Particle Reconstruction Research Articles

Reconstruction of unstable heavy particles using deep symmetry-preserving attention networks

Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. An approach based on a generalized attention mechanism, symmetry preserving attention networks (SPA-NET), has been previously applied to top quark pair decays at the Large Hadron Collider which produce only hadronic jets. Here we extend the SPA-NET architecture to consider multiple input object types, such as leptons, as well as global event features, such as the missing transverse momentum. In addition, we provide regression and classification outputs to supplement the parton assignment. We explore the performance of the extended capability of SPA-NET in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson. We find significant improvements in the power of three representative studies: a search for tt¯H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\bar{t}H$$\\end{document}, a measurement of the top quark mass, and a search for a heavy Z′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${Z}^{{\\prime} }$$\\end{document} decaying to top quark pairs. We present ablation studies to provide insight on what the network has learned in each case.

Atomic force microscopy 3D structural reconstruction of individual particles in the study of amyloid protein assemblies.

Recent developments in atomic force microscopy (AFM) image analysis have made three-dimensional (3D) structural reconstruction of individual particles observed on 2D AFM height images a reality. Here, we review the emerging contact point reconstruction AFM (CPR-AFM) methodology and its application in 3D reconstruction of individual helical amyloid filaments in the context of the challenges presented by the structural analysis of highly polymorphous and heterogeneous amyloid protein structures. How individual particle-level structural analysis can contribute to resolving the amyloid polymorph structure-function relationships, the environmental triggers leading to protein misfolding and aggregation into amyloid species, the influences by the conditions or minor fluctuations in the initial monomeric protein structure on the speed of amyloid fibril formation, and the extent of the different types of amyloid species that can be formed, are discussed. Future perspectives in the capabilities of AFM-based 3D structural reconstruction methodology exploiting synergies with other recent AFM technology advances are also discussed to highlight the potential of AFM as an emergent general, accessible and multimodal structural biology tool for the analysis of individual biomolecules.

Long-lived particle decays at the proposed MATHUSLA experiment

We carefully study the decay and reconstruction of long-lived particle (LLP) decays in the proposed MATHUSLA LLP detector for the HL-LHC. Our investigations are focused on three LLP benchmark models. MATHUSLA’s primary physics target is represented by hadronically decaying LLPs with mass above ∼10 GeV, produced in exotic Higgs decays. We also investigate GeV-scale scalar and right-handed neutrino LLPs, which are the target of many other proposed experiments. We first introduce a public astim code to allow for efficient signal-only studies of LLP decays in MATHUSLA and general external LLP detectors. For each of our benchmark scenarios, we carefully simulate LLP production and decay, and make our simulation library publicly accessible for future investigations and comparisons with other experiments. We then systematically study the geometric acceptance of MATHUSLA for LLP decays in these scenarios, and present updated sensitivity projections that include these acceptances. Our results show that the idealized reach of MATHUSLA computed in earlier studies is mostly realized. We also investigate possible ways of increasing the signal acceptance using the inherent geometric flexibility of the FastSim, which will provide useful inputs for realistic experimental and engineering optimization of the detector in the future. Published by the American Physical Society 2024

Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method.

Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs.

A polynomial model with line-of-sight constraints for Lagrangian particle tracking under interface refraction

