electron+proton and electron+nucleus collisions to explore several…
A U.S.-based Electron-Ion Collider will provide the ultimate capability to determine both the structure and properties of nucleons and nuclei, as well as how matter and energy can be transported through a strongly interacting quantum…
We discuss the prospects of using jets as precision probes in electron-nucleus collisions at the future Electron-Ion Collider. Jets produced in deep-inelastic scattering can be calibrated by a measurement of the scattered electron. Such…
Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning…
Jet flavour identification in multi-TeV e+e- collisions is expected to provide insights on new phenomena at scales beyond those probed by the LHC. The anticipated high track density and jet collimation represent a new challenge to jet…
Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle…
The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review…
Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information…
Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling…
Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context…
Over the last years, machine learning tools have been successfully applied to a wealth of problems in high-energy physics. A typical example is the classification of physics objects. Supervised machine learning methods allow for significant…