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Real-time data processing is a central aspect of particle physics experiments with high requirements on computing resources. The LHCb (Large Hadron Collider beauty) experiment must cope with the 30 million proton-proton bunches collision per second rate of the Large Hadron Collider (LHC), producing 109 particles/s. The large input data rate of 32 Tb/s needs to be processed in real time by the LHCb trigger system, which includes both reconstruction and selection algorithms to reduce the number of saved events. The trigger system is implemented in two stages and deployed in a custom data centre.We present Looking Forward, a high-throughput track following algorithm designed for the first stage of the LHCb trigger and optimised for GPUs. The algorithm focuses on the reconstruction of particles traversing the whole LHCb detector and is developed to obtain the best physics performance while respecting the throughput limitations of the trigger. The physics and computing performances are discussed and validated with simulated samples.

Based on the quality control diagnostics of the used base layers on the interstation railway section Palárikovo - Nové Zámky, the article analyzes the achieved results of static load tests that were carried out on the plane of the sub-ballast upper surface. The verification of the values is based on the project documentation, in which, on the basis of a previously performed geotechnical survey, the designer proposed a suitable type of structural sub-ballast layers. The design of the structural sub-ballast layers reflects the expected deformation resistance of the subgrade surface, its water regime, the railway line characteristic (speed zone), the type of construction work (reconstruction) and also takes into account the principle of conservation of natural resources (recycling of recovered ballast bed material).

A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase. The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies. [ABSTRACT FROM AUTHOR]

In high-energy particle collisions, charged track finding is a complex yet crucial endeavor. We propose a quantum algorithm, specifically quantum template matching, to enhance the accuracy and efficiency of track finding. Abstracting the Quantum Amplitude Amplification routine by introducing a data register, and utilizing a novel oracle construction, allows data to be parsed to the circuit and matched with a hit-pattern template, without prior knowledge of the input data. Furthermore, we address the challenges posed by missing hit data, demonstrating the ability of the quantum template matching algorithm to successfully identify charged-particle tracks from hit patterns with missing hits. Our findings therefore propose quantum methodologies tailored for real-world applications and underline the potential of quantum computing in collider physics.

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue 'Quantum technologies in particle physics'.

This paper introduces methods for parallelizing the algorithm to enhance the efficiency of event recovery in Spin Physics Detector (SPD) experiments at the Nuclotron-based Ion Collider Facility (NICA). The problem of eliminating false tracks during the particle trajectory detection process remains a crucial challenge in overcoming performance bottlenecks in processing collider data generated in high volumes and at a fast pace. In this paper, we propose and show fast parallel false track elimination methods based on the introduced criterion of a clustering-based thresholding approach with a chi-squared quality-of-fit metric. The proposed strategy achieves a good trade-off between the effectiveness of track reconstruction and the pace of execution on today's advanced multicore computers. To facilitate this, a quality benchmark for reconstruction is established, using the root mean square (rms) error of spiral and polynomial fitting for the datasets identified as the subsequent track candidate by the neural network. Choosing the right benchmark enables us to maintain the recall and precision indicators of the neural network track recognition performance at a level that is satisfactory to physicists, even though these metrics will inevitably decline as the data noise increases. Moreover, it has been possible to improve the processing speed of the complete program pipeline by 6 times through parallelization of the algorithm, achieving a rate of 2000 events per second, even when handling extremely noisy input data. [ABSTRACT FROM AUTHOR]

In high-energy physics experiments, the trajectories of charged particles are reconstructed using track reconstruction algorithms. Such algorithms need to both identify the set of measurements from a single charged particle and to fit the parameters by propagating tracks along the measurements. The propagation of the track parameter uncertainties is an important component in the track fitting to get the optimal precision in the fitted parameters. The error propagation is performed at intersections between the track and local coordinate frames defined on detector components by calculating a Jacobian matrix corresponding to the local-to-local frame transport. This paper derives the Jacobian matrix in a general manner to harmonize with numerical integration methods developed for inhomogeneous magnetic fields and materials. The Jacobian and transported covariance matrices are validated by simulating the propagation of charged particles between two frames and comparing with the results of numerical methods. [ABSTRACT FROM AUTHOR]

