Search NASA⌕ Search

SEARCH · Search NASA

Results for “reconstruction algorithms”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cost-efficient finite-volume high-order schemes for compressible magnetohydrodynamics

We present an efficient dimension-by-dimension finite-volume method which solves the adiabatic magnetohydrodynamics equations at high discretization order, using the constrained-transport approach on Cartesian grids. Results are presented up to tenth order of accuracy. The algorithmic architecture of this method is very close to that of commonly employed second-order schemes: it requires only one reconstructed value per face for each computational cell, independently of the scheme's order. This property is highly beneficial for the numerical efficiency. It results from reusing the required values already available in neighboring grid cells, in contrast to standard algorithms that require a number of reconstructions and evaluations which increases with the scheme's order of accuracy. At a given resolution, these high-order schemes present significantly less numerical dissipation than commonly employed lower-order approaches. Thus, results of comparable accuracy are achievable at a substantially coarser resolution, yielding overall performance gains. We also present a way to include physical dissipative terms: viscosity, magnetic diffusivity and cooling functions, respecting the finite-volume and constrained-transport frameworks. Benefits of this method are shown through applications in turbulent flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, in this work, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Medium-induced modification of groomed and ungroomed jet mass and angularities in Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The ALICE Collaboration presents a new suite of jet substructure measurements in Pb–Pb and pp collisions at a center-of-mass energy per nucleon pair $\sqrt{s_{NN}}$ = 5.02 TeV. These measurements provide access to the internal structure of jets via the momentum and angle of their constituents, probing how the quark–gluon plasma modifies jets, an effect known as jet quenching. Jet grooming additionally removes soft wide-angle radiation to enhance perturbative accuracy and reduce experimental uncertainties. We report the groomed and ungroomed jet mass m jet and jet angularities λ$^{κ}_{α}$ using κ = 1 and α > 0. Charged-particle jets are reconstructed at midrapidity using the anti-k T algorithm with resolution parameter R = 0.2. A narrowing of the jet mass and angularity distributions in Pb–Pb collisions with respect to pp is observed and is enhanced for groomed results, confirming modification of the jet core. By using consistent jet definitions and kinematic cuts between the mass and angularities for the first time, previous inconsistencies in the interpretation of quenching measurements are resolved, rectifying a hurdle for understanding how jet quenching arises from first principles and highlighting the importance of a well-controlled baseline. These results are compared with a variety of theoretical models of jet quenching, providing constraints on jet energy-loss mechanisms in the quark–gluon plasma.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Single-shot measurements of shaped XUV FEL pulses with frequency-resolved optical gating

The ability to control the amplitude and phase of extreme ultraviolet (XUV) and X-ray free-electron laser (FEL) pulses can allow for the extension of optical techniques, such as multidimensional spectroscopy or coherent control, to higher photon energies, for probing and controlling core electronic transitions. However, this requires the ability to make single-shot, and complete, electric field measurements of potentially complex FEL pulses, in order to develop, and verify, pulse shaping strategies. Here, we present direct, single-shot measurements of XUV pulses generated under special operating configurations for producing specific pulse shapes from a laser-seeded XUV FEL. To do this, we built upon our past work using transient grating (TG) cross correlation frequency-resolved optical gating (FROG), where an optical reference pulse is diffracted from an XUV TG produced by a pair of interfering FEL pulses. The resulting nonlinear signal versus frequency and delay, i.e., the FROG trace, contains the electric field of the FEL pulse. The FEL pulse electric field is reconstructed from the FROG trace using a phase retrieval algorithm. Here we confirmed three different pulse shaping strategies for generating chirped, double and multiple FEL pulses by tuning the seed laser and FEL parameters and measuring the resulting shaped FEL pulses with TG XFROG. This work paves the way for generating on-demand pulse shapes with a seeded FEL by improving the characterization of FEL pulses, for transform-limited to more complex shapes.

Elverson, Francesca↗
NASA NTRS1992

Fast Transform Decoding Of Nonsystematic Reed-Solomon Codes

Fast, efficient Fermat number transform used to compute F'(x) analogous to computation of syndrome in conventional decoding scheme. Eliminates polynomial multiplications and reduces number of multiplications in reconstruction of F'(x) to n log (n). Euclidean algorithm used to evaluate F(x) directly, without going through intermediate steps of solving error-locator and error-evaluator polynomials. Algorithm suitable for implementation in very-large-scale integrated circuits.

