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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. Here, we develop particle-flow neural-assisted simulations (parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the Compact Muon Solenoid (CMS) experiment. We show that parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2024

Iterative Stress Reconstruction Algorithm to Estimate Three-Dimensional Residual Stress Fields in Manufactured Components

Residual stress (RS) significantly impacts the mechanical performance of components. Measurement of RS often provides incomplete data in terms of components of stress and spatial density. Employing such fields in finite element simulations results in significant modification of the field to achieve equilibrium and compatibility among strains. To overcome this, an iterative stress reconstruction algorithm (ISRA) is developed to estimate 3D RS fields that satisfy equilibrium, are stress component-wise complete, and represent the characterized data sampled. An Al 7075-T651 plate and an additively manufactured (AM) A36 steel wall are considered for RS reconstruction using measurement data from the literature. A maximum variation of ~2.5 MPa in the Al plate, and ~10 MPa in the steel wall are observed between the reconstructed and measured stresses. Furthermore, unknown stress components emerge and reach significant magnitudes (upto ~2.3 MPa in the Al plate and ~45 MPa in the AM wall) during ISRA. Indeed, it is found that minor errors in measurement or data processing are eliminated through the physical requirements during ISRA. Employing a reconstructed RS field is hence not just more accurate given its compatibility, but it additionally corrects for minor errors in measurement. Furthermore, it is found that spatially dense measurement data result in convergence with fewer iterations. Finally, although ISRA yields a nonunique solution dependent on boundary conditions, measurement errors, fitting errors, and mesh density, it accommodates for uncertainties and inaccuracies in measurement, as opposed to failing to reach a physically realistic converged solution.

42 ENGINEERING↗
DOE OSTI2025

Black-box optimization of CT acquisition and reconstruction parameters: a reinforcement learning approach

Protocol optimization is critical in Computed Tomography (CT) for achieving desired diagnostic image quality while minimizing radiation dose. Due to the inter-effect of influencing CT parameters, traditional optimization methods rely on the testing of exhaustive combinations of these parameters. This poses a notable limitation due to the impracticality of exhaustive parameter testing. This study introduces a novel methodology leveraging Virtual Imaging Trials (VITs) and reinforcement learning to more efficiently optimize CT protocols. Computational phantoms with liver lesions were imaged using a validated CT simulator and reconstructed with a novel CT reconstruction Toolkit. The optimization parameter space included tube voltage, tube current, reconstruction kernel, slice thickness, and pixel size. The optimization process was done using a Proximal Policy Optimization (PPO) agent which was trained to maximize the Detectability Index (d’) of the liver lesion for each reconstructed image. Results showed that our reinforcement learning approach found the absolute maximum d’ across the test cases while requiring 79.7% fewer steps compared to an exhaustive search, demonstrating both accuracy and computational efficiency, offering a efficient and robust framework for CT protocol optimization. The flexibility of the proposed technique allows for use of varying image quality metrics as the objective metric to maximize for. Our findings highlight the advantages of combining VIT and reinforcement learning for CT protocol management.

Fenwick, David [Duke University Medical Center]↗
DOE OSTI2024

X-ray nano-holotomography reconstruction with simultaneous probe retrieval

In conventional tomographic reconstruction, the pre-processing step includes flat-field correction, where each sample projection on the detector is divided by a reference image taken without the sample. When using coherent X-rays as a probe, this approach overlooks the phase component of the illumination field (probe), leading to artifacts in phase-retrieved projection images, which are then propagated to the reconstructed 3D sample representation. The problem intensifies in nano-holotomography with focusing optics, which, due to various imperfections creates high-frequency components in the probe function. Here, we present a new iterative reconstruction scheme for holotomography, simultaneously retrieving the complex-valued probe function. Implemented on GPUs, this algorithm results in 3D reconstruction resolving twice thinner layers in a 3D ALD standard sample measured using nano-holotomography.

Nikitin, Viktor↗
DOE OSTI2026

Development of a pattern recognition algorithm for reconstructing multi-particle events in the Mu2e detector

Mu2e is an upcoming experiment at Fermilab and its main goal is to search for the Charged Lepton Flavor Violation (CLFV) in the coherent transition of a muon into an electron on an Al target. In Mu2e, multi-particle events can occur simultaneously within the same time region and it is crucial to accurately identify each particle track, including signals, to improve the robustness of track finding methods and enhance reconstruction efficiency. ¯p annihilation is one of the background events and produces multiple particles that can mimic signal events. Additionally, photons from radiative pion captures can produce a γ → $e+e−$ pair, which can be used to calibrate the Mu2e momentum scale and the resolution. The Mu2e track reconstruction sequence begins by grouping hits produced in the tracker based on time and z coordinate information, called TimeCluster, and selected hits are processed to reconstruct helices and determine their momentum. The current pattern recognition algorithms identify a single helix per TimeCluster for single track events. A new pattern recognition algorithm is being developed to reconstruct multi-particle events and its features for finding multiple tracks and the current evaluation results are reported.