This paper introduces an improvement of the ‘shake-the-box (STB)’ (Schanz, Gesemann, and Schröder, Exp. Fluids 57.5, 2016) technique using the polynomial calibration model and the line-of-sight constraints (LOSC) to overcome the refractive interface issues in Lagrangian particle tracking (LPT) measurement. The method (named LOSC-LPT) draws inspiration from the two-plane polynomial camera calibration in tomographic particle image velocimetry (Worth, Nickels, Thesis, 2010) and the STB-based open-source Lagrangian particle tracking (OpenLPT) framework (Tan, Salibindla, Masuk, and Ni, Exp. Fluids 61.2, 2019). The LOSC-LPT introduces polynomial mapping functions into STB calibration in conditions involving gas–solid–liquid interfaces at container walls exhibiting large refractive index variations, which facilitates the realization of particle stereo matching, three-dimensional (3D) triangulation, iterative particle reconstruction, and further refinement of 3D particle position by shaking the LOS. Performance evaluation based on synthetic noise-free images with a particle image density of 0.05 particle per pixel in the presence of refractive interfaces demonstrates that LOSC-LPT can detect a higher number of particles and exhibits lower position uncertainty in the reconstructed particles, resulting in higher accuracy and robustness than that achieved with OpenLPT. In the application to an elliptical jet flow in an octagonal tank with refractive interfaces, the use of polynomial mapping results in smaller errors (mean calibration error <0.1 px) and thus more long trajectories identified by LOSC-LPT (13 000) compared with OpenLPT (4500) which uses the pinhole Tsai model (mean calibration error >1.0 px). Moreover, 3D flow-field reconstructions demonstrate that the LOSC-LPT framework can recover a more accurate 3D Eulerian flow field and capture more complete coherent structures in the flow, and thus holds great potential for widespread application in 3D experimental fluid measurements.

Uncertainty elimination in 2D shape reconstruction of irregular particles in Interferometric particle imaging

Interferometric Particle Imaging (IPI) technique is presently utilized in measuring irregular particle whose shape and size are indispensable parameters in various scientific fields, while orientation and size uncertainty are tough problems in IPI measurement. Herein, an effective method is proposed to eliminate aforementioned uncertainties. Based on Square Gradient function and multi-plane phase retrieval algorithm, our method eliminates conjugate solution in single-plane shape reconstruction thereby making the orientation of the reconstructed particle shape unique, while position search of focused image has significantly reduced the size error of reconstructed particle shape. Dual-detector IPI system is constructed to measure actual irregular particles whose shapes are successfully recovered from detected defocused speckles with an average shape similarity of 0.960 in Jaccard Index, and theoretical size error greatly decreases from 44.7% to 0.8% under the same system configuration. The proposed method is potential in further work such as real-time particle measurement and 3D shape reconstruction.

Phase reconstruction and singularity recovery of submicron particles in far-field phase space data using deep learning networks

Phase images often contain more comprehensive information compared to amplitude images. The precise visualization of phase images generated from light scattering by submicron particles is paramount for investigating the underlying scattering mechanisms and exploring applications centered around these submicron particles. This study showcases the efficacy of neural networks in acquiring the capacity to conduct phase reconstruction and recover singularities through appropriate training. Our deep learning-based approach introduces an entirely novel framework for phase recovery, accomplished by learning the distribution of scattered light fields from submicron particles, as obtained through a polarized indirect microscopic imaging (PIMI) system, within a custom Poincaré sphere (phase space). We validate this method by successfully reconstructing phase images of polystyrene (PS) balls with varying radii, and comparing diverse network structures and data flows in the process. Furthermore, we substantiate our experimental results by corroborating them with finite difference time domain (FDTD) simulations. These findings underscore the remarkable regularity within the distribution of phase space data, opening up new avenues for advancing the study of nanostructures.

In situ Tracking the Constrained Reconstruction of Cu3Pd@SiO2 Nanoparticles Driven by Redox Atmospheres

AbstractEndowed with flexible surface coordination and synergetic electronic status, bimetallic particles serve as promising heterogeneous catalysts as their microstructures evolved sensitively to the treatment atmospheres, whereas knowledge of dynamic manners is less. Herein, utilizing environmental transmission electron microscopy (ETEM), the reconstructions of Cu3Pd particles in oxidization/reduction atmospheres were explored at atomic scale. Specifically, bare Cu3Pd particles went through a phase separation of CuOx and PdOx during in situ oxidation, and subsequent agglomeration after H2 reduction. While protected in a silica shell, the confined Cu3Pd particles were oxidized into Cu1.5Pd0.5O2 phase after air calcination and subsequently restructured into versatile configurations during H2 reduction. Specifically, hollow Cu3Pd alloy architecture with Pd enriched layer near surface as reduced at 200 °C. Further rising 400 to 600 °C, it yielded disordered Cu3Pd alloys with slightly Pd atoms enrichment at outmost surface. The dynamical behaviors of single Cu1.5Pd0.5O2 particle during in situ reduction have been visualized in ETEM, wherein a series of deformation, elongation and rotation is involved during the hollow architecture firstly formation, and then vanished into a Cu3Pd solid solution nanoparticle. The tunable microstructures of Cu3Pd@SiO2 driven by redox atmospheres demonstrate efficient approach for precisely regulating the chemical environments of constrained bimetallic nanocatalysts.

