Particle Reconstruction Research Articles

The Open Data Detector Tracking System

Charged particle reconstruction in High Energy Physics experiments is a significant part of overall event reconstruction. Depending on the physics environment, for instance in collider experiments with high multiplicities or luminosities, the tracking problem increases in complexity and often poses not only an algorithmic, but also a computational challenge. With the high-luminosity phase of the LHC at CERN approaching, research for new approaches and algorithms for track reconstruction has seen an increased interest. Both new technological approaches like hardware accelerators, as well as machine learning are being developed. However, testing and developing these new approaches against the existing experiments’ software stacks can prove to be challenging, as they typically focus on stable data taking, discouraging disruptive changes. This document presents a virtual tracking detector that is designed to be a simplified, but realistic model of a real-world detector, that can serve as a robust testbed for new developments.

Improved method for generating sand assembly with wide range of aspect ratios and angularities based on insights gained from comparative study of existing techniques

The generation of realistic sand assemblies with a wide range of particle shapes is important for investigating the influence of particle shape on the microscale and macroscale mechanical behaviors of sands using the discrete element method. The major contribution of this study is the development of an improved technique for such assemblies. The method generates sand assemblies with a wide range of aspect ratios and angularities and retains the particle size effects and major morphological characteristics of the original particles. First, based on the insights gained from a comparative study of existing techniques, a method that integrates principal component analysis (PCA) with coefficient standardization is selected as the basic framework for particle generation. Second, by expanding the spherical harmonics (SHs) of the compensation radii between particles and their circumscribed super-ellipsoid surfaces, a new method that can extract the aspect ratio characteristics of sand particles is proposed for particle reconstruction. Finally, a method integrating the SH expansion of the compensation radius, PCA, and coefficient standardization is formulated and demonstrated to be efficient in generating sand assemblies with a wide range of aspect ratios and angularities.

Structure-guided mutagenesis of the capsid protein indicates that a nanovirus requires assembled viral particles for systemic infection.

Nanoviruses are plant multipartite viruses with a genome composed of six to eight circular single-stranded DNA segments. The distinct genome segments are encapsidated individually in icosahedral particles that measure ≈18 nm in diameter. Recent studies on the model species Faba bean necrotic stunt virus (FBNSV) revealed that complete sets of genomic segments rarely occur in infected plant cells and that the function encoded by a given viral segment can complement the others across neighbouring cells, presumably by translocation of the gene products through unknown molecular processes. This allows the viral genome to replicate, assemble into viral particles and infect anew, even with the distinct genome segments scattered in different cells. Here, we question the form under which the FBNSV genetic material propagates long distance within the vasculature of host plants and, in particular, whether viral particle assembly is required. Using structure-guided mutagenesis based on a 3.2 Å resolution cryogenic-electron-microscopy reconstruction of the FBNSV particles, we demonstrate that specific site-directed mutations preventing capsid formation systematically suppress FBNSV long-distance movement, and thus systemic infection of host plants, despite positive detection of the mutated coat protein when the corresponding segment is agroinfiltrated into plant leaves. These results strongly suggest that the viral genome does not propagate within the plant vascular system under the form of uncoated DNA molecules or DNA:coat-protein complexes, but rather moves long distance as assembled viral particles.

Fast and Robust Single Particle Reconstruction in 3D Fluorescence Microscopy

Single particle reconstruction has recently emerged in 3D fluorescence microscopy as a powerful technique to improve the axial resolution and the degree of fluorescent labeling. It is based on the reconstruction of an average volume of a biological particle from the acquisition of multiple views with unknown poses. Current methods are limited either by template bias, restriction to 2D data, high computational cost or lack of robustness to low fluorescent labeling. In this work, we propose a single particle reconstruction method dedicated to convolutional models in 3D fluorescence microscopy that overcomes these issues. We address the joint reconstruction and estimation of the poses of the particles, which translates into a challenging non-convex optimization problem. Our approach is based on a multilevel reformulation of this problem, and the development of efficient optimization techniques at each level. We demonstrate on synthetic data that our method outperforms the standard approaches in terms of resolution and reconstruction error, while achieving a low computational cost. We also perform successful reconstruction on real datasets of centrioles to show the potential of our method in concrete applications.

2D shape reconstruction of irregular particles with deep learning based on interferometric particle imaging.