Kitagawa, H. [Pisa U.]↗
DOE OSTI2025

Selecting 1D projections for 2D tomography reconstruction

Previous works on reconstructing the 4D phase space using tomography require optimal selection of projection views to achieve accurate reconstruction. In 2D reconstruction, the process is straightforward, as an object can be evenly sampled by dividing the angles evenly. However, extending this concept from 2D to 4D is not intuitive. This work demonstrates that quaternions can be used to more effectively describe views in 4D and introduces the Fibonacci Flower algorithm and repulsive force algorithm to evenly space views in 4D space in order to achieve higher reconstruction accuracy.

Accelerator Physics↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Track Reconstruction using Graph Neural Networks in the EMPHATIC Experiment

Track reconstruction is essential for extracting physics observables from detector data in high-energy and nuclear physics experiments. In this work, we investigate the use of graph neural networks (GNNs) to reconstruct particle momentum in the EMPHATIC experiment using simulated data. The model takes raw hit information from the silicon strip detectors (SSDs) as input and is trained to predict momentum components and the scattering angle of the particle. We describe the GNN architecture, training procedure, and performance metrics, and present results showing improved resolution in momentum reconstruction. These results demonstrate the potential of GNN-based approaches in track reconstruction tasks within complex detector environments like EMPHATIC.

Bhattarai, Aayush [Notre Dame U.]↗
DOE OSTI2025

Improving ICARUS Track Reconstruction Algorithms

The ICARUS experiment is part of the Short-Baseline Neutrino (SBN) program at Fermilab. The main goal of the experiment is to investigate the possibility of sterile neutrinos in the O(1 eV) mass region and provide clarification of the anomaly detected from the Liquid Scintillator Neutrino Detector (LSND) and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber (LAr-TPC), that can provide excellent 3D imaging and calorimetric reconstruction of any ionizing particles. This detection technique allows a detailed study of neutrino interactions, spanning a wide energy spectrum (from a few keV to several hundreds of GeV). The detector consists of two identical adjacent modules, filled with a total of 760 tons of ultra-pure liquid argon. Each module houses two LAr-TPCs separated by a common cathode with a maximum drift distance of 1.5 m, equivalent to about 1 ms drift time for the nominal $500$ V/m electric drift field. The anode is made of three parallel wire planes positioned 3 mm apart, where the stainless-steel wires are oriented on each plane at a different angle with respect to the horizontal direction ($+60^\degree$,$-60^\degree$,$0^\degree$). The first two planes (Induction 1 and Induction 2) provide a non-destructive charge measurement, whereas the ionization charge is fully collected by the last collection plane. In total, 53248 wires with a 3 mm pitch and length up to 9 m are installed in the detector. In the first stage of the reconstruction, segments of waveforms corresponding to physical signals (hits) are searched for in the deconvolved wire waveform with a threshold-based hit-finding algorithm. Each hit is then fitted with a Gaussian, whose area is proportional to the number of drift electrons generating the signal. In the second stage of the reconstruction, hits are passed as input to Pandora, a framework software composed of different pattern recognition algorithms, that performs a 3D reconstruction of the full image recorded in the collected event, including the identification of interaction vertices and tracks and showers inside the TPC. These are organized into a hierarchical structure (called slice) of particles generated starting from a primary interaction vertex. In some cases, related to the inefficiencies in the hit detection or excessive deflection of the particle trajectory, Pandora breaks the particle's track into two or more smaller pieces and considers each piece as an independent track. We studied this phenomenon focusing on primary muons from ν_μ CC interactions contained in a single module with a track at least 20 cm long, to exclude delta rays. The study determined that about $7-8\%$ of the muon tracks are broken. Approximately $80\%$ of the times, Pandora assigns all segments of the track to the same slice (intra-slice track split), while in the remaining $20\%$ of the cases, one of the segments is associated with another slice (extra-slice track split). To mitigate this phenomenon, we designed an algorithm that detects and stitches the tracks broken by Pandora for the intra-slice split. In Monte Carlo simulations, the algorithm showed an efficiency exceeding $80\%$ and a purity exceeding $93\%$.

