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Register a new userOptimizing the Performance of a High-Granularity Silicon-Pad EM Calorimeter
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A silicon-based fine granularity calorimeter is a potential technology for the future International Linear Collider ILC, the future circular collider CEPC, and is also the chosen technology for the upgraded CMS experiment of the Large Hadron Collider. Active silicon sensing pads are used as MIP counters and the standard calibration of the calorimeter uses weights based on the average energy loss, $dEdx$. In this work, the limitations of the dEdx calibration method in terms of energy linearity, scale and resolution are explored. In the case of a calorimeter with varying passive layer thickness as the one planned for CMS, the $dEdx$ method leads to a significant constant term in the resolution function and a non-linearity of energy response. For these reasons, a method based on the calorimeter sampling fraction that exploits the per-event measured shower depth is presented and shown to deliver superior absolute energy scale, linearity and resolution. Calorimetric designs in which the back of the shower is sampled less, offer reduced cost without loss in performance. Therefore, a proper calibration as proposed here is crucial in obtaining the most cost- and performance-effective silicon-sampling calorimeter design.
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The replacement of the existing endcap calorimeter in the Compact Muon Solenoid (CMS) detector for the high-luminosity LHC (HL-LHC), scheduled for 2027, will be a high granularity calorimeter. It will provide detailed position, energy, and timing information on electromagnetic and hadronic showers in the immense pileup of the HL-LHC. The High Granularity Calorimeter (HGCAL) will use 120-, 200-, and 300-$mutextrm{m}$ thick silicon (Si) pad sensors as the main active material and will sustain 1-MeV neutron equivalent fluences up to about $10^{16}~textrm{n}_textrm{eq}textrm{cm}^{-2}$. In order to address the performance degradation of the Si detectors caused by the intense radiation environment, irradiation campaigns of test diode samples from 8-inch and 6-inch wafers were performed in two reactors. Characterization of the electrical and charge collection properties after irradiation involved both bulk polarities for the three sensor thicknesses. Since the Si sensors will be operated at -30 $^circ$C to reduce increasing bulk leakage current with fluence, the charge collection investigation of 30 irradiated samples was carried out with the infrared-TCT setup at -30 $^circ$C. TCAD simulation results at the lower fluences are in close agreement with the experimental results and provide predictions of sensor performance for the lower fluence regions not covered by the experimental study. All investigated sensors display 60$%$ or higher charge collection efficiency at their respective highest lifetime fluences when operated at 800 V, and display above 90$%$ at the lowest fluence, at 600 V. The collected charge close to the fluence of $10^{16}~textrm{n}_textrm{eq}textrm{cm}^{-2}$ exceeds 1 fC at voltages beyond 800 V.
A new design of highly granular hadronic calorimeter using Glass Resistive Plate Chambers (GRPCs) with embedded electronics has been proposed for the future International Linear Collider (ILC) experiments. It features a 2-bit threshold semi-digital read-out. Several GRPC prototypes with their electronics have been successfully built and tested in pion beams. The design of these detectors is presented along with the test results on efficiency, pad multiplicity, stability and reproducibility.
An imaging calorimeter has been designed and is being built for the PAMELA satellite-borne experiment. The physics goals of the experiment are the measurement of the flux of antiprotons, positrons and light isotopes in the cosmic radiation. The calorimeter is designed to perform a precise measurement of the total energy deposited, to reconstruct the spatial development of the showers (both in the longitudinal and in the transverse directions), and to measure the energy distribution along the shower itself. From this information, the calorimeter will identify antiprotons from a electron background and positrons in a background of protons with an efficiency of about 95% and a rejection power better than 10^-4. Furthermore, a self-trigger system has been implemented with the calorimeter that will be employed to measure high-energy (from about 300 GeV to more than 1 TeV) electrons. The instrument is composed of 22 layers of tungsten, each sandwiched between two views of silicon strip detectors (X and Y). The signals are read out by a custom VLSI front-end chip, the CR1.4P, specifically designed for the PAMELA calorimeter, with a dynamic range of 7.14 pC or 1400 mip (minimum ionizing particle). We report on the simulated performance and prototype design.
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 response of the system to pions with momenta between 4 GeV/c and 32 GeV/c is analysed, including the energy response, resolution, and longitudinal shower profiles. The results of a software compensation technique based on weighting according to hit energy are compared to those of a standard linear energy reconstruction. The results are compared to predictions of the GEANT4 physics lists QGSP_BERT_HP and FTFP_BERT_HP.
We present the 3DGAN for the simulation of a future high granularity calorimeter output as three-dimensional images. We prove the efficacy of Generative Adversarial Networks (GANs) for generating scientific data while retaining a high level of accuracy for diverse metrics across a large range of input variables. We demonstrate a successful application of the transfer learning concept: we train the network to simulate showers for electrons from a reduced range of primary energies, we then train further for a five times larger range (the model could not train for the larger range directly). The same concept is extended to generate showers for other particles (photons and neutral pions) depositing most of their energies in electromagnetic interactions. In addition, the generation of charged pion showers is also explored, a more accurate effort would require additional data from other detectors not included in the scope of the current work. Our further contribution is a demonstration of using GAN-generated data for a practical application. We train a third-party network using GAN-generated data and prove that the response is similar to a network trained with data from the Monte Carlo simulation. The showers generated by GAN present accuracy within $10%$ of Monte Carlo for a diverse range of physics features, with three orders of magnitude speedup. The speedup for both the training and inference can be further enhanced by distributed training.
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