Particle Track Reconstruction Research Articles

Spaceborne COTS-Capsule hodoscope: Detecting and characterizing particle radiation

The COTS-Capsule Spaceborne hodoscope was launched into low Earth orbit aboard the International Space Station and operated from 2021 to 2022. The primary objectives of the payload are measuring and characterizing the radiation environment within the space station, serving as a technology demonstrator for the COTS-Capsule radiation mitigation apparatus, and testing the interaction of high-energy cosmic particles on-orbit with our detectors. The payload features a particle hodoscope equipped with an array of novel detectors based on polyvinyl toluene scintillators and silicon photomultiplier sensors that are used for radiation detection and characterization employing 2D intensity-based triangulation. This paper provides a comprehensive account of the COTS-Capsule payload’s construction, preflight performance, testing, qualification, and on-orbit calibration. The hodoscope, sensitive to ionizing cosmic particles, facilitates impinging particle track reconstruction by multi-detector 2D position estimation, energy deposition estimation, and linear energy transfer estimation.

Quantum-Annealing-Inspired Algorithms for Track Reconstruction at High-Energy Colliders

Charged particle reconstruction or track reconstruction is one of the most crucial components of pattern recognition in high-energy collider physics. It is known to entail enormous consumption of computing resources, especially when the particle multiplicity is high, which will be the conditions at future colliders, such as the High Luminosity Large Hadron Collider and Super Proton–Proton Collider. Track reconstruction can be formulated as a quadratic unconstrained binary optimization (QUBO) problem, for which various quantum algorithms have been investigated and evaluated with both a quantum simulator and hardware. Simulated bifurcation algorithms are a set of quantum-annealing-inspired algorithms, known to be serious competitors to other Ising machines. In this study, we show that simulated bifurcation algorithms can be employed to solve the particle tracking problem. The simulated bifurcation algorithms run on classical computers and are suitable for parallel processing and usage of graphical processing units, and they can handle significantly large amounts of data at high speed. These algorithms exhibit reconstruction efficiency and purity comparable to or sometimes improved over those of simulated annealing, but the running time can be reduced by as much as four orders of magnitude. These results suggest that QUBO models together with quantum-annealing-inspired algorithms are valuable for current and future particle tracking problems.

Real-time charged track reconstruction for CLAS12

This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing.

Visual analytics system for understanding DeepRL-based charged particle tracking

AbstractIn this work, we propose a visual analytics system to analyze deep reinforcement learning (deepRL) models working on the track reconstruction of charged particles in the field of particle physics. The data of these charged particles are in the form of point clouds with high-dimensional features. We use one of the existing post hoc saliency methods of explainable artificial intelligence (XAI) and extend its adaptation to compute saliency attributions for the input data corresponding to the output of the model. Our proposed system helps users to explore these saliency attributions corresponding to the high-dimensional input data of the machine learning model and interpret the decision-making process of the model. In particular, we provide the users with multiple task-oriented components, different types of linked views and interactive tools to analyze the model. We explain how to use the system by outlining a typical user workflow and demonstrate the system’s usefulness using several case studies which address specific analysis tasks.

Optimising the Configuration of the CMS GPU Reconstruction

Particle track reconstruction for high energy physics experiments like CMS is computationally demanding but can benefit from GPU acceleration if properly tuned. This work develops an autotuning framework to automatically optimise the throughput of GPU-accelerated CUDA kernels in CMSSW. The proposed system navigates the complex parameter space by generating configurations, benchmarking performance, and leveraging multi-fidelity optimisation from simplified applications. The autotuned launch parameters improved CMSSW tracking throughput over the default settings by finding optimised, GPU-specific configurations. The successful application of autotuning to CMSSW demonstrates both performance portability across diverse accelerators and the potential of the methodology to optimise other HEP codebases.

Charged Track Reconstruction with Artificial Intelligence for CLAS12

Charged Track Reconstruction with Artificial Intelligence for CLAS12

Quantum Algorithms for Charged Particle Track Reconstruction in the LUXE Experiment

The LUXE experiment is a new experiment in planning in Hamburg, which will study quantum electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally expensive for classical computers. This paper investigates the potential future use of gate-based quantum computers for pattern recognition in track reconstruction. Approaches based on a quadratic unconstrained binary optimisation and a quantum graph neural network are investigated in classical simulations of quantum devices and compared with a classical track reconstruction algorithm. In addition, a proof-of-principle study is performed using quantum hardware.

A quantum algorithm for track reconstruction in the LHCb vertex detector

High-energy physics is facing increasingly demanding computational challenges in real-time event reconstruction for the near-future high-luminosity era. Using the LHCb vertex detector as a use case, we explore a new algorithm for particle track reconstruction based on the minimisation of an Ising-like Hamiltonian with a linear algebra approach. The use of a classical matrix inversion technique results in tracking performance similar to the current state-of-the-art but with worse scaling complexity in time. To solve this problem, we also present an implementation as a quantum algorithm, using the Harrow-Hassadim-Lloyd (HHL) algorithm: this approach can potentially provide an exponential speedup as a function of the number of input hits over its classical counterpart, in spite of limitations due to the well-known HHL Hamiltonian simulation and readout problems. The findings presented in this paper shed light on the potential of leveraging quantum computing for real-time particle track reconstruction in high-energy physics.

