Related papers: Hadron Identification Prospects With Granular Calo…
Correctly identifying the nature and properties of outgoing particles from high energy collisions at the Large Hadron Collider is a crucial task for all aspects of data analysis. Classical calorimeter-based classification techniques rely on…
We present a new approach to identification of boosted neutral particles using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The identification of photons and neutral pions is currently based on the geometric parameters which…
Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to…
This study introduces chromatic calorimetry, a novel particle detection method that uses strategically layered scintillators with different emission wavelengths. This approach aims to enhance energy measurement by capturing particle…
Distinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider physics data. We explore whether there is information…
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find new solutions for the detection, identification, and measurement of final-state…
Prototypes of electromagnetic and hadronic imaging calorimeters developed and operated by the CALICE collaboration provide an unprecedented wealth of highly granular data of hadronic showers for a variety of active sensor elements and…
Using a combination of a preshower detector and a charged particle veto, it is shown that the neural network method is able to provide satisfactory discrimination between photons and hadrons in the case of extremely high particle density…
This work is dedicated to the study of a technique for hadron identification in the TeV momentum range, based on the simultaneous measurement of the energies and of the emission angles of the Transition Radiation (TR) X-rays with respect to…
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most computationally expensive simulations involve calorimeter…
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The…
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…
The software compensation algorithms developed for the CALICE Analog Hadron Calorimeter are extended to incorporate time information on the cell level, and the performance is studied in GEANT4 simulations with a detector model of a…
At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet…
Jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and…
This paper presents results obtained with the combined CALICE Scintillator Electromagnetic Calorimeter, Analogue Hadronic Calorimeter and Tail Catcher & Muon Tracker, three high granularity scintillator-SiPM calorimeter prototypes. The…
A new method for calibrating the hadron response of a segmented calorimeter is developed and successfully applied to beam test data. It is based on a principal component analysis of energy deposits in the calorimeter layers, exploiting…
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…
Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between…
Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based…