Interferometric particle imaging (IPI) technology is widely used in the measurement of various particles. Obtaining particle shape information directly by IPI is challenging because of the complex relationship between the speckle distribution of interference-defocused speckle patterns and the shape of the corresponding irregular particles. Considering this challenge, we implement a deep learning method based on the convolutional neural network (CNN) to reconstruct defocused images of sand particles with sparse features. We also introduce the negative Pearson correlation coefficient as the loss function. To verify the feasibility of our method, we implemented it to reconstruct defocused images obtained from IPI experiments. Finally, compared with another common CNN-based structure, we confirmed that our network structure has good performance in the shape reconstruction of irregular particles.

A substrate-induced gating mechanism is conserved among Gram-positive IgA1 metalloproteases

The mucosal adaptive immune response is dependent on the production of IgA antibodies and particularly IgA1, yet opportunistic bacteria have evolved mechanisms to specifically block this response by producing IgA1 proteases (IgA1Ps). Our lab was the first to describe the structures of a metal-dependent IgA1P (metallo-IgA1P) produced from Gram-positive Streptococcus pneumoniae both in the absence and presence of its IgA1 substrate through cryo-EM single particle reconstructions. This prior study revealed an active-site gating mechanism reliant on substrate-induced conformational changes to the enzyme that begged the question of whether such a mechanism is conserved among the wider Gram-positive metallo-IgA1P subfamily of virulence factors. Here, we used cryo-EM to characterize the metallo-IgA1P of a more distantly related family member from Gemella haemolysans, an emerging opportunistic pathogen implicated in meningitis, endocarditis, and more recently bacteremia in the elderly. While the substrate-free structures of these two metallo-IgA1Ps exhibit differences in the relative starting positions of the domain responsible for gating substrate, the enzymes have similar domain orientations when bound to IgA1. Together with biochemical studies that indicate these metallo-IgA1Ps have similar binding affinities and activities, these data indicate that metallo-IgA1P binding requires the specific IgA1 substrate to open the enzymes for access to their active site and thus, largely conform to an “induced fit” model.

Momentum reconstruction of charged particles using multiple Coulomb scatterings in a nuclear emulsion detector

Abstract This paper describes a new method for momentum reconstruction of charged particles using multiple Coulomb scatterings in a nuclear emulsion detector with a layered structure of nuclear emulsion films and target materials. The method utilizes the scattering angles of particles precisely measured in the emulsion films. The method is based on the maximum likelihood to include the new information on the decrease of the energy as the particle travels through the detector. According to the Monte Carlo simulations, this method can measure momentum with a resolution of 10% for muons of ${500}\, {\rm MeV}/c$ passing through the detector perpendicularly. The momentum resolution is evaluated to be 10–20%, depending on the momentum and emission angle of the particle. By accounting for the effect of the energy decrease, the momentum can be reconstructed correctly with less bias, particularly in the low-momentum region. We apply this method to measure the momentum of muon tracks detected in the Neutrino Interaction research with Nuclear emulsion and J-PARC Accelerator (NINJA) experiment where the momentum is also measured independently by using the track range. The two measurements agree well within experimental uncertainties, verifying the method experimentally. This method will extend the measurable phase space of muons and hadrons in the NINJA experiment.

High-resolution cryo-EM performance comparison of two latest-generation cryo electron microscopes on the human ribosome

Recent technological advances in cryo electron microscopy (cryo-EM) have led to new opportunities in the structural biology field. Here we benchmark the performance of two 300 kV latest-generation cryo electron microscopes, Titan Krios G4 from Thermofisher Scientific and CRYO ARM 300 from Jeol, with regards to achieving high resolution single particle reconstructions on a real case sample. We compare potentially limiting factors such as drift rates, astigmatism & coma aberrations and performance during image processing and show that both microscopes, while comprising rather different technical setups & parameter settings and equipped with different types of energy filters & cameras, achieve a resolution of around 2 Å on the human ribosome, a non-symmetric object which constitutes a key drug target. Astigmatism correction, CTF refinement and correction of higher order aberrations through refinement in separate optics groups helped to account for astigmatism/coma caused by beam tilting during multi-spot and multi-hole acquisition in neighbouring holes without stage movement. The obtained maps resolve Mg2+ ions, water molecules, inhibitors and side-chains including chemical modifications. The fact that both instruments can resolve such detailed features will greatly facilitate understanding molecular mechanisms of various targets and helps in cryo-EM structure based drug design. The methods and analysis tools used here will be useful also to characterize existing instruments and optimize data acquisition settings and are applicable broadly to other drug targets in structural biology.

