Related papers: Energy reconstruction in a liquid argon calorimete…

Many of the interesting physics processes to be measured at the LHC have a signature involving one or more isolated electrons. The electron reconstruction and identification efficiencies of the ATLAS detector at the LHC have been evaluated…

A new method for calibrating the hadron response of a segmented calorimeter is developed. It is based on a principal component analysis of the calorimeter layer energy deposits, exploiting longitudinal shower development information to…

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We…

Hadron energy reconstruction for the Atlas barrel prototype combined calorimeter, consisting of the lead-liquid argon electromagnetic part and the iron-scintillator hadronic part, in the framework of the non-parametrical method has been…

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 Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon calorimeters enhances the physics reach of the experiment during the upcoming operation at increasing Large Hadron Collider luminosities. The new system, installed…

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has…

The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become increasingly difficult with the start of the…

The increased particle flux at the high luminosity phase of the Large Hadron Collider (HL-LHC), with instantaneous luminosities of up to 7.5 times the original design value, will have an impact on many sub-systems of the ATLAS detector.…

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…

The possibility to use Neural Networks for reconstruction of the energy deposited in the calorimetry system of the CMS detector is investigated. It is shown that using feed - forward neural network, good linearity, Gaussian energy…

This paper presents the electron and photon energy calibration achieved with the ATLAS detector using about 25 fb$^{-1}$ of LHC proton--proton collision data taken at centre-of-mass energies of $\sqrt{s}$ = 7 and 8 TeV. The reconstruction…

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…

ATLAS has chosen for its Hadronic End-Cap Calorimeter (HEC) the copper-liquid argon sampling technique with flat plate geometry and GaAs pre-amplifiers in the argon. The contruction of the calorimeter is now approaching completion. Results…

A module of the ATLAS electromagnetic barrel liquid argon calorimeter was exposed to the CERN electron test-beam at the H8 beam line upgraded for precision momentum measurement. The available energies of the electron beam ranged from 10 to…

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…

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino…

The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or Atlas experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used…

We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of…

We contrasted the performance of deep neural networks - Convolutional Neural Network (CNN) and Graph Neural Network (GNN) - to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This…