Track Reconstruction Research Articles

Readout system for a prototype multi-purpose time projection chamber at CSNS Back-n

This paper presents a readout system for a prototype multi-purpose Time Projection Chamber (MTPC), which is developed for the research of neutron-induced light-charged particles emission reaction. The prototype MTPC consists of 1521 readout channels for charge measurements, track reconstruction, and neutron time-of-flight measurement. The main function of the readout system is to perform 1521-channel waveform digitization based on multi-channel analog to digital converters (ADCs). Digital waveform compression, multi-board time synchronization, and many other efforts have been made to ensure system performance and stability. Test results show that the readout system has a time resolution better than 10 ns at a trigger rate of 20 kHz. With a 2.8 pC dynamic range of charge measurement, the noise without any input load is around 0.76 fC (in RMS), and the nonlinearity of each channel is less than 0.35%. A beam test with 6Li target has been carried out at the back-streaming white neutron source (Back-n) of China Spallation Neutron Source. The tracks of particles from neutron-induced reactions have been successfully reconstructed. The time-of-flight distribution of the 6Li(n,α)3H reaction reveals the resonance peak at about 240 keV. These results indicate that the readout system fulfils the requirements of the prototype MTPC.

Reconstruction of a muon bundle in the JUNO central detector

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment. One of the main goals is to determine the neutrino mass ordering by precisely measuring the energy spectrum of reactor antineutrinos. For the detection of reactor antineutrinos, cosmogenic backgrounds, such as 9Li/8He and fast neutrons induced by cosmic muons, should be rejected carefully by applying muon veto cuts, which require good muon track reconstruction. With a 20-kton liquid scintillator detector, the simulation shows the proportion of muon bundles (muon multiplicity ≥ 2) to be approximately 8% in JUNO, whereas its reconstruction has been rarely discussed in previous experiments. This study proposes an efficient algorithm for muon track reconstruction based on the charge response of a photomultiplier tube array. This is the first reconstruction of muon bundles in a large-volume liquid scintillator detector. In addition, the algorithm shows good performance and potential for reconstruction for both a single muon and double muons (muon multiplicity = 2). The spatial resolution of a single-muon reconstruction was 20 cm, and the angular resolution was 0.5°. For double-muon reconstruction, the spatial and angular resolutions could be 30 cm and $$1.0^\circ$$ , respectively. Moreover, this paper also discusses muon classification and the veto strategy.

HRBF-Fusion: Accurate 3D Reconstruction from RGB-D Data Using On-the-fly Implicits

Reconstruction of high-fidelity 3D objects or scenes is a fundamental research problem. Recent advances in RGB-D fusion have demonstrated the potential of producing 3D models from consumer-level RGB-D cameras. However, due to the discrete nature and limited resolution of their surface representations (e.g., point or voxel based), existing approaches suffer from the accumulation of errors in camera tracking and distortion in the reconstruction, which leads to an unsatisfactory 3D reconstruction. In this article, we present a method using on-the-fly implicits of Hermite Radial Basis Functions (HRBFs) as a continuous surface representation for camera tracking in an existing RGB-D fusion framework. Furthermore, curvature estimation and confidence evaluation are coherently derived from the inherent surface properties of the on-the-fly HRBF implicits, which are devoted to a data fusion with better quality. We argue that our continuous but on-the-fly surface representation can effectively mitigate the impact of noise with its robustness and constrain the reconstruction with inherent surface smoothness when being compared with discrete representations. Experimental results on various real-world and synthetic datasets demonstrate that our HRBF-fusion outperforms the state-of-the-art approaches in terms of tracking robustness and reconstruction accuracy.

Quantum speedup for track reconstruction in particle accelerators

To investigate the fundamental nature of matter and its interactions, particles are accelerated to very high energies and collided inside detectors, producing a multitude of other particles that are scattered in all directions. As charged particles traverse the detector, they leave signals of their passage. The problem of track reconstruction is to recover the original trajectories from these signals. This challenging data analysis task will become even more demanding as the luminosity of future accelerators increases, leading to collision events with a more complex structure. We identify four fundamental routines present in every local tracking method and analyse how they scale in the context of a standard tracking algorithm. We show that for some of these routines we can reach a lower computational complexity with quantum search algorithms. Although the found quantum speedups are mild, this constitutes, to the best of our knowledge, the first rigorous evidence of a quantum advantage for a high-energy physics data processing task.