Intel Stratix 10 FPGA design for track reconstruction for the ATLAS experiment at the HL-LHC

The fast reconstruction of charged particle tracks with high efficiency and track quality is an essential part of the online data selection for the ATLAS experiment at the High Luminosity LHC.Dedicated custom designed hardware boards and software simulations have been developed to assess the feasibility of a Hardware Tracking Trigger (HTT) system.The Pattern Recognition Mezzanine (PRM), as part of the HTT system, has been designed to recognize track candidates in silicon detectors with Associative Memory ASICs and to select and reconstruct tracks using linearized algorithms implemented in an Intel Stratix 10 MX FPGA.The highly parallelized FPGA design makes extensive use of the integrated High-Bandwidth-Memory.In this paper, the FPGA design for the PRM board is presented. Its functionalities have been verified in both simulations and hardware tests on an Intel Stratix 10 MX development kit.

Simulation and data analysis of a fission time projection chamber based on Monte Carlo method

In this study, we use Monte Carlo method to conduct simulation research on a time projection chamber (TPC) which used for fission cross-section measurement. Based on Garfield, SRIM, COMSOL and other platforms and software, we explore the physical process during its actual operation, and analyze the simulation data. We carry out α particle track measurement experiments with U-235 ultra-thin target, and the measured results are in good agreement with the simulation results. This paper focuses on the simulation and reconstruction of particle tracks at different emission angles, and introduces the concept of ballistic deficit to correct the experimental measurement results. The corrected 2D distribution of particle track length and energy is closer to the theoretical model than the uncorrected distribution.

Simultaneous single particle tracking, phase retrieval and PSF reconstruction.

3D particle tracking and localization provide direct means to monitor biomolecular processes within nano-scale environments. However, optical aberrations due to inhomogeneous refractive indices are a major shortcoming in probing these processes in situ. In particular, point spread functions (PSF) may be distorted resulting in poor localization and linking across frames. This issue is particularly important when using pre-calibrated PSFs that do not take into account sample induced aberrations. The sample induced aberrations are often removed using experimental techniques such as adaptive optics (AO) by introducing new optical components to microscope setups. Here, we propose a computational method, leveraging a Bayesian framework, as an alternative to the AO techniques. Our Bayesian method is capable of simultaneous particle tracking, phase retrieval and PSF reconstruction directly from a given data set, without adding any new hardware to the optical setup. Moreover, our method is data efficient by rigorously propagating uncertainty from all existing sources in the problem, such as the uncertainty in the shape of PSF, often ignored. We benchmark our method using a wide range of synthetic and experimental data.

Studying quantum algorithms for particle track reconstruction in the LUXE experiment

The LUXE experiment (LASER Und XFEL Experiment) is a new experiment in planning at DESY Hamburg, which will study Quantum Electrodynamics (QED) at the strong-field frontier. In this regime, QED is non-perturbative. This manifests itself in the creation of physical electron-positron pairs from the QED vacuum. LUXE intends to measure the positron production rate in this unprecedented regime by using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally very hard for classical computers. This paper presents a preliminary study to explore the potential of quantum computers to solve this problem and to reconstruct the positron trajectories from the detector energy deposits. The reconstruction problem is formulated in terms of a quadratic unconstrained binary optimisation. Finally, the results from the quantum simulations are discussed and compared with traditional classical track reconstruction algorithms.

Charged particle tracking in real-time using a full-mesh data delivery architecture and associative memory techniques

We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.

Charged Particle Track Reconstruction Program in the SPD Experiment

Charged Particle Track Reconstruction Program in the SPD Experiment

Status and prospects of TPC prototype using the UV laser mimic tracks

To meet the high precision physical goals in the future e + e − circular collider (CEPC), the high resolution tracker detector for the particle track reconstruction (¡100μ m) and particle identification are required. Time Projection Chamber (TPC) is one of the main concept option of the central tracker detector. On behalf of the track detector subgroup in CEPC, the status and update R&D results of TPC module and prototype for the specific requirements will be presented in this article. TPC module will could suppress the ions in chamber continuously running in the different gains (2000-5000) and T2K mixture gases, and TPC prototype with Micro-pattern Gaseous Detector (MPGD) detector module integrated the narrow laser mimic tracks system. the update results of the spatial resolution, particles identified (dE/dx) were been investigated. In this TPC prototype, the precision drift time of the different laser mimic tracks ionized primary electrons in the specific operation working gases were recorded. Some considerations of the low power consumption readout requirements will be given according to the pad TPC detector operating at the high luminosity Z pole at CEPC.

