Abstract
To sustain the harsher conditions of the high-luminosity LHC, the CMS collaboration is designing a novel endcap calorimeter system. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly-granular information in the readout to help mitigate the effects of pileup. In regions characterised by lower radiation levels, small scintillator tiles with individual on-tile SiPM readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigate pileup in the very dense environment of HL-LHC. The inputs to the framework are clusters of energy deposited in individual calorimeter layers. Clusters are formed by a density-based algorithm. Recent developments and tunes of the clustering algorithm will be presented. To help reduce the expected pressure on the computing resources in the HL-LHC era, the algorithms and their data structures are designed to be executed on GPUs. Preliminary results will be presented on decreases in clustering time when using GPUs versus CPUs. Ideas for machine-learning techniques to further improve the speed and accuracy of reconstruction algorithms will be presented.
Highlights
The significant increase in the instantaneous and integrated luminosity comes at the price of almost an order of magnitude increase in the number of multiple proton-proton collisions in the same or neighbouring bunch crossings, and the significant increase of the radiation levels
One of the fundamental ingredients of the high granularity calorimeter (HGCAL) reconstruction is the collection of RecHits in the same HGCAL layer that originate from the same particle, broadly known as clustering, to form the “Layer Clusters” (LC)
The tracksters produced by The Iterative CLustering" (TICL) are used as inputs to a Convolutional Neural Network (CNN): each trackster is represented as a three-dimensional image of 50 × 10 × 3, where each dimension represents the number of HGCAL layers per endcap, the maximum number of LC on each layer ordered by decreasing energy, and the number of features of each LC
Summary
The significant increase in the instantaneous and integrated luminosity comes at the price of almost an order of magnitude increase in the number of multiple proton-proton collisions in the same or neighbouring bunch crossings (referred to as pileup), and the significant increase of the radiation levels Both of these effects pose major challenges for the experiments, which need to be upgraded to cope with the harsher data taking conditions. Taking all these motivations under consideration, the most promising detector upgrade is an imaging calorimeter with very fine lateral and longitudinal segmentation, complemented by precision timing capabilities. In regions with lower radiation levels, small plastic scintillator tiles with individual SiPMs readout are employed
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