Measurement of the cluster position resolution of the Belle II Silicon Vertex Detector

The Silicon Vertex Detector (SVD), with its four double-sided silicon strip sensor layers, is one of the two vertex sub-detectors of Belle II operating at SuperKEKB collider (KEK, Japan). Since 2019 and the start of the data taking, the SVD has demonstrated a reliable and highly efficient operation, even running in an environment with harsh beam backgrounds that are induced by the world’s highest instantaneous luminosity.In order to provide the best quality track reconstruction with an efficient pattern recognition and track fit, and to correctly propagate the uncertainty on the hit’s position to the track parameters, it is crucial to precisely estimate the resolution of the cluster position measurement. Several methods for estimating the position resolution directly from the data will be discussed.

A Common Tracking Software Project

The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.

The Silicon Vertex Detector of the Belle II experiment

The Silicon Vertex Detector (SVD) is a part of the vertex detector in the Belle II experiment at the SuperKEKB collider (KEK, Japan). Since the start of data taking in spring 2019, the SVD has been operating stably and reliably with a high signal-to-noise ratio and hit efficiency, achieving good spatial resolution and high track reconstruction efficiency. The hit occupancy, which mostly comes from the beam-related background, is currently about 0.5% in the innermost layer, causing no impact on the SVD performance. In anticipation of the operation at higher luminosity in the following years, two strategies to sustain the tracking performance in future high beam background conditions have been developed and tested on data. One is to reduce the number of signal waveform samples to decrease dead time, data size, and occupancy. The other is to utilize the good hit-time resolution to reject the beam background hits. We also measured the radiation effects on the full depletion voltage, sensor current, and strip noise caused during the first two and a half years of operation. The results show no detrimental effect on the SVD performance.

Track Reconstruction in the Upgraded Tracking System of MPD/NICA

Track Reconstruction in the Upgraded Tracking System of MPD/NICA

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.

Track Reconstruction in a High-Density Environment with ALICE

ALICE is the dedicated heavy-ion experiment at the CERN Large Hadron Collider (LHC). Its main tracking and particle-identification detector is a large volume Time Projection Chamber (TPC). The TPC has been designed to perform well in the high-track density environment created in high-energy heavy-ion collisions. In this proceeding, we describe the track reconstruction procedure in ALICE. In particular, we focus on the two main challenges that were faced during the Run 2 data-taking period (2015–2018) of the LHC, which were the baseline fluctuations and the local space charge distortions in the TPC. We present the corresponding solutions in detail and describe the software tools that allowed us to circumvent these challenges.

Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment

In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion films followed by a silicon pixel tracker was installed in front of the Goliath magnet at the H4 proton beam-line. Behind the magnet, scintillating-fibre, drift-tube and RPC detectors were placed. The purpose of this run was to validate the measurement's feasibility, to develop the required analysis tools and fine-tune the detector layout. In this paper, we present the track reconstruction in the pixel tracker and the track matching with the moving emulsion detector. The pixel detector performed as expected and it is shown that, after proper alignment, a vertex matching rate of 87% is achieved.

Track recognition for the [formula omitted] telescopes with silicon strip detectors

For the high granularity and high energy resolution, Silicon Strip Detector (SSD) is widely applied in assembling telescopes to measure the charged particles in heavy ion reactions. In this paper, we present a novel method to achieve track recognition in the SSD telescopes of the Compact Spectrometer for Heavy Ion Experiment (CSHINE). Each telescope consists of a single-sided silicon strip detector (SSSSD) and a double-sided silicon strip detector (DSSSD) backed by 3 × 3 CsI(Tl) crystals. Detector calibration and track reconstruction are implemented. Special decoding algorithm is developed for the multi-track recognition procedure to deal with the multi-hit effect convoluted by charge sharing and the missing signals with certain probability. It is demonstrated that the track recognition efficiency of the method is approximately 90% and 80% for the DSSSD-CsI and SSSSD–DSSSD events, respectively.