Status and prospects of TPC module and prototype at high luminosity Z physics on CEPC

The Circular Electron–Positron Collider (CEPC) has been proposed as a Higgs and high luminosity [Formula: see text] factory for the last few years in China. To achieve the high precision physical goals at CEPC, the high resolution ([Formula: see text]) tracker for the particle track reconstruction and particle identification are demanded. Time Projection Chamber (TPC) is one of the main concept proposal of the central tracker detector in very large 3D volume, it has an excellent performance on the moment measurement, [Formula: see text] measurement and the spatial resolution. The simulation results were given when the TPC detector will operate at the high luminosity [Formula: see text] pole at the future [Formula: see text] circular collider. The pixelated readout TPC is a potential option to replace the traditional pad readout with the low gain, low occupancy, and outstanding pattern recognition. In the last several years, a TPC module, the readout electronics and prototype for the specific requirements have been developed at the Institute of High Energy Physics (IHEP). TPC module will could suppress the ions in chamber continuously running in the different gains (2000–5000) under T2K mixture gases. A TPC prototype with [Formula: see text] active area G detector module integrated the narrow 266[Formula: see text]nm UV laser tracks along the drift length of 500[Formula: see text]mm, and the low power consumption ([Formula: see text]) Application-Specific Integrated Circuits (ASIC) chip including analog to digital converter (ADC) function has been developed too. The update results of the spatial resolution has been obtained after some experimental studies have been done by the TPC prototype.

Event-based imaging velocimetry: an assessment of event-based cameras for the measurement of fluid flows

Contrary to conventional frame-based imaging, event-based vision or dynamic vision sensing asynchronously records binary signals of intensity changes for given pixels with microsecond resolution. The present work explores the possibilities of harnessing the potential of event-based vision for fluid flow measurement. The described implementations of event-based imaging velocimetry rely on imaging small particles that are illuminated by a laser light sheet which is similar to classical two-dimensional, two-component (2d–2c) PIV with the difference that a continuously operating laser light sheet is used without modulation of the laser or camera. The moving particles generate continuous time-stamped events on the detector that are later used to infer their velocity using patch-wise processing schemes. Two flow estimation algorithms are proposed; one uses a “motion compensation” that maximizes the local contrast and the other is based on a sum-of-correlations approach. The underlying motion detection schemes in combination with the complete absence of background signal allow straightforward retrieval of the events associated with individual particles thereby allowing the reconstruction of individual particle tracks. Alternatively, the event data can be processed with conventional PIV algorithms using images reconstructed from the event data stream. The concepts are demonstrated on simple flows in water and air.Graphic abstract

Using machine learning for particle track identification in the CLAS12 detector

Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements (“hits”) to identify those that form an actual particle trajectory. In this article, we describe the development of four machine learning (ML) models that assist the tracking algorithm by identifying valid track candidates from the measurements in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this work, an MLP network classifier was implemented as part of the CLAS12 reconstruction software to provide the tracking code with recommended track candidates. The resulting software achieved accuracy of greater than 99% and resulted in an end-to-end speedup of 35% compared to existing algorithms.

Characterization of Heavily Irradiated Dielectrics for Pixel Sensors Coupling Insulator Applications

An increase in the radiation levels during the high-luminosity operation of the Large Hadron Collider calls for the development of silicon-based pixel detectors that are used for particle tracking and vertex reconstruction. Unlike the conventionally used conductively coupled (DC-coupled) detectors that are prone to an increment in leakage currents due to radiation, capacitively coupled (AC-coupled) detectors are anticipated to be in operation in future collider experiments suitable for tracking purposes. The implementation of AC-coupling to micro-scale pixel sensor areas enables one to provide an enhanced isolation of radiation-induced leakage currents. The motivation of this study is the development of new generation capacitively coupled (AC-coupled) pixel sensors with coupling insulators having good dielectric strength and radiation hardness simultaneously. The AC-coupling insulator thin films were aluminum oxide (Al2O3) and hafnium oxide (HfO2) grown by the atomic layer deposition (ALD) method. A comparison study was performed based on the dielectric material used in MOS, MOSFET, and AC-coupled pixel prototypes processed on high resistivity p-type Magnetic Czochralski silicon (MCz-Si) substrates. Post-irradiation studies with 10 MeV protons up to a fluence of 1015 protons/cm2 suggest HfO2 to be a better candidate as it provides higher sensitivity with negative charge accumulation on irradiation. Furthermore, even though the nature of the dielectric does not affect the electric field within the AC-coupled pixel sensor, samples with HfO2 are comparatively less susceptible to undergo an early breakdown due to irradiation. Edge-transient current technique (e-TCT) measurements show a prominent double-junction effect as expected in heavily irradiated p-type detectors, in accordance with the simulation studies.

Hybrid quantum classical graph neural networks for particle track reconstruction

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as “hit”. The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classical graph neural network that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit-based hybrid quantum-classical graph neural networks.