Muon Energy Measurement from Radiative Losses in a Calorimeter for a Collider Detector

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 particles in subnuclear collisions. One such challenge is the precise measurement of muon momenta at very high energy, where the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient to achieve the desired resolution. In this work we show the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by the use of the spatial information of the clusters of deposited photon energy in the regression task. Using a homogeneous lead-tungstate calorimeter as a benchmark, we show how energy losses may provide significant complementary information for the estimate of muon energies above 1 TeV.Comment: 20 pages, 12 figure

Jet Flavour Classification Using DeepJet

Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern deep learning techniques. This new model, called DeepJet, overcomes the limitations in input size that affected previous approaches. As a result, the heavy flavour classification performance improves, and the model is extended to also perform quark-gluon tagging.Comment: 14 pages, 9 figures, accepted for publication in JINS

Isothermal annealing of radiation defects in bulk material of diodes from 8" silicon wafers

The high luminosity upgrade of the LHC will provide unique physics opportunities, such as the observation of rare processes and precision measurements. However, the accompanying harsh radiation environment will also pose unprecedented challenged to the detector performance and hardware. In this paper, we study the radiation induced damage and its macroscopic isothermal annealing behaviour of the bulk material from new 8" silicon wafers using diode test structures. The sensor properties are determined through measurements of the diode capacitance and leakage current for three thicknesses, two material types, and neutron fluences from $6.5\cdot 10^{14}$ to $10^{16}\,\mathrm{neq/cm^2}$.Comment: 15 pages, 11 Figure

Isothermal annealing of radiation defects in silicon bulk material of diodes from 8” silicon wafers

The high luminosity upgrade of the LHC will provide unique physics opportunities, such as the observation of rare processes and precision measurements. However, the accompanying harsh radiation environment will also pose unprecedented challenged to the detector performance and hardware. In this paper, we study the radiation induced damage and its macroscopic isothermal annealing behaviour of the bulk material from new 8" silicon wafers using diode test structures. The sensor properties are determined through measurements of the diode capacitance and leakage current for three thicknesses, two material types, and neutron fluences from 6.5 · 10$^{14}$ to 1 · 10$^{16}$ n$_{eq}$/cm$^2$

End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of O(1000) particles in high-luminosity conditions with 200 pileup. © 2022, The Author(s)

Calorimeters for the FCC-hh

The future proton-proton collider (FCC-hh) will deliver collisions at a center of mass energy up to $\sqrt{s}=100$ TeV at an unprecedented instantaneous luminosity of $L=3~10^{35}$ cm$^{-2}$s$^{-1}$, resulting in extremely challenging radiation and luminosity conditions. By delivering an integrated luminosity of few tens of ab$^{-1}$, the FCC-hh will provide an unrivalled discovery potential for new physics. Requiring high sensitivity for resonant searches at masses up to tens of TeV imposes strong constraints on the design of the calorimeters. Resonant searches in final states containing jets, taus and electrons require both excellent energy resolution at multi-TeV energies as well as outstanding ability to resolve highly collimated decay products resulting from extreme boosts. In addition, the FCC-hh provides the unique opportunity to precisely measure the Higgs self-coupling in the di-photon and b-jets channel. Excellent photon and jet energy resolution at low energies as well as excellent angular resolution for pion background rejection are required in this challenging environment. This report describes the calorimeter studies for a multi-purpose detector at the FCC-hh. The calorimeter active components consist of Liquid Argon, scintillating plastic tiles and Monolithic Active Pixel Sensors technologies. The technological choices, design considerations and achieved performances in full Geant4 simulations are discussed and presented. The simulation studies are focused on the evaluation of the concepts. Standalone studies under laboratory conditions as well as first tests in realistic FCC-hh environment, including pileup rejection capabilities by making use of fast signals and high granularity, have been performed. These studies have been performed within the context of the preparation of the FCC conceptual design reports (CDRs)

Particle-flow reconstruction and global event description with the CMS detector

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)