The ages of pragmatic particles in Colloquial Singapore English

Abstract The study aims to work towards a diachronic reconstruction of pragmatic particles in Colloquial Singapore English (CSE, also known as “Singlish”) by exploiting an unused historical data source: The Oral History Interviews held by the National Archives of Singapore (OHI-NAS). We investigate the distribution of five pragmatic particles (ah, lah, leh, lor, and meh) in 101 interviews conducted between 1979 and 2009 in speakers born between 1899 and 1983. Lim (2007) reconstructs the origin of these particles in different substrate languages, with the first two particles (ah and lah) being traceable to earlier Bazaar Malay and/or Hokkien, while the latter three (leh, lor, and meh) are of later Cantonese origin. The results of the present study show that ah and lah are the most frequent particles attested earliest. Their frequency of use increases over time, being additionally contingent on the gender and age of the speakers, their educational level, and their ethnic background. The particles ah and lah are mostly used in assertive contexts.

Deep-learning-enhanced model reconstruction of realistic 3D rock particles by intelligent video tracking of 2D random particle projections

Deep-learning-enhanced model reconstruction of realistic 3D rock particles by intelligent video tracking of 2D random particle projections

Shake-The-Box 3D particle tracking for two-pulse recordings

The Shake-The-Box (STB) three-dimensional Lagrangian Particle Tracking (LPT) technique introduced the concept of particle prediction in order to integrate the temporal domain into the particle reconstruction process based on Iterative Particle Reconstruction. Taking advantage of long time-resolved (TR) recording sequences, the STB technique is able to cope with high particle image densities while delivering the accurate measurement of the particles position, velocity and acceleration along individual tracks. The Multi-Pulse STB extended the range of applicability of the algorithm to high flow velocities, where time-resolved recordings cannot be attained due to the limitation frequency of the high-speed acquisition systems, by adopting multi-pulse systems (i.e. dual illumination and imaging setup). Nevertheless, dual-frame 3D acquisition systems, consisting in a dual-cavity laser and double-frame cameras, remain commonly used for many particle-image-based investigations in a wide range of flow velocities and applications. As a consequence, a 3D LPT approach capable of dealing with two-pulse recordings is of high interest for both the scientific community and industry. In the present study, a Two-Pulse Shake-The-Box approach (TP-STB) is proposed, based on the advanced IPR algorithm presented by Jahn et al. 2021, in combination with the iterative scheme of reconstruction and tracking introduced for the Multi-Pulse STB algorithm development. The performances of TP-STB are assessed by means of comparison with the results from the time-resolved STB algorithm (TR-STB) on synthetic data. Application to an experimental dataset of Rayleigh-Bénard-Convection proves the concept in real-life conditions.

3D Particle Position Determination And Correction At High Particle Densities

The method of Iterative Particle Reconstruction (IPR), introduced by Wieneke in 2013, constitutes a major step towards high-density 3D Lagrangian Particle Tracking. It reconstructs 3D particle positions from their projections onto several cameras. In the first part of this work, we present several approaches to enhance the original IPR working principle which, in combination, nearly triple the processable particle image densities, allowing complete and ghost-free reconstructions on a single snapshot from a four-camera system at up to 0.14 particles-per pixel (ppp). The updated method is proven to be fast, accurate and robust against image noise and other imaging artifacts. A central piece of the IPR functionality is a position optimization algorithm, using the difference of the local re-projected and original images (the residual images) as a cost function and displacing particles along its steepest gradient ('shaking' of the particles). The same approach is used within the Shake-The-Box (STB) Lagrangian Particle Tracking (LPT) scheme to correct predicted particle positions. The positional errors to be corrected during IPR-processing are typically in the sub-pixel range, however larger errors can occur at the prediction stage of STB. The second part of this manuscript quantifies the ability of the position optimization to successfully correct misplaced particles and proposes a method to further increase this range. Cost-function-gradient based methods require a certain overlap of the re-projected image with the original image for any given particle. Still, a misplacement of 2-3 pixels - depending on its direction relative to the camera positions - is shown to be reliably correctable. As seen by statistics from a DNS of a turbulent cylinder flow, such high accelerations (and therefore mispredictions) are rare at typical sampling rates. Therefore, most particle predictions can be successfully optimized to the correct position. In order to additionally handle rare events of large acceleration and misplacement, an iterative grid-search is applied specifically to particles not being optimally placed yet ('Variable Space step', VS). Such particles are identified using the local shape of the cost-function gradient. Using synthetic data, it is demonstrated that this method is able to correct even large prediction errors with high reliability. Applying a low temporal sampling on the test case of turbulent cylinder flow results in 20.8 px average particle shift. In this case, using noisy image data at a particle image density of 0.1 ppp, approx. 95.3% of the particle predictions can be corrected by pure position optimization, while around 99.5 % are achieved by additionally applying VS. Transferring this approach to experimental data could further improve tracking fidelity and would allow relaxing on the temporal resolution demands.