Truong, Trieu-Kie↗
NASA NTRS2000

Wavelet-Based Interpolation and Representation of Non-Uniformly Sampled Spacecraft Mission Data

A well-documented problem in the analysis of data collected by spacecraft instruments is the need for an accurate, efficient representation of the data set. The data may suffer from several problems, including additive noise, data dropouts, an irregularly-spaced sampling grid, and time-delayed sampling. These data irregularities render most traditional signal processing techniques unusable, and thus the data must be interpolated onto an even grid before scientific analysis techniques can be applied. In addition, the extremely large volume of data collected by scientific instrumentation presents many challenging problems in the area of compression, visualization, and analysis. Therefore, a representation of the data is needed which provides a structure which is conducive to these applications. Wavelet representations of data have already been shown to possess excellent characteristics for compression, data analysis, and imaging. The main goal of this project is to develop a new adaptive filtering algorithm for image restoration and compression. The algorithm should have low computational complexity and a fast convergence rate. This will make the algorithm suitable for real-time applications. The algorithm should be able to remove additive noise and reconstruct lost data samples from images.

Bose, Tamal↗
NASA NTRS2010

Heat Flux and Wall Temperature Estimates for the NASA Langley HIFiRE Direct Connect Rig

An objective of the Hypersonic International Flight Research Experimentation (HIFiRE) Program Flight 2 is to provide validation data for high enthalpy scramjet prediction tools through a single flight test and accompanying ground tests of the HIFiRE Direct Connect Rig (HDCR) tested in the NASA LaRC Arc Heated Scramjet Test Facility (AHSTF). The HDCR is a full-scale, copper heat sink structure designed to simulate the isolator entrance conditions and isolator, pilot, and combustor section of the HIFiRE flight test experiment flowpath and is fully instrumented to assess combustion performance over a range of operating conditions simulating flight from Mach 5.5 to 8.5 and for various fueling schemes. As part of the instrumentation package, temperature and heat flux sensors were provided along the flowpath surface and also imbedded in the structure. The purpose of this paper is to demonstrate that the surface heat flux and wall temperature of the Zirconia coated copper wall can be obtained with a water-cooled heat flux gage and a sub-surface temperature measurement. An algorithm was developed which used these two measurements to reconstruct the surface conditions along the flowpath. Determinations of the surface conditions of the Zirconia coating were conducted for a variety of conditions.

Cuda, Vincent, Jr.↗
NASA NTRS1998

Correcting Satellite Image Derived Surface Model for Atmospheric Effects

This project was a continuation of the project entitled "Resolution Earth Surface Features from Repeat Moderate Resolution Satellite Imagery". In the previous study, a Bayesian Maximum Posterior Estimate (BMPE) algorithm was used to obtain a composite series of repeat imagery from the Advanced Very High Resolution Radiometer (AVHRR). The spatial resolution of the resulting composite was significantly greater than the 1 km resolution of the individual AVHRR images. The BMPE algorithm utilized a simple, no-atmosphere geometrical model for the short-wave radiation budget at the Earth's surface. A necessary assumption of the algorithm is that all non geometrical parameters remain static over the compositing period. This assumption is of course violated by temporal variations in both the surface albedo and the atmospheric medium. The effect of the albedo variations is expected to be minimal since the variations are on a fairly long time scale compared to the compositing period, however, the atmospheric variability occurs on a relatively short time scale and can be expected to cause significant errors in the surface reconstruction. The current project proposed to incorporate an atmospheric correction into the BMPE algorithm for the purpose of investigating the effects of a variable atmosphere on the surface reconstructions. Once the atmospheric effects were determined, the investigation could be extended to include corrections various cloud effects, including short wave radiation through thin cirrus clouds. The original proposal was written for a three year project, funded one year at a time. The first year of the project focused on developing an understanding of atmospheric corrections and choosing an appropriate correction model. Several models were considered and the list was narrowed to the two best suited. These were the 5S and 6S shortwave radiation models developed at NASA/GODDARD and tested extensively with data from the AVHRR instrument. Although the 6S model was a successor to the 5S and slightly more advanced, the 5S was selected because outputs from the individual components comprising the short-wave radiation budget were more easily separated. The separation was necessary since both the 5S and 6S did not include geometrical corrections for terrain, a fundamental constituent of the BMPE algorithm. The 5S correction code was incorporated into the BMPE algorithm and many sensitivity studies were performed.