Machine learning for track reconstruction at the LHC

The planned upgrade of the LHC to its High-Luminosity counterpart (HL-LHC) circa 2027 will bring about a drastic increase in instantaneous luminosity, pileup, and trigger rates. Currently, most LHC experiments use Kalman filter based track reconstruction algorithms which exhibit outstanding physics performance but scale poorly with the amount of data produced per bunch crossing. Therefore, the high energy physics community is currently performing intensive R&D to commission new or improved algorithms for this crucial data reconstruction task. This article presents many approaches such as running existing Kalman filter algorithms on accelerated hardware and overhauling the current approaches with machine learning techniques. A new algorithm testbed for research in track reconstruction, ACTS, is also discussed.

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.

Integrating Biologging and Behavioral State Modeling to Identify Cryptic Behaviors and Post-capture Recovery Processes: New Insights From a Threatened Marine Apex Predator

Multisensor biologging provides a powerful tool for ecological research, enabling fine-scale observation of animals to directly link physiology and movement to behavior across ecological contexts. However, applied research into behavioral disturbance and recovery following human interventions (e.g., capture and translocation) has mostly relied on coarse location-based tracking or unidimensional approaches (e.g., dive profiles and activity/energetic metrics) that may not resolve behaviors and recovery processes. Biologging can improve insights into both disturbed and natural behavior, which is critical for management and conservation initiatives, although challenges remain in objectively identifying distinct behavioral modes from complex multisensor datasets. Using white sharks (Carcharodon carcharias) released from a non-lethal catch-and-release shark bite mitigation program, we explored how combining multisensor biologging (video, depth, accelerometers, gyroscopes, and magnetometers), track reconstruction and behavioral state modeling using hidden Markov models (HMMs) can improve our understanding of behavioral processes and recovery. Biologging tags were deployed on eight white sharks, recording their continuous behaviors, movements, and environmental context (habitat, interactions with other organisms/objects) for periods of 10–87 h post-release. Dive profiles and tailbeat analysis (as a standard, activity-based method for assessing recovery) indicated an immediate “disturbed” period of offshore movement, displaying rapid tailbeats and an average tailbeat-derived recovery period of 9.7 h, with evidence of smaller individuals having longer recoveries. However, further integrating magnetometer-derived headings, track reconstruction and HMM modeling revealed a cryptic shift to diurnal clockwise-counterclockwise circling behavior, which we argue represents compelling new evidence for hypothesized unihemispheric sleep amongst elasmobranchs. By simultaneously providing critical information toward conservation-focused shark management and understudied aspects of shark behavior, our study highlights how integrating multisensor information through HMMs can improve our understanding of both post-release and natural behavior, especially in species that are difficult to observe directly.

Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration

In traffic scenarios, vehicle trajectories can provide almost all the dynamic information of moving vehicles. Analyzing the vehicle trajectory in the monitoring scene can grasp the dynamic road traffic information. Cross-camera association of vehicle trajectories in multiple cameras can break the isolation of target information between single cameras and obtain the overall road operation conditions in a large-scale video surveillance area, which helps road traffic managers to conduct traffic analysis, prediction, and control. Based on the framework of DBT automatic target detection, this paper proposes a cross-camera vehicle trajectory correlation matching method based on the Euclidean distance metric correlation of trajectory points. For the multitarget vehicle trajectory acquired in a single camera, we first perform 3D trajectory reconstruction based on the combined camera calibration in the overlapping area and then complete the similarity association between the cross-camera trajectories and the cross-camera trajectory update, and complete the trajectory transfer of the vehicle between adjacent cameras. Experiments show that the method in this paper can well solve the problem that the current tracking technology is difficult to match the vehicle trajectory under different cameras in complex traffic scenes and essentially achieves long-term and long-distance continuous tracking and trajectory acquisition of multiple targets across cameras.

Development, construction and tests of the Mu2e electromagnetic calorimeter mechanical structures