Visual analytics system for understanding DeepRL-based charged particle tracking

In 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.

Generation of 3D realistic geological particles using conditional generative adversarial network aided spherical harmonic analysis

The reconstruction of 3D realistic geological particles remains a significant challenge in the field of granular mechanics. Specifically, numerous spherical harmonic (SH) based generation frameworks have been proposed to synthetic new particle shapes retaining majority particle morphology yet having a certain variety. However, given the fact of assuming one or more established distributions or ignoring secondary particle features, the regenerated particles inevitably lose certain diversities. To address this issue, the deep learning method, conditional generative adversarial network (CGAN) was introduced to the SH analysis for particle shape regeneration. Three kinds of sand particles were synthesized and compared with their real mother particle samples concerning the distribution features of SH coefficients and particle shape parameters for validation. Results prove the proposed method has a good reliable and diverse regeneration performance. This approach is promising to facilitate a more reality closer research on 3D particle-related issues in the future.

Application of quantum machine learning in a Higgs physics study at the CEPC

Machine learning has blossomed in recent decades and has become essential in many fields. It significantly solved some problems in particle physics — particle reconstruction, event classification, etc. However, it is now time to break the limitation of conventional machine learning with quantum computing. A support-vector machine algorithm with a quantum kernel estimator (QSVM-Kernel) leverages high-dimensional quantum state space to identify a signal from backgrounds. In this study, we have pioneered employing this quantum machine learning algorithm to study the [Formula: see text] process at the Circular Electron–Positron Collider (CEPC), a proposed Higgs factory to study electroweak symmetry breaking of particle physics. Using 6 qubits on quantum computer simulators, we optimized the QSVM-Kernel algorithm and obtained a classification performance similar to the classical support-vector machine algorithm. Furthermore, we have validated the QSVM-Kernel algorithm using 6-qubits on quantum computer hardware from both IBM and Origin Quantum: the classification performances of both are approaching noiseless quantum computer simulators. In addition, the Origin Quantum hardware results are similar to the IBM Quantum hardware within the uncertainties in our study. Our study shows that state-of-the-art quantum computing technologies could be utilized by particle physics, a branch of fundamental science that relies on big experimental data.

Measurements With A Quantum Vision Transformer: A Naive Approach

In mainstream machine learning, transformers are gaining widespread usage. As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications. In particular, transformers for High Energy Physics (HEP) experiments continue to be investigated for tasks including jet tagging, particle reconstruction, and pile-up mitigation. An improved Quantum Vision Transformer (QViT) with a quantum-enhanced self-attention mechanism is introduced and discussed. A shallow circuit is proposed for each component of self-attention to leverage current Noisy Intermediate Scale Quantum (NISQ) devices. Variations of the hybrid architecture/model are explored and analyzed. The results demonstrate a successful proof of concept for the QViT, and establish a competitive performance benchmark for the proposed design and implementation. The findings also provide strong motivation to experiment with different architectures, hyperparameters, and datasets, setting the stage for implementation in HEP environments where transformers are increasingly used in state of the art machine learning solutions.

