Related papers: Reconstructing jets in the Phase-2 upgrade of the …

The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual…

In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to…

ALICE (A Large Heavy Ion Experiment) is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online compute farm which reconstructs events measured by the ALICE detector in…

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning…

We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine-learning models are developed; namely the convolutional neural…

Accurate clustering of electromagnetic energy deposits is essential for reconstructing photons and electrons in modern hadron collider experiments, where boosted topologies and pileup cause overlapping showers and ambiguous energy…

The fortran version of the AcerDET package has been published in [1], and used in the multiple publications on the predictions for physics at LHC. The package provides, starting from list of particles in the event, the list of reconstructed…

We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics. The particle convolution layer can be viewed as an extension of Deep Sets and Energy Flow network…

The jet energy scale, jet energy resolution, and their systematic uncertainties are measured for jets reconstructed with the ATLAS detector in 2012 using proton-proton data produced at a centre-of-mass energy of 8 TeV with an integrated…

Full jet reconstruction in heavy-ion collisions is expected to provide more sensitive measurements of jet quenching in hot QCD matter at RHIC. In this paper we review recent studies of jets utilizing modern jet reconstruction algorithms and…

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods…

Positron Emission Tomography (PET) is an important molecular imaging tool widely used in medicine. Traditional PET systems rely on complete detector rings for full angular coverage and reliable data collection. However, incomplete-ring PET…

We propose a new global jet-finding algorithm for reconstructing two-prong objects like hadronic weak gauge bosons at a hadron collider. The selection of particles in a two-prong jet is required to maximize a $J_{E_T}^{\rm II}$ function,…

In many analyses in Higgs, top and electroweak physics, the kinematic reconstruction of the final state is improved by constrained fits. This is a particularly powerful tool at $e^{+}e^{-}$ colliders, where the initial state four-momentum…

The High-Luminosity LHC (HL-LHC) will usher in a new era in high-energy physics. The HL-LHC experimental conditions entail an instantaneous luminosity of up to $7.5 \times 10^{34}$ cm$^{-2}$ s$^{-1}$ and up to 200 simultaneous collisions…

Reconstructing charged particle tracks is a fundamental task in modern collider experiments. The unprecedented particle multiplicities expected at the High-Luminosity Large Hadron Collider (HL-LHC) pose significant challenges for track…

Single-image piece-wise planar 3D reconstruction aims to simultaneously segment plane instances and recover 3D plane parameters from an image. Most recent approaches leverage convolutional neural networks (CNNs) and achieve promising…

Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and application of convolutional neural networks with modified…

Object reconstruction from 3D point clouds has been a long-standing research problem in computer vision and computer graphics, and achieved impressive progress. However, reconstruction from time-varying point clouds (a.k.a. 4D point clouds)…

An improved algorithm is proposed for the reconstruction of singular connectivity from the available pairwise connections during preprocessing phase. To evaluate the performance of the algorithm, an in-house computational fluid dynamics…