Track Reconstruction Research Articles

High-precision large-area muon tracking and triggering with drift-tube chambers at future colliders

Experiments like the ATLAS detector at the HL-LHC or detectors at future hadron colliders need muon detectors with excellent momentum resolution at the percent level up to the TeV scale both at the trigger and the offline reconstruction level. This requires muon tracking chambers with high spatial resolution even at the highest background fluxes. Drift-tube chambers are the most cost effective technology for the instrumentation of large-area muon systems providing the required high rate capability and three-dimensional spatial resolution. Thanks to the advances in analog and digital electronics, modern drift-tube chambers can be used in stand-alone mode up to the highest background rates providing event times and second coordinates without the necessity of additional trigger chambers. New key developments in the integrated front-end electronics are fast baseline restoration of the shaped signal and picosecond time-to-digital converters for second coordinate measurement with double-sided read-out of the tubes. Self-triggered operation has become possible using modern high-performance FPGAs allowing for real-time pattern recognition and track reconstruction.

Status of the Jiangmen Underground Neutrino Observatory

The Jiangmen Underground Neutrino observatory (JUNO) experiment uses a large liquid scintillator detector to detect electron antineutrinos issued from nuclear reactors at a distance of 53 km. Its main goal is to determine the neutrino mass hierarchy and precisely measure some of the oscillation parameters. The detector will be located 700 m underground and will consist of 20 ktons of liquid scintillator contained in a 35 m diameter acrylic sphere, instrumented with 17612 (20-inch) and 25600 (3-inch) photomultiplier tubes (PMT), respectively. It will achieve an unprecedented 3% energy resolution (at 1 MeV). The objective is to detect 100000 events after 6 years of data taking. Two independent veto systems are foreseen to reduce the different backgrounds. A 40 kton ultrapure water Cherenkov pool instrumented with 2400 20-inch PMTs surrounds the central detector. It will tag events coming from outside the neutrino target. It will also act as a passive shielding for neutrons and gammas. In addition, a muon tracker will be installed on top of the detector (top muon veto) in order to tag cosmic muons and validate the muon track reconstruction.

An exploratory study of machine learning techniques applied to therapeutic energies particle tracking in microdosimetry using the novel hybrid detector for microdosimetry (HDM)

In this work we present an advanced random forest-based machine learning (ML) model, trained and tested on Geant4 simulations. The developed ML model is designed to improve the performance of the hybrid detector for microdosimetry (HDM), a novel hybrid detector recently introduced to augment the microdosimetric information with the track length of particles traversing the microdosimeter. The present work leads to the following improvements of HDM: (i) the detection efficiency is increased up to 100%, filling not detected particles due to scattering within the tracker or non-active regions, (ii) the track reconstruction algorithm precision. Thanks to the ML models, we were able to reconstruct the microdosimetric spectra of both protons and carbon ions at therapeutic energies, predicting the real track length for every particle detected by the microdosimeter. The ML model results have been extensively studied, focusing on non-accurate predictions of the real track lengths. Such analysis has been used to identify HDM limitations and to understand possible future improvements of both the detector and the ML models.

Electroweak physics at LHCb

The Electroweak (EW) Theory is one of the most important and well-known pillars of the Standard Model (SM) of particle physics. Possible signs of New Physics could appear in the EW sector, therefore requiring the SM to be extended. EW physics can be probed at the LHCb experiment, which is a general purpose forward detector that offers unique opportunities to do studies in the forward region. Its excellent vertex and track reconstruction system allows to perform several measurements of perturbative QCD physics in a region unexplored by other experiments. In this paper the latest EW physics analyses performed at LHCb studying pp collisions during Run 1 and Run 2 are presented.