Understanding the Onset of Surface Degradation in LiNiO2 Cathodes

Nickel-based layered oxides offer an attractive platform for the development of energy-dense cobalt-free cathodes for lithium-ion batteries but suffer from degradation via oxygen gas release during electrochemical cycling. While such degradation has previously been characterized phenomenologically with experiments, an atomic-scale understanding of the reactions that take place at the cathode surface has been lacking. Here, we discuss a first-principles methodology that we developed for the prediction of the surface reconstructions of intercalation electrode particles as a function of the temperature and state of charge. We report the surface phase diagrams of the LiNiO2(001) and (104) surfaces and identify surface structures that are likely visited during the first charge and discharge. Our calculations indicate that both surfaces experience oxygen loss during the first charge, resulting in irreversible changes to the surface structures. We identify specific reasons why Ni aggravates surface degradation and discuss possible remedies. Figure 1

(Invited) Dynamic Reconstruction of Lanthanum Nickelate Catalysts and Activation By Iron during OER

Accurate description of activity trends among perovskite oxide oxygen evolution catalysts using electronic descriptors requires that the bulk structure of the catalyst is comparable to that of the surface. Relatively few works thus far have addressed the dynamic nature of the catalyst structure during OER and the implications this has for rationalization of activity. Here the in-situ surface reconstruction of LaNiO3 particles and an analogous Ruddlesden-Popper phase, La2NiO4+ δ, is described using electrochemical and materials characterization techniques. Small, but characteristic redox features were observed during cyclic voltammetry of these materials corresponding to the Ni2+/3+ redox reaction in amorphous NiOxHy. The size of these redox features grow with prolonged cycling and chronoamperometry indicating that increased duration of electrochemical conditioning continuously induces reconstruction. The reconstructed species contributes to an increased surface area as determined by electrochemical impedance spectroscopy. Near doubling of the OER activity after intentional introduction of only 35 ppb Fe species is observed after cycling, consistent with the formation of NiOxHy on the crystalline surface and subsequent adsorption of Fe to form well-known and extremely active NiFeOxHy OER catalysts. This work brings into focus the importance of considering surface amorphization on perovskite catalysts in discussion of their activity.

DUNE atmospheric neutrinos: Earth tomography

In this paper we show that the DUNE experiment can measure the Earth’s density profile by analyzing atmospheric neutrino oscillations. The crucial feature that enables such measurement is the detailed event reconstruction capability of liquid argon time projection chambers. This allows for studying the sub-GeV atmospheric neutrino component, which bears a rich oscillation phenomenology, strongly dependent on the matter potential sourced by the Earth. We provide a pedagogical discussion of the MSW and parametric resonances and their role in measuring the core and mantle densities. By performing a detailed simulation, accounting for particle reconstruction at DUNE, nuclear physics effects relevant to neutrino-argon interactions and several uncertainties on the atmospheric neutrino flux, we manage to obtain a robust estimate of DUNE’s sensitivity to the Earth matter profile. We find that DUNE can measure the total mass of the Earth at 9.3% precision with an exposure of 400 kton-year. By accounting for previous measurements of the total mass and moment of inertia of the Earth, the core, lower mantle and upper mantle densities can be determined with 9%, 14% and 22% precision, respectively, for the same exposure. Finally, for a low exposure run of 60 kton-year, which would correspond to two far detectors running for three years, we have found that the core density could be measured by DUNE at ∼ 30% precision.