Ricci, Alessandro Maria [Pisa U.; INFN, Pisa] (ORC↗
DOE OSTI2026

A Joint Search for Muon Neutrino Disappearance with the Short-Baseline Neutrino Program Using the SPINE Deep Learning-Based Reconstruction Package

We present the status of a joint search for muon neutrino disappearance in the Booster Neutrino Beam at Fermilab using the Short-Baseline Neutrino (SBN) Program's two-detector configuration, SBND and ICARUS. Charged-current interactions consistent with muon neutrinos and containing only a muon and at least one proton in the final state are reconstructed and selected using the SPINE deep learning-based particle reconstruction package. To exploit proton multiplicity information and enhance sensitivity to modeling effects, the selected sample is partitioned into exclusive channels with exactly one reconstructed proton and with more than one reconstructed proton. Comprehensive systematic uncertainties from the neutrino flux, interaction, and detector response are incorporated into the analysis, and the coverage of these systematic uncertainty models is validated using data from both detectors, including checks of near-far consistency in key kinematic and topology-sensitive observables. This analysis is intended for inclusion in SBN's first oscillation result.

Mueller, Justin [Fermilab]↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

ELECTRON SHOWER RECONSTRUCTION IN THE ICARUS EXPERIMENT

This dissertation presents a study in the field of neutrino physics. Neutrinos are fundamental particles that are electrically neutral and have extremely small mass, allowing them to traverse matter with very little interaction. Because of this property, neutrinos are exceptionally difficult to detect. Nevertheless, understanding their behavior is essential for addressing fundamental questions about the origin of the Universe and the properties of matter. This work focuses on the reconstruction of electron showers produced by interactions of electron neutrinos (𝜈𝑒) in the ICARUS experiment, located at the Fermi National Accelerator Laboratory in the United States. ICARUS employs a liquid argon time projection chamber detector, which is capable of recording with high precision the tracks left by particles produced in neutrino interactions. The main objective of this research is to improve the reconstruction algorithms and techniques used to identify and characterize these electron showers, enhancing metrics such as completeness, defined as the fraction of correctly reconstructed signals, and purity, which quantifies how much of the reconstructed signal truly belongs to the candidate event. These improvements are crucial for reducing false positives and increasing the accuracy of electron photon discrimination. Consequently, this work directly contributes to improved neutrino oscillation analyses and to a deeper understanding of neutrino properties.

Salmoria, Gabrieli [Parana Tech. Fed. U., Toledo]↗
DOE OSTI2026

Reaching For New Physics With MeV-scale Reconstruction In The MicroBooNE LArTPC Neutrino Detector

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing MeV-Scale energy depositions, or blips, in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV scale using reconstructed blips in MicroBooNE LArTPC data at Fermilab. A concentration of low-energy ($<$3 MeV) blips is observed around fiberglass mechanical support struts along the TPC edges, with spectral features consistent with the Compton edge of the 2.614 MeV $^{208}$Tl decay $\gamma$ ray. With these features we perform the electron energy scale calibration to few-percent precision and yield the specific activity of $^{208}$Tl in the struts, $(11.7 \pm 0.2 \text{(stat)} \pm 2.8 \text{(syst)})$ Bq/kg. Using cosmogenic blips above 3 MeV, we demonstrate the ability of large LArTPCs to discriminate low-energy proton and electron depositions. An enriched low-energy proton sample selected with this technique is smaller in data than in dedicated CORSIKA simulations, pointing to possible mismodeling in CORSIKA incident cosmic fluxes or Geant4 particle transport. These methods are applied to MicroBooNE's inclusive single-photon search, which reported a 2.2$\sigma$ excess below 600 MeV in shower energy for events with no reconstructed protons. By identifying and classifying blips near single-photon events selected by the WireCell reconstruction framework, a more comprehensive labeling of nearby hadronic activity is established: blips upstream of the shower axis indicate previously unidentified final-state protons, while elevated blip counts at wide angles signal final-state neutrons. Taken together with MiniBooNE's long-standing low-energy excess (LEE) and MicroBooNE electron-like and sterile neutrino searches disfavored as possible explanations of the MiniBooNE anomaly, this analysis motivates an expanded exploration of the single-photon channel in Fermilab's short-baseline LArTPC program. This thesis documents the current status of this enhanced analysis, which will form a key part of MicroBooNE's final low-energy-excess results.