Numerical Studies on Primary Ionization in TPC

Identification followed by directionality measurement using reconstruction of tracks is very crucial for studying the reaction vertex kinematics. In the field of low-energy nuclear physics, Active-Target Time Projection Chambers (AT-TPCs) can be used to study the nuclear reaction kinematics through tracking of the reaction products which is important for cross-section measurement. Ions produced in primary ionization by any charged particle along their track in the active gas volume of TPC can be utilized for track reconstruction with position-sensitive electron collection system placed inside an electric field. The TPC gas volume acts as the tracker of the reaction products and the target for the reaction with the incoming projectile simultaneously which is advantageous to conventional detector arrays. In this context, the design of the electric field in the drift volume of the TPC is an important criterion for precise tracking as the tracking capability of the TPC is strongly governed by the homogeneity of the electric field. Due to the lesser mobility of positive ions produced in primary ionization, their accumulation in the drift volume can distort the local electric field. In low-energy nuclear physics, this effect may be substantial due to the significant amount of ionization produced by the low-energy projectile and reaction products. Here we report the spatial information of primary space charges produced by cosmic muon and alpha particle obtained with geant4 [1] and Heed [2] simulation packages. We have used photo absorption and ionization physics lists in geant4 for the simulation and compared the results with that of the Heed. The simulation results for the change in pressure of gas volume will be reported. These results can be used for finding the distortion of the electric field due to the space charge in the drift region of the TPC which can be helpful for designing an AT-TPC for low-energy nuclear reaction experiments.

“RIPTIDE” — an innovative recoil-proton track imaging detector

Neutron detectors perform key tasks in many research fields as nuclear, particle and astroparticle physics as well as neutron dosimetry, radiotherapy, and radiation protection. Neutron detectors exhibiting tracking capability are still missing, although several approaches to neutron momentum reconstruction have been proposed. In this context, we aim at developing a novel RecoIl-Proton Track Imaging DEtection system “RIPTIDE”, in which the light output of a fast scintillation signal is used to perform a complete reconstruction in space and time of the neutron-proton elastic scattering. The 3D track reconstruction is going to be implemented by state-of-the-art high-sensitivity imaging detector (CMOS, MCP-Timepix). Preliminary Geant4 simulations of the proposed set-up show up a good detection efficiency in a compact active volume. The envisaged electronic readout can be easily adapted according to a specific application (event-by-event mode or integration mode). The system can be rescaled by increasing the detection volume or by combining several detection modules. Further developments of the basic detection technique can be adapted for fast charged particle detection tracking as well.

Readout Electronics of the Prototype Beam Monitor in the HIRFL-CSR External-Target Experiment

The External-target Experiment (CEE) at the Cooling Storage Ring of the Heavy-Ion Research Facility in Lanzhou (HIRFL-CSR) will be the first large-scale experiment in nuclear physics independently developed in China covering the GeV energy regime. The beam monitor located at the center front of the CEE accurately measures the position of the particles with a few tens of um accuracy in a non-interceptive way. This unique advantage significantly improves the accuracy of the particle track reconstructions. This beam monitor’s readout electronics consist of the Front-end module (FEM), Readout Control Module (RCM), and Clock Synchronization module (CSM). Twhe novel Topmetal series pixel sensors directly collect the ionized charge along the track of the ion beam while it passes through the gas in the beam monitor. Lab test proves that the readout electronics have an INL of less than 1%. In addition, the prototype beam monitor can measure the position of the 40Ar beam of 320 MeV/u with a resolution of ~6.9 μm. This paper will discuss the design, characterization, and test of the readout electronics.

The visual measurement system in the parachute used on the Tianwen-1 Mars exploration mission

ABSTRACT The parachute of the probe plays an important role in the deceleration of the Mars landing mission. This paper provides an overview of the visual measurement system of the parachute in Tianwen-1 exploration mission. Using two monitoring cameras, the system has captured stereo images during the parachute deployment. The morphological and motion parameters of the parachute are reconstructed by image-based feature tracking and 3D reconstruction methods. To improve the image quality, a super-resolution (SR) reconstruction method based on sparse dictionary coding is proposed. In addition, we use a multi-scale feature tracking method to track the feature points and update the motion parameters. The system has been verified by the test on the Earth and successfully applied on the onboard images. The reconstructed parachute parameters are important in the post-flight performance assessment, and the research results can be used as a regression analysis means for the structural design.