KinKal: A Kinematic Kalman filter Track Fitting Package for the Mu2e Experiment

Many current physics experiments require precision reconstruction of low-energy particles. One example is the Mu2e experiment, which requires reconstructing an isolated 105 MeV electron with better than 500 KeV/c momentum resolution. Mu2e uses a low-mass straw tube tracker, and a CsI crystal calorimeter, to reconstruct tracks. In this paper, we present the design and performance of a track reconstruction algorithm optimized for Mu2e’s unusual requirements. The algorithm is based on the KinKal [1] kinematic Kalman filter track fit package. KinKal supports multiple track parameterizations, including one optimized for looping tracks, such as Mu2e signal tracks, and others optimized for straight or slightly-curved tracks, such as the high-momentum (&gt;1 GeV/c) cosmic ray muons used to calibrate and align the Mu2e detectors. All KinKal track parameterizations include the track origin time, to correctly model correlations arising from measurements that couple time and space, such as the straw drift time or the calorimeter cluster time. KinKal employs magnetic field inhomogeneity and material effect correction algorithms with 10−4 fractional precision. The Mu2e fit uses Artificial Neural Net functions to discriminate background hits from signal hits, and to resolve the straw tube hit left-right ambiguity, while iterating the extended Kalman filter. The efficiency, accuracy, and precision of the Mu2e track reconstruction, as tested on detailed simulations of Mu2e data, are presented.

Development of particle flow algorithms based on Neural Network techniques for the IDEA calorimeter at future colliders

IDEA (Innovative Detector for an Electron-positron Accelerator) is an innovative general-purpose detector concept, designed to study electron-positron collisions at future e+e− circular colliders. The detector will be equipped with a dual read-out calorimeter able to measure separately the hadronic component and the electromagnetic component of the showers initiated by the impinging hadrons. Particle flow algorithms (PFAs) have become the paradigm of detector design for the high energy frontier and this work focuses on a project to build a particle flow algorithm for the IDEA detector using Machine Learning (ML) techniques. ML is used for particle reconstruction and identification profiting of the high granularity of the fiber-based dual-readout calorimeter. Neural Networks (NN) are built for electron reconstruction inside the calorimeter. The performance of several NN architectures is shown, with particular attention to the layer setup and the activation function choices. The performance is evaluated on the energy resolution function of the reconstructed particles. The algorithm is trained using both parallel CPUs and GPU, and the time performance and the memory usage of the two approaches are systematically compared.

Development of an individual 3D particle reconstruction method for discrete mechanical modeling: Interpolation by Fourier composition

Reconstruction methods for granular materials are an essential step in achieving an accurate geometrical basis in the use of particle methods such as the well-known discrete element method. In this study, a novel continuous analytical method will be proposed to achieve individual 3D reconstructions of granular materials. For this purpose, the 2D Fourier Descriptor theory is used and a generalization of it to the 3D case is obtained, which is quite different from the spherical harmonics theory. Some limitations existing in the original model will be addressed by developing and calibrating a generalized approach that results in a total of four main models. Particles from a study sample are reconstructed, and the models are evaluated based on four generalized error metrics, as well as the presence of some specific limitations detected in the models. Finally, the models are compared using a decision-making method in different decisional scenarios.

The study of optical properties of single soot aggregate using three-dimension soft X-ray tomographic reconstruction

The linear depolarization ratio (LDR) is a key parameter used in remote sensing to distinguish particle types. The LDR of the soot fractal model is uneasy to match the measured values. In this paper we used three-dimensional (3D) synchrotron soft X-ray tomography (SXT) for the 3D reconstruction of the toluene-burning soot particles and used discrete dipole approximation to calculate optical properties. The obtained results showed the 3D reconstructed soot particles can efficiently reproduce the measured LDR. The results of this study will encourage us to develop a new morphology model, completely different from the traditional fractal model, for bare soot based on 3D reconstruction. This study is helpful for the understanding and explanation of LDR of bare soot particles.

Fractal simulation of hydrophobic flocculation fragmentation and reconstruction of coal particles

Based on a diffusion limited cluster aggregation model, this study analyzed the fragmentation and reconstruction of flocs during the hydrophobic flocculation process of coal particles. The flocs gyration radius and gyration fractal dimension calculated by the program characterize the geometric size and spatial structure of the coal particle flocs, respectively. With advancing step length, the distributions of gyration radius first increased and then decreased and finally basically stabilized at approximately 1.70 ± 0.2, indicating that the reconstruction of flocs is reversible in geometric size. However, the gyration fractal dimensions first decreased and then increased and finally stabilized at 1.49, 1.63, 1.60 and 1.51 with increasing step length, showing variability. This indicated that the spatial structure of the flocs is irreversible. The above conclusions of the simulation analysis were confirmed by the floc particle size distribution curves and the image analysis of surface fractal dimensions. The study clarifies the morphological changes and fragmentation-reconstruction changes of flocs and improves the hydrophobic flocculation mechanism of coal particles.