This paper introduces methods for parallelizing the algorithm to enhance the efficiency of event recovery in Spin Physics Detector (SPD) experiments at the Nuclotron-based Ion Collider Facility (NICA). The problem of eliminating false tracks during the particle trajectory detection process remains a crucial challenge in overcoming performance bottlenecks in processing collider data generated in high volumes and at a fast pace. In this paper, we propose and show fast parallel false track elimination methods based on the introduced criterion of a clustering-based thresholding approach with a chi-squared quality-of-fit metric. The proposed strategy achieves a good trade-off between the effectiveness of track reconstruction and the pace of execution on today’s advanced multicore computers. To facilitate this, a quality benchmark for reconstruction is established, using the root mean square (rms) error of spiral and polynomial fitting for the datasets identified as the subsequent track candidate by the neural network. Choosing the right benchmark enables us to maintain the recall and precision indicators of the neural network track recognition performance at a level that is satisfactory to physicists, even though these metrics will inevitably decline as the data noise increases. Moreover, it has been possible to improve the processing speed of the complete program pipeline by 6 times through parallelization of the algorithm, achieving a rate of 2000 events per second, even when handling extremely noisy input data.

As a part of the future upgrade program of the Multi-Purpose Detector (MPD) experiment at the Nuclotron-Based Ion Collider Facility (NICA) complex, an Inner Tracking System (ITS) made of Monolitic Active Pixel Sensors (MAPSs) is proposed between the beam pipe and the Time Projection Chamber (TPC). It is expected that the new detector will enhance the experimental potential for the reconstruction of short-lived particles—in particular, those containing the open charm particle. To study the detector performance and select its best configuration, a track reconstruction approach based on a constrained combinatorial search was developed and implemented as a software toolkit called Vector Finder. This paper describes the proposed approach and demonstrates its characteristics for primary and secondary track finding in ITS, ITS-to-TPC track matching and hyperon reconstruction within the MPD software framework. The results were obtained on a set of simulated central gold–gold collision events at sNN=9 GeV with an average multiplicity of ∼1000 charged particles in the detector acceptance produced with the Ultra-Relativistic Quantum Molecular Dynamics (UrQMD) generator.

MeV Gamma-ray Telescope (MGT) is a conceptual mission aimed at improving the detection sensitivity of gamma-ray astronomy in the MeV energy range. It consists of three sub-detectors: Gamma-ray Conversion silicon tracker, CALOrimeter and Anti-Coincident Detector. In this paper, a track reconstruction algorithm based on Convolutional Neural Networks (CNN) is developed for MGT. In order to train and test the model, Geant4 simulation is used and generates a large number of gamma-ray events at nine energy points in the energy band from 0.5 GeV to 10 GeV. Finally, the reconstruction results of angular resolution, position resolution and acceptance are shown. The testing results indicate that the angular resolution of MGT significantly improves in the 0. 5 ∼ 10 GeV range compared with Fermi-LAT. [ABSTRACT FROM AUTHOR]

Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality. The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods. The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads. The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality. [ABSTRACT FROM AUTHOR]

Real-time particle track reconstruction in high-energy physics experiments at colliders running at high luminosity is very challenging for trigger systems. To perform pattern recognition and track fitting in online trigger system, the artificial Retina algorithm has been introduced in the field. Retina can be implemented in the state-of-the-art field-programmable gate array (FPGA) devices. Our developments use Retina in an iterative way to identify tracks in a barrel-shaped tracker embedded in a high magnetic field and with high track multiplicity. As a benchmark, we simulate LHC t-tbar events from 14-TeV proton–proton collisions, with a pile-up of 200 interactions. The produced particles are propagated using GEANT-4 in a 4-T magnetic field from the interaction point through a six-layer barrel tracker made of silicon modules. With this sample, the performance of the hardware design [FPGA resource usage and latency] is evaluated. Both track reconstruction efficiency and purity of the Retina track finding are over 90%. To improve further the resolution on the track parameters, we are investigating the addition of a Kalman filter process on the FPGA, after the Retina step. First results obtained with an emulator of the Kalman filter are also discussed in this article. [ABSTRACT FROM AUTHOR]

Real-time data processing is one of the central processes of particle physics experiments which require large computing resources. The LHCb (Large Hadron Collider beauty) experiment will be upgraded to cope with a particle bunch collision rate of 30 million times per second, producing 109 particles/s. 40 Tbits/s need to be processed in real-time to make filtering decisions to store data. This poses a computing challenge that requires exploration of modern hardware and software solutions. We present Compass, a particle tracking algorithm and a parallel raw input decoding optimized for GPUs. It is designed for highly parallel architectures, data-oriented, and optimized for fast and localized data access. Our algorithm is configurable, and we explore the trade-off in computing and physics performance of various configurations. A CPU implementation that delivers the same physics performance as our GPU implementation is presented. We discuss the achieved physics performance and validate it with Monte Carlo simulated data. We show a computing performance analysis comparing consumer and server-grade GPUs, and a CPU. We show the feasibility of using a full GPU decoding and particle tracking algorithm for high-throughput particle trajectories reconstruction, where our algorithm improves the throughput up to 7.4 × compared to the LHCb baseline.

