The project of a Multi-TeV Muon Collider represents a unique opportunity to explore the high energy physics frontier and to measure with high precision the Higgs coupling with the other particles of the Standard Model as well as the trilinear and quadrilinear Higgs self-coupling, leading to a precise determination of the Higgs potential, in order to confirm the theoretical predictions of the SM and possibly to find evidences for new physics. One of the major challenges for the design and optimization of the technologies suitable for a Muon Collider experiment is represented by the high background induced by the decay of the muons coming from the beam. This contribution present the design of an innovative MPGD-based hadronic calorimeter (HCAL). The detector consists of a sampling calorimeter exploiting the Micro Pattern Gas Detectors (MPGDs) as active layers: MPGDs offer a fast and robust technology for high radiation environments and a high granularity for precise spatial measurements. Moreover, the detector is designed to optimize the jet reconstruction and for background suppression. The calorimeter is simulated using the Geant4 toolkit to support the detector R&D. The detector design and layout optimization supported by the simulation is described. 

Design and simulation of a MPGD-based hadronic calorimeter for Muon Collider

We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and electronics effects, events are simulated in which a single charged pion is shot at a homogenous lead glass calorimeter, split into longitudinal and transverse segments of varying size. As an approximation of an optimal reconstruction, a neural network-based energy regression is trained. The architecture is based on blocks of convolutional kernels customized for shower energy regression using local energy densities; biases at the edges of the training dataset are mitigated using a histogram technique. With this approximation, we find that a longitudinal and transverse segment size less than or equal to 0.5 and 1.3 nuclear interaction lengths, respectively, is necessary to achieve an optimal energy measurement. In addition, an intrinsic energy resolution of $8\%/\sqrt{E}$ for pion showers is observed.

Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks

The paper describes a novel approach for validation of Geant4 simulations at secondaries level using observables provided by highly granular calorimeters. The response of the highly granular scintillator-steel hadron calorimeter to negative pions with momenta from 10 to 80 GeV was simulated with the Geant4 package version 10.3. Several global observables, which characterise different aspects of hadronic shower development, are used as inputs for deep neural network. The network regression model is trained using the supervised learning and exploiting the true information from the simulations. The trained model is used to predict a number of neutrons and energy of neutral pions produced within a hadronic shower. The achieved performance of the model is demonstrated. 

/pdf/machine-learning-based-prediction-of-parameters-of-2fda5myq.pdf

Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables

Abstract Digital Hadronic Calorimeters (DHCAL) were suggested for future Colliders as part of the particle-flow concept. Though studied mainly with RPC, studies focusing on sampling elements based on Micro-Pattern Gaseous Detector have shown the potential advantages; they can be operated with environment-friendly gases and reach similar detection efficiency at lower average pad multiplicity. We summarize here the experimental test-beam results of a small-size DHCAL prototype, incorporating six Micromegas and two RPWELL sampling elements, interlaced with steel-absorber plates. It was investigated with 2–6 GeV pion beams at the CERN/PS beam facility. The data permitted validating a GEANT4 simulation framework of a DHCAL, and evaluating the expected pion energy resolution of a full-scale RPWELL-based calorimeter. The pion energy resolution derived for the RPWELL concept is competitive to that of glass RPC and Micromegas sampling techniques. 

Test-beam and simulation studies towards RPWELL-based DHCAL

The motivation for PF calorimetry is to experimentally measure the energyof hadron jets with excellent resolution. In particle flow designs, σ/E &amp;lt; 5%should be possible for a range of jet energies from ∼ 50 GeV to ∼ 250 GeV, im-portant particularly for experiments at electron-positron colliders (ILC, CLIC,FCCee, CEPC). The high granularity, which is essential for PF calorimetry, canalso be very beneficial for removal of background from pile-up on an event-by-event basis making such calorimeters an attractive approach for hadron colliderexperiments, for example the HGCAL under construction for CMS at the CERNHL-LHC. 

Particle Flow Calorimetry

We present a study of the response of the highly granular Digital Hadronic Calorimeter with steel absorbers, the Fe-DHCAL, to positrons , muons , and pions with momenta ranging from 2 to 60 GeV/c. Developed in the context of the CALICE collaboration, this hadron calorimeter utilises Resistive Plate Chambers as active media, interspersed with steel absorber plates. With a transverse granularity of 1 × 1 cm2 and a longitudinal segmentation of 38 layers, the calorimeter counted 350,208 readout channels, each read out with single-bit resolution (digital readout). The data were recorded in the Fermilab test beam in 2010–11. The analysis includes measurements of the calorimeter response and the energy resolution to positrons and muons, as well as detailed studies of various shower shape quantities. The results are compared to simulations based on Geant4 , which utilise different electromagnetic and hadronic physics lists.

/pdf/analysis-of-testbeam-data-of-the-highly-granular-rpc-steel-43p2gvxtkj.pdf

Analysis of testbeam data of the highly granular RPC-steel CALICE digital hadron calorimeter and validation of Geant4 Monte Carlo models

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Analysis of testbeam data of the highly granular RPC-steel CALICE digital hadron calorimeter and validation of Geant4 Monte Carlo models