Related papers: Deep learning techniques for energy clustering in …
Chromatic calorimetry (CCAL) analyses particle detection by utilizing scintillators with distinct emission wavelengths to measure the longitudinal energy deposition of particle showers in high-energy physics, improving particle…
The understanding of the reconstruction and calibration of electrons and photons is one of the key steps at the start-up of data-taking with ATLAS at the LHC (Large Hadron Collider). The calorimeter cells are electronically calibrated…
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…
The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and…
The TileCal is the main hadronic calorimeter of ATLAS and it covers the central part of the detector ($|\eta|$ < 1.6). The energy deposited by the particles in TileCal is read out by approximately 10,000 channels. The signal provided by the…
The operation and performance of the Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) are presented, based on data collected in pp collisions at $\sqrt{s}$ = 13 TeV at the CERN LHC, in the years from 2015 to 2018 (LHC Run 2),…
In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the…
Pions constitute nearly $70\%$ of final state particles in ultra high energy collisions. They act as a probe to understand the statistical properties of Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in such…
A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are…
The CALICE Semi-Digital Hadronic CALorimeter (SDHCAL) is the first technological prototype in a family of high-granularity calorimeters developed by the CALICE Collaboration to equip the experiments of future lepton colliders. The SDHCAL is…
Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at…
Photon reconstruction in calorimeters represents a crucial challenge in particle physics experiments, especially in high-density environments where shower overlapping probabilities become significant. We present an energy-core-based photon…
We present a new publicly available dataset that contains simulated data of a novel calorimeter to be installed at the CERN Large Hadron Collider. This detector will have more than six-million channels with each channel capable of position,…
The CMS electromagnetic calorimeter (ECAL) is a high-resolution, hermetic, and homogeneous calorimeter made of 75,848 scintillating lead tungstate crystals. Following the discovery of the Higgs boson, the CMS ECAL is at the forefront of…
In 2017, the LHC (Large Hadron Collider) at CERN has obtained an astonishing result providing proton-proton collisions with an integrated luminosity of 50.25 fb-1. The CMS (Compact Muon Solenoid), a general-purpose detector operating at the…
The CALICE collaboration has constructed highly granular electromagnetic and hadronic calorimeter prototypes to evaluate technologies for the use in detector systems at the future International Linear Collider. These calorimeters have been…
A description is provided of the performance of the CMS detector for photon reconstruction and identification in proton-proton collisions at a centre-of-mass energy of 8 TeV at the CERN LHC. Details are given on the reconstruction of…
We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each…
The High Luminosity LHC (HL-LHC) will integrate 10 times more luminosity than the LHC, posing significant challenges for radiation tolerance and event pileup on detectors, especially for forward calorimetry, and hallmarks the issue for…
Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a…