AlphaCAMM, a Micromegas-based camera for high-sensitivity screening of alpha surface contamination

Surface contamination of 222Rn progeny from the 238U natural decay chain is one of the most difficult background contributions to measure in rare event searches experiments. In this work we propose AlphaCAMM, a gaseous chamber read with a segmented Micromegas, for the direct measurement of 210Pb surface contamination of flat samples. The detection concept exploits the readout capabilities of the Micromegas detectors for the reconstruction of 210Po alpha tracks to increase the signal-to-background ratio. We report here on the design and realization of a first 26 × 26 cm2 non-radiopure prototype, with which the detection concept is demonstrated by the use of a new algorithm for the reconstruction of alpha tracks. AlphaCAMM aims for minimum detectable 210Pb activities of 100 nBq cm-2 and sensitivity upper limits about 60 nBq cm-2 at 95% of C.L., which requires an intrinsic background level of 5×10-8 alphas cm-2 s-1. We discuss here the prospects to reach these sensitivity goals with a radiopure AlphaCAMM prototype currently under construction.

Directionality and head-tail recognition in the keV-range with the MIMAC detector by deconvolution of the ionic signal

Directional detection is the only strategy for the unambiguous identification of galactic Dark Matter (DM) even in the presence of an irreducible background such as beyond the neutrino floor. This approach requires measuring the direction of a DM-induced nuclear recoil in the keV-range. To probe such low energies, directional detectors must operate at high gain where 3D track reconstruction can be distorted by the influence of the numerous ions produced in the avalanches. The article describes the interplay between electrons and ions during signal formation in a Micromegas. It introduces SimuMimac, a simulation tool dedicated to high gain detection that agrees with MIMAC measurements. This work proposes an analytical formula to deconvolve the ionic signal induced on the grid from any measurements, with no need for prior nor ad hoc parameter. This deconvolution is experimentally tested and validated, revealing the fine structure of the primary electrons cloud and consequently leading to head-tail recognition in the keV-range. Finally, the article presents how this deconvolution can be used for directionality by reconstructing the spectra of mono-energetic 27 keV and 8 keV neutrons with an angular resolution better than 15°. This novel approach for directionality appears as complementary to the standard one from 3D tracks reconstruction and offers redundancy for improving directional performances at high gain in the keV region.

Construction and test of the SM1 type Micromegas chambers for the upgrade of the ATLAS forward muon spectrometer

Large-size Resistive Micromegas have been chosen for the upgrade of the forward muon spectrometer of the ATLAS experiment, the New Small Wheel project. These chambers, together with small-strip Thin Gap Chambers (sTGC), allow reconstruction of high-momentum muon tracks in a high-radiation environment and provide a robust low-threshold single-muon trigger. A collaboration of seven INFN units built 32 SM1 type chambers, corresponding to one fourth of the total number needed for this upgrade. Each SM1 chamber has a surface of approximately 2 m2 and four sensitive layers. The production was shared among five INFN construction sites and it was completed in fall 2020. The construction methods, as well as the results of the quality tests done on components of the detector and on the assembled chambers, are reported in the present paper.

Endoscope Localization and Dense Surgical Scene Reconstruction for Stereo Endoscopy by Unsupervised Optical Flow and Kanade-Lucas-Tomasi Tracking.