Stability analysis of varying height waste dump in open-pit mine under particle size gradation and reconstruction effect of waste materials

ABSTRACT This study determines the safety-stability factors of the waste dump benches at different heights and analyses the deformation law, stress variation, and crack propagation characteristics of typical slope models. The meso-mechanical parameters obtained by discrete element numerical simulation are modified through slope radar monitoring data. The results show that the critical height of the dumping benches of the studied open-pit mine is 60 ~ 70 m, and the amount of bench settlement is positively correlated with the height of the benches. After the critical height is exceeded, the bench forms a three-wedge failure due to uneven settlement.

ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM

Particle picking is currently a critical step in the cryo-electron microscopy single particle reconstruction pipeline. Contaminations in the acquired micrographs severely degrade the performance of particle pickers, resulting in many “non-particles” in the collected stack of particles. In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximate particle size. In particular, it does not require any parameter tuning nor manual intervention. Our method is based on the observation that the statistical distribution of contaminated regions is different from that of the rest of the micrograph. This nonrestrictive assumption allows to automatically detect various types of contaminations, from the carbon edges of the supporting grid to high contrast blobs of different sizes. We demonstrate the efficiency of our algorithm using various experimental data sets containing various types of contaminations. ASOCEM is integrated as part of the KLT picker (Eldar et al., 2020) and is available at https://github.com/ShkolniskyLab/kltpicker2.

On the tracking of shelly carbonate sands using deep learning

It is well known that carbonate sands possess weak mineralogies, complex particle morphologies and porous microstructures. These characteristics lead to very distinct mechanical properties of carbonate sands, such as low shear strength, high crushability and high permeability. This paper presents a novel investigation of the recognition and tracking of intact carbonate sand particles using a deep learning method called PointNet++. The capability of PointNet++ to extract the global and local features of the porous structures of carbonate sand particles enables it to excel in the pattern recognition of porous granular materials. First, for the reconstruction of carbonate sand particles, a set of two-dimensional raw images obtained from X-ray microtomography scanning were handled by a series of image processing techniques such as median filter, segmentation and thresholding algorithms. In particular, a special technique previously developed by the authors was used to treat the abundant intra-particle pores and surface concavities of carbonate sand particles to avoid the problem of image over-segmentation. Second, to prepare the training datasets to be used in the PointNet++ deep learning exercise, a strategy of sampling and grouping was proposed to divide the initial point set of each sand particle into several groups. Next, PointNet++ was utilised to capture the global and local context features of the sand particles at different length scales and shown to successfully recognise and track all of the particles. Finally, a comprehensive comparison between several particle tracking methods reported in the literature was made, and the outstanding advantages of the deep learning-based particle tracking method were summarised.

Understanding the Onset of Surface Degradation in LiNiO2 Cathodes

Nickel-based layered oxides offer an attractive platform for the development of energy-dense cobalt-free cathodes for lithium-ion batteries but suffer from degradation via oxygen gas release during electrochemical cycling. While such degradation has previously been characterized phenomenologically with experiments, an atomic-scale understanding of the reactions that take place at the cathode surface has been lacking. Here, we develop a first-principles methodology for the prediction of the surface reconstructions of intercalation electrode particles as a function of the temperature and state of charge. We report the surface phase diagrams of the LiNiO2(001) and (104) surfaces and identify surface structures that are likely visited during the first charge and discharge. Our calculations indicate that both surfaces experience oxygen loss during the first charge, resulting in irreversible changes to the surface structures. At the end of charge, the surface Ni atoms migrate into tetrahedral sites, from which they further migrate into Li vacancies during discharge, leading to Li/Ni mixed discharged surface phases. Further, the impact of the temperature and voltage range during cycling on the charge/discharge mechanism is discussed. The present study thus provides insight into the initial stages of cathode surface degradation and lies the foundation for the computational design of cathode materials that are stable against oxygen release.

3D tomographic reconstruction of irregular rough particles from interferometric images

Using a Digital Micromirror Device (DMD), the 3D-reconstruction of programmable rough particles (centrosymmetric or non-centrosymmetric) are done from a set of 120 interferometric images. This can be done using the error-reduction (ER) algorithm for the 2D shape reconstructions and the filtered back-projection for the 3D tomographic reconstruction.