The “muon-to-electron conversion” (Mu2e) experiment at Fermilab will search for the charged lepton flavour violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of the existence of physics beyond the standard model. Mu2e detectors comprise a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. In particular, the calorimeter provides excellent electron identification, a fast calorimetric online trigger, and complementary information to aid pattern recognition and track reconstruction. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1348 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named “disks”) positioned at the relative distance of 70 cm downstream the aluminum target along the muon beamline satisfies the Mu2e physics requirements. The hostile Mu2e operational conditions, in terms of radiation levels (total expected ionizing dose of 12 krad and a neutron fluence of 5 × 1010 n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10−4 Torr) have posed tight constraints on scintillating materials, sensors, electronics and on the design of the detector mechanical structures and material choice. The support structure of each 674 crystal matrix is composed of an aluminum hollow ring and parts made of open-cell vacuum-compatible carbon fiber. The photosensors and front-end electronics for the readout of each crystal are inserted in a machined copper holder and make a unique mechanical unit. The resulting 674 mechanical units are supported by a machined plate of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal contact with the copper holders to constitute a low resistance thermal bridge. The data acquisition electronics are hosted in aluminum custom crates positioned on the external lateral surface of the disks. The crates also integrate the electronics cooling system as lines running in parallel to the front-end system. In this paper we report on the calorimeter mechanical structure design, the mechanical and thermal simulations that have determined the design technological choices, and the status of component production, quality assurance tests and plans for assembly at Fermilab.

The Stabilization Control of the Railroad Track

An important area in the repair and maintenance of railway tracks is the development of digital technologies for monitoring the parameters of track machinery operation. Currently, digital monitoring systems are being introduced on the JSC "Russian Railways" network to ensure control over track machinery operation, which transmit information on machinery operation or on the current condition of units and aggregates in real time.In accordance with the requirements of JSC "Russian Railways" when carrying out repair and track works at railway transport infrastructure facilities, operational quality control of work performance is required. The control consists in ensuring the absence of hidden defects affecting the long-term stability of the track during the reconstruction and repair of the railway track. Automation of control functions is widely used in the world practice, but for their use it is necessary to have evaluation criteria for transmitted data. The approach proposed in the article to assess the quality of ballast layer compaction makes it possible to organize monitoring of the functioning parameters of track facilities, affecting the quality of work, and thereby will reduce the number of unscheduled track repairs carried out due to improper operation and maintenance of equipment.

Robust and Long-Term High-Speed Operational Status Inspection of Pantograph-OCS in Complex Environments

Good contact conditions between the pantograph and catenary ensure the safety of electrified railways. However, the relevant state-of-the-art status inspection methods cannot achieve high speed, and robust effects under complex conditions, such as multi-suspended line interference, unknown complex backgrounds, and light and dark changes. Therefore, a two-module contact status diagnosis methodology, consisted of long-term tracking and 3D reconstruction, is proposed in this study. This long-term tracking method is a combination of tracking, detection and discrimination, focusing on locating the contact area of high-speed movement in complex environments. First, a brand-new tracking paradigm, namely, GMSTrack, based on feature matching is developed to adapt to complex background changes. Second, we modify the loss function via GloU-plus loss for box regression and via adaptive wing loss for landmark regression on CenterNet and realize a dramatic improvement in detection accuracy at 125 FPS. Third, sequential information is collected for the transformer-based discriminator to evaluate the correctness of the current tracking result. Experimental results show that the optimal strategy that combines tracking, detection, and discrimination can locate the contact area in complex environments with an accuracy higher than 93% and with a speed higher than 84 FPS. Lastly, dynamic stagger value and height of conductor that can reflect contact status are analyzed though 3D reconstruction of the binocular contact points. The overall status inspection methodology can be applied to the on-site high-speed trains at speeds exceeding 80 FPS.

Reference Model Control of the Time Delayed Double Integrator

This paper illustrates the reference model-based dead-time compensator (RM-DTC) recently developed for first-order time-delayed systems using a real-time example, and extends this novel approach for a time-delayed double integrator system. RM-DTC enables fully decoupled setpoint tracking and disturbance reconstruction and compensation. The overall structure of RM-DTC includes feedforward control using either a transfer function based implementation or the primary loop which produces a filtered inverse of the model transfer function. The setpoint feedforward is complemented by a disturbance-observer-based input disturbance reconstruction that also uses the filtered inverse of the plant model. The reference models of setpoint and input disturbance feedforward control allow the introduction of a stabilising PD controller without compromising the nominally independent dynamics of setpoint tracking and disturbance rejection. The main advantage of retaining the full functionality of disturbance reconstruction in RM-DTC is that it enables monitoring and diagnostics of the controlled process, which is important in terms of full automation of running processes representing the fundamental goal of the Industry 4.0 campaign. This surpasses the approaches based on internal model control, which have been modified for unstable systems to achieve internal stability by eliminating the reconstructed disturbance signal from the control loop. The excellent properties of RM -DTCs are illustrated by simulation and real-time control of thermal process.