3D trajectory, morphology and phase discrimination of burning Al/AP propellant particles with time-resolved astigmatic dual-beam interferometric particle imaging

This work implements astigmatic dual-beam interferometric particle imaging (ADIPI) in propellant combustion diagnostics to enable the measurement of the three-dimensional (3D) spatial position and particle morphology of opaque particles. The ADIPI allows the discrimination of different substances, such as metal droplets, flames, and pyrolysis residues. A general theoretical model based on the generalized Huygens-Fresnel integral and the ray transfer matrix model is developed to describe the identification of ADIPI signals for regular and irregular particles. An experimental system combining a 13 kHz time-resolved ADIPI with digital in-line holography (DIH) is built to enable simultaneous measurements of the propellant combustion field. The typical ADIPI signals presented by different substances in the combustion field are analyzed by comparing the experimental results of DIH. The time-resolved 3D trajectory reconstruction of burning particles and the measurement of irregular particle size and morphology are realized. Due to the effect of the flame, the average deviation of the depth locating of the particles is 1.4 mm with a standard deviation of 1.0 mm. The average deviation value of the depth position relative to the measured distance is 1%. In propellant combustion measurements, the motion of burning particles can be accurately determined, proving its feasibility and practicality.

Seeing the Supracolloidal Assemblies in 3D: Unraveling High-Resolution Structures Using Electron Tomography.

Transmission electron microscopy (TEM) imaging has revolutionized modern materials science, nanotechnology, and structural biology. Its ability to provide information about materials' structure, composition, and properties at atomic-level resolution has enabled groundbreaking discoveries and the development of innovative materials with precision and accuracy. Electron tomography, single particle reconstruction, and microcrystal electron diffraction techniques have paved the way for the three-dimensional (3D) reconstruction of biological samples, synthetic materials, and hybrid nanostructures at near atomic-level resolution. TEM tomography using a series of two-dimensional (2D) projections has been used extensively in biological science, but in recent years it has become an important method in synthetic nanomaterials and soft matter research. TEM tomography offers unprecedented morphological details of 3D objects, internal structures, packing patterns, growth mechanisms, and self-assembly pathways of self-assembled colloidal systems. It complements other analytical tools, including small-angle X-ray scattering, and provides valuable data for computational simulations for predictive design and reverse engineering of nanomaterials with the desired structure and properties. In this perspective, I will discuss the importance of TEM tomography in the structural understanding and engineering of self-assembled nanostructures with specific emphasis on colloidal capsids, composite cages, biohybrid superlattices with complex geometries, polymer assemblies, and self-assembled protein-based superstructures.

In Situ Single Particle Reconstruction Reveals 3D Evolution of PtNi Nanocatalysts During Heating.

Tailoring nanoparticles' composition and morphology is of particular interest for improving their performance for catalysis. A challenge of this approach is that the nanoparticles' optimized initial structure often changes during use. Visualizing the three dimensional(3D) structural transformation in situ is therefore critical, but often prohibitively difficult experimentally. Although electron tomography provides opportunities for 3D imaging, restrictions in the tilt range of in situ holders together with electron dose considerations limit the possibilities for in situ electron tomography studies. Here, an in situ 3D imaging methodology is presented using single particle reconstruction (SPR) that allows 3D reconstruction of nanoparticles with controlled electron dose and without tilting the microscope stage. This in situ SPR methodology is employed to investigate the restructuring and elemental redistribution within a population of PtNi nanoparticles at elevated temperatures. The atomic structure of PtNi is further examined and a heat-induced transition is found from a disordered to an ordered phase. Changes in structure and elemental distribution are linked to a loss of catalytic activity in the oxygen reduction reaction. The in situ SPR methodology employed here can be extended to a wide range of in situ studies employing not only heating, but gaseous, aqueous, or electrochemical environments to reveal in-operando nanoparticle evolution in 3D.