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain. [ABSTRACT FROM AUTHOR]

In high-energy particle collisions, charged track finding is a complex yet crucial endeavor. We propose a quantum algorithm, specifically quantum template matching, to enhance the accuracy and efficiency of track finding. Abstracting the Quantum Amplitude Amplification routine by introducing a data register, and utilizing a novel oracle construction, allows data to be parsed to the circuit and matched with a hit-pattern template, without prior knowledge of the input data. Furthermore, we address the challenges posed by missing hit data, demonstrating the ability of the quantum template matching algorithm to successfully identify charged-particle tracks from hit patterns with missing hits. Our findings therefore propose quantum methodologies tailored for real-world applications and underline the potential of quantum computing in collider physics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Brown, Spannowsky, Tapper, Williams and Xiotidis.)

The software for determining charged track parameters of the Spin Physics Detector (SPD) experiment on the future Nuclotron based Ion Collider fAcility (NICA) collider (JINR, Dubna) is presented along with the results obtained. The use of this program allows prompt simulations of various magnetic field configurations and the tracking system of the SPD experiment to determine the optimal structure for better track reconstruction. [ABSTRACT FROM AUTHOR]

• A pixel-based range telescope is a good candidate for proton computed tomography. • The detector design must be optimized for proton track reconstruction. • A design with 3.5 mm Al absorbers between the sensor layers is recommended. • Simulations show that over ten million protons per second can be reconstructed. A pixel-based range telescope for tracking particles during proton imaging is described. The detector applies a 3D matrix of stacked Monolithic Active Pixel Sensors with fast readout speeds. This study evaluates different design alternatives of the range telescope on basis of the protons' range accuracy and the track reconstruction efficiency. Detector designs with different thicknesses of the energy-absorbing plates between each sensor layer are simulated using the GATE/Geant4 Monte Carlo software. Proton tracks traversing the detector layers are individually reconstructed, and a Bragg curve fitting procedure is applied for the calculation of each proton's range. Simulations show that the setups with 4 mm and thinner absorber layers of aluminum have a low range uncertainty compared to the physical range straggling, systematic errors below 0.3 mm water equivalent thickness and a track reconstruction capability exceeding ten million protons per second. In order to restrict the total number of layers and to yield the required tracking and range resolution properties, a design recommendation is reached where the proposed range telescope applies 3.5 mm thick aluminum absorber slabs between each sensor layer. [ABSTRACT FROM AUTHOR]

We study the track reconstruction algorithms of the CEPC luminometer. Depend on the current geometry design, the conventional track reconstruction method is applied, but it suffers the energy leakage problem when tracks falling into the tile gaps regions. To solve this problem, a novel reconstruction method based on deep neural networks has been investigated, and the reconstruction efficiency has been improved significantly, as well as the energy and direction resolutions. This new reconstruction method is proposed to replace the conventional one for the CEPC luminometer. [ABSTRACT FROM AUTHOR]

Traditional charge readout technologies of single-phase Liquid Argon Time projection Chambers (LArTPCs) based on projective wire readout introduce intrinsic ambiguities in event reconstruction. Combined with the slow response inherent in LArTPC detectors, reconstruction ambiguities have limited their performance, until now. Here, we present a proof of principle of a pixelated charge readout that enables the full 3D tracking capabilities of LArTPCs. We characterize the signal-to-noise ratio of charge readout chain to be about 14, and demonstrate track reconstruction on 3D space points produced by the pixel readout. This pixelated charge readout makes LArTPCs a viable option for high-multiplicity environments.

We develop a novel Time Projection Chamber (TPC) concept suitable for deployment in kilotonne-scale detectors, with a charge-readout system free from reconstruction ambiguities, and a robust TPC design that reduces high-voltage risks while increasing the coverage of the light-collection system and maximizing the active volume. This novel concept could be used as a far detector module in the Deep Underground Neutrino Experiment (DUNE). For the charge-readout system, we used the charge-collection pixels and associated application-specific integrated circuits currently being developed for the liquid argon (LAr) component of the DUNE Near Detector design, ArgonCube. In addition, we divided the TPC into a number of shorter drift volumes, reducing the total voltage used to drift the ionization electrons, and minimizing the stored energy per TPC. Segmenting the TPC also contains scintillation light, allowing for precise trigger localization and a more expansive light-readout system. Furthermore, the design opens the possibility of replacing or upgrading components. These augmentations could substantially improve the reliability and the sensitivity, particularly for low-energy signals, in comparison to traditional monolithic LArTPCs with projective-wire charge readouts.