In image-guided surgery, endoscope tracking and surgical scene reconstruction are critical, yet equally challenging tasks. We present a hybrid visual odometry and reconstruction framework for stereo endoscopy that leverages unsupervised learning-based and traditional optical flow methods to enable concurrent endoscope tracking and dense scene reconstruction. More specifically, to reconstruct texture-less tissue surfaces, we use an unsupervised learning-based optical flow method to estimate dense depth maps from stereo images. Robust 3D landmarks are selected from the dense depth maps and tracked via the Kanade-Lucas-Tomasi tracking algorithm. The hybrid visual odometry also benefits from traditional visual odometry modules, such as keyframe insertion and local bundle adjustment. We evaluate the proposed framework on endoscopic video sequences openly available via the SCARED dataset against both ground truth data, as well as two other state-of-the-art methods - ORB-SLAM2 and Endo-depth. Our proposed method achieved comparable results in terms of both RMS Absolute Trajectory Error and Cloud-to-Mesh RMS Error, suggesting its potential to enable accurate endoscope tracking and scene reconstruction.

Muon track reconstruction in a segmented bolometric array using multi-objective optimization

Recent advances in segmented solid-state detector arrays for rare-event searches have allowed the technology to approach the ton-scale in detector mass and the scale of meters in size. Often focused around searches for neutrinoless double-beta decay or direct dark matter detection, such experiments also have the capability to search for exotic particles that leave track-like signatures across their volume. However, the segmented nature of such detector arrays often sets the spatial resolution and makes the problem of reconstructing track-like paths non-trivial. In this paper, we present an algorithm that improves reconstruction of track-like events in segmented detectors using multi-objective optimization — a computational technique that optimizes more than one cost function at a time without specifying a quantitative weighting between them. Such a technique allows the reconstruction of tracks through a detector and the determination of path-lengths through individual elements. When combined with the reconstructed energy depositions in each element this allows for a calculation of the stopping power of track-like particles and opens the door to searches for particles with abnormal stopping power like monopoles or lightly-ionizing particles (LIPs). Results are presented which evaluate the precision of the reconstruction tools as they currently stand against Monte Carlo generated data. The algorithm is presented in the context of the CUORE experiment, but has applications to other segmented calorimeter detectors.

High performance FPGA embedded system for machine learning based tracking and trigger in sPhenix and EIC

We present a comprehensive end-to-end pipeline to classify triggers versus background events in this paper. This pipeline makes online decisions to select signal data and enables the intelligent trigger system for efficient data collection in the Data Acquisition System (DAQ) of the upcoming sPHENIX and future EIC (Electron-Ion Collider) experiments. Starting from the coordinates of pixel hits that are lightened by passing particles in the detector, the pipeline applies three-stage of event processing (hits clustering, track reconstruction, and trigger detection) and labels all processed events with the binary tag of trigger versus background events. The pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the message-passing graph neural network to predict links between hits and reconstruct tracks and a hierarchical pooling algorithm (DiffPool) to make the graph-level trigger detection. We obtain an impressive performance (⩾70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline. We deploy the end-to-end pipeline into a field-programmable gate array (FPGA) and accelerate the three stages with speedup factors of 1152, 280, and 21, respectively.

RGB-D SLAM in Indoor Planar Environments With Multiple Large Dynamic Objects

This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation and background reconstruction. Previous dynamic SLAM methods either rely on semantic segmentation to directly detect dynamic objects; or assume that dynamic objects occupy a smaller proportion of the camera view than the static background and can, therefore, be removed as outliers. Our approach, however, enables dense SLAM when the camera view is largely occluded by multiple dynamic objects with the aid of camera motion prior. The dynamic planar objects are separated by their different rigid motions and tracked independently. The remaining dynamic non-planar areas are removed as outliers and not mapped into the background. The evaluation demonstrates that our approach outperforms the state-of-the-art methods in terms of localisation, mapping, dynamic segmentation and object tracking. We also demonstrate its robustness to large drift in the camera motion prior.

