Exploitation of HPC Resources for data intensive sciences

The Large Hadron Collider (LHC) will enter a new phase beginning in 2027 with the upgrade to the High Luminosity LHC (HL-LHC). The increase in the number of simultaneous collisions coupled with a more complex structure of a single event will result in each LHC experiment collecting, storing, and processing exabytes of data per year. The amount of generated and/or collected data greatly outweighs the expected available computing resources. In this paper, we discuss effcient usage of HPC resources as a prerequisite for data-intensive science at exascale. First, we discuss the experience of porting CMS Hadron and Electromagnetic calorimeters reconstruction code to utilize Nvidia GPUs within the DEEP-EST project; second, we look at the tools and their adoption in order to perform benchmarking of a variety of resources available at HPC centers. Finally, we touch on one of the most important aspects of the future of HEP - how to handle the flow of petabytes of data to and from computing facilities, be it clouds or HPCs, for exascale data processing in a flexible, scalable and performant manner. These investigations are a key contribution to technical work within the HPC collaboration among CERN, SKA, GEANT and PRACE.

Investigation of the Light Yield Distribution in LYSO Crystals by the Optical Spectroscopy Method for the Electromagnetic Calorimeters of the COMET Experiment

The distribution of the light yield and luminescence intensity along LYSO:Ce crystal length is investigated. These distributions, determined by different defects and emission centers of the dopant in the crystalline structure and its distribution along the length, is measured by two methods: the gamma spectroscopy using radiation sources and the optical spectroscopy using ultraviolet sources. It is shown that crystals have considerable variation of the light yield and luminescence intensity both over the length of an individual crystal (in the growing direction) and for different crystals. It is established that the correction factors for the segmented calorimeter of the COMET experiment can be obtained using optical spectroscopy methods. Consideration of the correction factors will significantly reduce an error of energy measurement in a segmented calorimeter during data handling.

Calorimeters

The determination of the energy of particles is called ‘calorimetry’ and the corresponding detectors are called calorimeters. The particle energy is deposited in a calorimeter through inelastic reactions leading to the formation of particle showers. The deposited energy is measured either through the charge generated by ionisation or through scintillation or Cherenkov light. Depending on the particle type initiating a shower one distinguishes electromagnetic calorimeters from hadronic calorimeters. In this chapter the formation of showers for both cases is explained and the corresponding construction principles are discussed. For hadron calorimeters special attention is given to the different response to electromagnetically and hadronically deposited energy and the possible compensation of invisible energy. This is followed by a description of typical implementations of electromagnetic and hadronic calorimeters as well as of systems combining both types. Special emphasis is given to the discussion of the energy resolution of the different detectors and detector systems.

Machine Learning based Global Particle Identification Algorithms at the LHCb Experiment

One of the most important aspects of data processing at flavor physics experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging Cherenkov detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. The charged PID based on the sub-detectors response is considered as a machine learning problem solved in different modes: one-vs-rest, one-vs-one and multi-classification, which affect the models training and prediction. To improve charged particle identification for pions, kaons, protons, muons and electrons, neural network and gradient boosting models have been tested. This paper presents these models and their performance evaluated on Run 2 data and simulation samples. A discussion of the performances is also presented.

Silicon Calorimeters

We review the development of silicon-based calorimeters from the very first applications of small calorimeters used in collider experiments to the large-scale systems that are being designed today. We discuss silicon-based electromagnetic calorimeters for future e− e+ colliders and for the upgrade of the CMS experiment's endcap calorimeter to be used in the high-luminosity phase of the LHC. We present the intrinsic advantages of silicon as an active detector material and highlight the enabling technologies that have made calorimeters with very high channel densities feasible. We end by discussing the outlook for further extensions to the silicon calorimeter concept, such as calorimeters with fine-pitched pixel detectors.