A drift chamber array at the External Target Facility in HIRFL-CSR has been constructed for three-dimensional particle tracking in high-energy radioactive ion beam experiments. The design, readout, track reconstruction program and calibration procedures for the detector are described. The drift chamber array was tested in a 311 AMeV 40 Ar beam experiment. The detector performance based on the measurements of the beam test is presented. A spatial resolution of 230 μ m is achieved. [ABSTRACT FROM AUTHOR]

The ASACUSA collaboration aims at measuring the ground state hyperfine splitting of antihydrogen for probing fundamental symmetries. A cryogenic trap for mixing antiprotons and positrons serves as an antihydrogen source for in-flight spectroscopy. In order to be able to monitor the antihydrogen formation process, a dedicated Micromegas tracking detector has been designed and built to record the annihilation distribution in the trap. In this paper, we present the first results from antiproton annihilation data recorded with the Micromegas, together with a description of the event reconstruction algorithm. [ABSTRACT FROM AUTHOR]

The key to photoelectric X-ray polarimetry is the determination of the emission direction of photoelectrons. Because of the low mass of an electron, the ionisation trajectory is not straight and the useful information needed for polarimetry is stored mostly in the initial part of the track where less energy is deposited. We present a new algorithm, based on the shortest path problem in graph theory, to reconstruct the 2D electron track from the measured image that is blurred due to transversal diffusion along drift and multiplication in the gas chamber. Compared with previous methods based on moment analysis, this algorithm allows us to identify the photoelectric interaction point more accurately and precisely for complicated tracks resulting from high energy photons or low pressure chambers. This leads to a better position resolution and a higher degree of modulation toward high energy X-rays. The new algorithm is justified using simulations and measurements with the gas pixel detector (GPD), and it should also work for other polarimetric techniques such as a time projection chamber (TPC). As the improvement is restricted in the high energy band, this new algorithm shows limited improvement for the sensitivity of GPD polarimeters, but it may have a larger potential for low-pressure TPC polarimeters. [ABSTRACT FROM AUTHOR]

Modern semiconductor detectors allow for charged particle tracking with ever increasing position resolution. Due to the reduction of the spatial hit uncertainties, multiple Coulomb scattering in the detector layers becomes the dominant source for tracking uncertainties. In this case long distance effects can be ignored for the momentum measurement, and the track fit can consequently be formulated as a sum of independent fits to hit triplets. In this paper we present an analytical solution for a three-dimensional triplet(s) fit in a homogeneous magnetic field based on a multiple scattering model. Track fitting of hit triplets is performed using a linearization ansatz. The momentum resolution is discussed for a typical spectrometer setup. Furthermore the track fit is compared with other track fits for two different pixel detector geometries, namely the Mu3e experiment at PSI and a typical high-energy collider experiment. For a large momentum range the triplets fit provides a significantly better performance than a single helix fit. The triplets fit is fast and can easily be parallelized, which makes it ideal for the implementation on parallel computing architectures. [ABSTRACT FROM AUTHOR]

A deep learning method for the particle trajectory reconstruction with the DAMPE experiment is presented. The developed algorithms constitute the first fully machine-learned track reconstruction pipeline for space astroparticle missions. Significant performance improvements over the standard hand-engineered algorithms are demonstrated. Thanks to the better accuracy, the developed algorithms facilitate the identification of the particle absolute charge with the tracker in the entire energy range, opening a door to the measurements of cosmic-ray proton and helium spectra at extreme energies, towards the PeV scale, hardly achievable with the standard track reconstruction methods. In addition, the developed approach demonstrates an unprecedented accuracy in the particle direction reconstruction with the calorimeter at high deposited energies, above several hundred GeV for hadronic showers and above a few tens of GeV for electromagnetic showers. [ABSTRACT FROM AUTHOR]

The expected factor four increase in peak luminosity of the high-luminosity LHC (HL-LHC) compared to the current LHC system will force the ATLAS experiment to increase early stage trigger selection power. The agreed strategy is to implement precise hardware track reconstruction, through which sharper trigger turn-on curves can be achieved for primary single-lepton selections, while contributing to b-tagging and tau-tagging techniques as well as pileup mitigation for hadronic signatures, such as multijet and missing transverse momentum. This work discusses the requirements, architecture and projected performance of the system in terms of tracking capability, and trigger selection, based on detailed simulations. • Hardware-based system for precise particle track reconstruction. • It uses a combination of Associative Memory ASICs and FPGAs. • It allows for reduced lepton transverse momentum trigger thresholds. • Factor 5 background rejection for 95% signal efficiency for electrons. • It also contributes to pile-up mitigation, essential for hadronic signatures. [ABSTRACT FROM AUTHOR]