Andrade Aldana, Diego Armando [IIT, Chicago (main)↗
DOE OSTI2024

Using pollen in turbidites for vegetation reconstructions

Turbidites, deposited by sub-aqueous gravity flows, are common in sedimentary archives worldwide and present a unique challenge and opportunity when reconstructing past vegetation through pollen analysis. When sampling pollen from a sediment core for palaeovegetation records, it is common practice to target background sediments (i.e. pelagic sediment) and avoid sampling turbidites, as they are presumed to portray a misleading picture of past vegetation. This assumption stems from our limited understanding of pollen abundance and distribution through turbidites, meaning that palynologists overlook deposits that could potentially be used to reconstruct past vegetation and climate. We present pollen assemblage and sedimentological data from four recent (<150 years) deep marine turbidite deposits from the Hikurangi Subduction Margin, Aotearoa-New Zealand, with the aim of understanding the abundance and distribution of pollen in fine-grained turbidites. We find that pollen is diluted in the bases of turbidites, but despite this dilution, the proportions of different pollen taxa remain consistent through each turbidite. These results confirm that pollen can be sampled from turbidites for palaeovegetation reconstructions and that sampling the fine-grained upper parts of turbidites will provide the best pollen recovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Lewis Acid Site Engineering in Chromite Spinels Orchestrated Surface Reconstruction and Surpasses RuO 2 in Oxygen Evolution

Atomic-scale engineering of chromite spinels featuring redox-active tetrahedral A-sites and strong Cr–O covalency offers a promising route to superior platinum-group-metal-free oxygen evolution reaction (OER) catalysts. However, comprehensive studies addressing how cation substitution influences surface chemistry and governs OER activity and durability in chromite spinels remain limited. Here, in this work, a systematic investigation of the multicationic chromite series Ni x Fe y Cr 3−x−y O 4 is presented, identifying composition-dependent Lewis acidity as a descriptor of superior OER performance. It is further demonstrated that tuning surface acidity directly controls dynamic reconstruction processes and lattice-oxygen participation during spinel-based electrocatalysis. Following activation, the optimized Ni 0.8 Fe 0.3 Cr 1.9 O 4 catalyst delivers a current density of 10 mA cm −2 at an overpotential of 235 mV, surpassing RuO 2 , with excellent long-term stability. Integrating microscopic and spectroscopic analysis with operando impedance spectroscopy, it shows that activation generates an oxyhydroxide overlayer and reveals a previously unrecognized link between surface Lewis acidity and the growth kinetics and activity of these shells. Density functional theory calculations indicate that Fe incorporation at octahedral sites raises the O 2p-band center and lowers oxygen-vacancy formation energy, promoting lattice-oxygen activation and triggering reconstruction, yielding enhanced OER. This work integrates cation-driven surface-acidity modulation, acidity-governed reconstruction, and OER activity enhancement into a unified predictive framework for designing earth-abundant spinel-based catalysts.

operando impedance spectroscopy↗
DOE OSTI2024

Capturing thin structures in VOF simulations with two-plane reconstruction

A novel interface reconstruction strategy for volume of fluid (VOF) methods is introduced that represents the liquid-gas interface as two planes that co-exist within a single computational cell. In comparison to the piecewise linear interface calculation (PLIC), this new algorithm greatly improves the accuracy of the reconstruction, in particular when dealing with thin structures such as films. The placement of the two planes requires the solution of a non-linear optimization problem in six dimensions, which has the potential to be overly expensive. Further, an efficient solution to this optimization problem is presented here that exploits two key ideas: an algorithm for extracting multiple plane orientations from transported surface data, and an efficient and mass-conserving distance-finding algorithm that accounts for two planes with arbitrary orientation. Additionally, a simple and robust strategy is presented to accurately represent the surface tension forces produced at the interface of subgrid-thickness films. The performance of this new VOF reconstruction is demonstrated on several test cases that illustrate the capability to handle arbitrarily thin films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Iterative Reconstruction for Multimodal Neutron Tomography

Here, we describe a unified framework for model-based iterative 3-D reconstruction of multimodal neutron transmission, hydrogen-scatter, and induced-fission images from low resolution data recorded using 14.1-MeV neutrons and the associated-particle imaging (API) technique. The framework, which was developed to facilitate use in challenging field-deployment scenarios, is centered around physics-based system models and a total variation (TV) constrained implementation of the simultaneous iterative reconstruction technique (SIRT). Modified to solve a statistically weighted least squares (WLS) problem, the SIRT algorithm is accelerated using ordered subsets and Nesterov’s momentum for which we derive a near-optimal value of the governing Lipschitz constant. The approach enables the reconstruction of images that are high resolution compared to the acquired data and is robust to both limited statistics and a limited number of projection angles. Moreover, the framework is fast enough to be practical. Example images are provided that demonstrate both the ability to perform fast-neutron imaging of high-atomic-number materials with low radiation dose and the benefit of multimodal neutron imaging to identify key materials.

Hydrogen scatter↗