A4LidarTag: Depth-Based Fiducial Marker for Extrinsic Calibration of Solid-State Lidar and Camera

Visual-based simultaneous localization and mapping (SLAM) systems perform weakly in object tracking and map reconstruction due to the unreliable depth measurement originating from image-only data. Light Detection and Ranging (LiDAR) can be coupled to overcome the drawback of uncertain depth estimation. The prerequisite for performing data fusion is to align visual-Lidar sensors to a specific coordinate system with extrinsic pose by calibrating. The conventional extrinsic calibration frameworks either rely on markers in artificial large-size calibration boards or uncontrollable natural scenes (Fig. 2 ), limiting stability and convenience. In this paper, we have designed a novel marker pattern, A4LidarTag, composed of circular holes. The difference in depth measurement is used to encode location information. Based on A4LidarTag, the automatic extrinsic calibration framework between solid-state Lidar (SSL) and the camera is developed. The proposed framework can be implemented in close range (within 1 m) and on an A4-size calibration board. The average reprojection error resulting from Lidar point clouds projection is about 0.12pixels. Experiments show excellent efficiency and versatility in both indoor and outdoor scenes.

A light tracker based on scintillating fibers with SiPM readout

We have developed a novel light tracker based on plastic scintillating fiber arrays readout with Silicon Photomultipliers (SiPMs). The tracker consists of multiple planes, with the fibers in each plane oriented perpendicularly to those in the adjacent plane, in order to allow 3D track reconstruction. The fibers in each plane have round cross sections, with a diameter of 500μm, and are arranged in two staggered layers in a close-packed configuration. The fibers are readout by means of SiPM arrays with a 250μm strip pitch placed at one of their ends. Scintillating fibers allow a reduced material budget while providing a good spatial resolution and a fast response. This design is therefore suitable to track low-energy particles, such as the lowest energy cosmic rays or the electrons produced in Compton scatterings of gamma rays with energies down to 100 keV. We have built a detector prototype, equipped with Hamamatsu 128-channel SiPM arrays, readout with 32-channel PETIROC2A front-end ASICs. These ASICs are controlled by a custom data acquisition system board equipped with Xilinx Kintex-7 FPGA with self-triggering capabilities. The prototype has been tested with particle beams, cosmic rays and radioactive sources. The tracker design will be presented and performance of the prototype will be discussed.

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

BioFace-3D

Over the last decade, facial landmark tracking and 3D reconstruction have gained considerable attention due to their numerous applications, such as human-computer interactions, facial expression analysis, emotion recognition, etc. However, existing camera-based solutions require users to be confined to a particular location and face a camera at all times without occlusions, which largely limits their usage in practice. To overcome these limitations, we propose the first single-earpiece lightweight biosensing system, Bioface-3D, that can unobtrusively, continuously, and reliably sense the entire facial movements, track 2D facial landmarks, and further render 3D facial animations. Without requiring a camera positioned in front of the user, this paradigm shift from visual sensing to biosensing would introduce new opportunities in many emerging mobile and IoT applications.

IMPROVED EDGE DETECTION FOR SATELLITE IMAGES

Abstract. Edges are a key feature employed in various computer vision applications namely segmentation, object recognition, feature tracking and 3D reconstruction. Edges provide key information with regards to object presence, shape, form and detail which aid in many computer vision tasks. While there are various edge detection techniques in literature, challenges in edge detection remain. Varying image contrast due to non uniform scene illumination and imaging resolution affects the edge information obtained from any given image. The edge detection results are characterised by missing edges, edge fragmentation and some false positive edges. Gradient based edge detectors are the most commonly used detectors. These detectors all suffer from aforementioned challenges. In this, paper we present an edge detection framework that aims to recover long unfragmented edges from satellite images. This is achieved by using an edge accumulator that operates on the entire edge detection parameter space. Gradient based edge detectors rely on thresholding to retrieve salient edges. This usually results in missed or noisy edges. To counter this, the accumulator is run over a wide parameter space, growing edges at each accumulator level while maintaining edge position using a localisation filter. The results are longer unbroken edges that are detected for most objects, even in shadowy regions and low contrast areas. The results show improved edge detection that preserves the form and detail of objects when compared to current gradient based detectors.