Emery, William↗
DOE OSTI2026

Wasserstein normalized autoencoder for anomaly detection

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
NASA NTRS1991

Small convolution kernels for high-fidelity image restoration

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

Reichenbach, Stephen E.↗
DOE OSTI2024

Calibration of a soft secondary vertex tagger using proton-proton collisions at s = 13 TeV with the ATLAS detector

Several processes studied by the ATLAS experiment at the Large Hadron Collider produce low-momentum b-flavored hadrons in the final state. This paper describes the calibration of a dedicated tagging algorithm that identifies b-flavored hadrons outside of hadronic jets by reconstructing the soft secondary vertices originating from their decays. The calibration is based on a proton-proton collision dataset at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 140 fb -1 . Scale factors used to correct the algorithm’s performance in simulated events are extracted for the b-tagging efficiency and the mistag rate of the algorithm using a data sample enriched in $t\overline{t}$ events. Several orthogonal measurement regions are defined, binned as a function of the multiplicities of soft secondary vertices and jets containing a b-flavored hadron in the event. The mistag rate scale factors are estimated separately for events with low and high average numbers of interactions per bunch crossing. The results, which are derived from events with low missing transverse momentum, are successfully validated in a phase space characterized by high missing transverse momentum and therefore are applicable to new physics searches carried out in either phase space regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Toward an event-level analysis of hadron structure using differential programming

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon de- grees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental QCD-defined densities that characterize the micro- scopic structure of hadronic systems. Recent advances in AI and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of complex theoretical calculations and experimental simulations into a unified framework capable of reconstructing these densities directly from event-level information. In this document, we introduce a key algorithm called LOITS, which enables differentiable program- ming within such a framework, facilitating the use of AI/ML techniques to solve the inverse problem of QCF reconstruction at the event level.

Braga, Kevin [College of William and Mary, William↗
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↗
DOE OSTI2025

Selection Algorithm for electron neutrino charged current interactions in SBND

The Short Baseline Neutrino (SBN) program at Fermilab is a joint proposal by three experimental collaborations primarily for the investigation of the cause behind the low-energy electron-like event excess observed by the MiniBoone experiment. This dissertation focuses on the near detector of the project, named the Short Baseline Near Detector (SBND), a Liquid Argon Time Projection Chamber apparatus which will conduct searches for sterile neutrinos in the mass range of 1 ${eV}^2/{c}^4$, as well as provide cross-section measurements for neutrino interactions in argon and perform other beyond the standard model studies.\\ \indent As is the case for all detectors in the program, the SBND will use the Booster Neutrino Beam as its source, which will provide it with both muon and electron neutrinos. Given that the ability to discern between the neutrino flavors will be crucial to the fulfillment of the detector's physics goals, the objective of this work is to provide the collaboration with a tool capable of doing so. As such, we here present the development process for an inclusive selection algorithm for the identification of electron neutrino charged current (CC) events regardless of their interaction channel. This is done through a combination of traditional techniques, such as the implementation of cuts on the reconstructed interaction properties, with the use of the Convolutional Visual Network, a machine learning algorithm capable of classifying particle interactions through the analysis of the topology of their final states. With this approach, we have developed a selection process that is capable of identifying $\nu_e$ CC interactions across a wide range of topologies with 34.4\% efficiency, as well as a purity of 91.2\%, making it especially promising for use in cross section studies.

Freire, Hector Moya [ABC Federal U.] (ORCID:000900↗
DOE OSTI2025

Selection Algorithm Improvement for MicroBooNE

Data selection is an extremely important part of data analysis for any experiment. Finding a physics result is often the result of sifting through a massive amount of data, keeping data that we believe to be signal and throwing out data we do not. This process is called data selection. Creating a selection algorithm is an intensive process that must balance keeping enough data to have statistics and maximizing the signal purity of that data. In this study, we used three different reconstruction tools, Pandora, WireCell, and LANTERN, for the MicroBooNE experiment in conjunction to improve the selection algorithm for analysis. For the case of this study, we look into the charged current N proton 0 pions (CCNp0$\pi$) interaction channel. This is the dominant channel for the Short Baseline Neutrino (SBN) program and is expected to be a large contributor to the Deep Underground Neutrino Experiment (DUNE). We first investigated each of the three tools to find out more about their strengths and weaknesses as reconstructions. We then put together a direct comparison of the three methods to find which method or combination of methods would return the best result for us. While the study is ongoing, we have learned a lot about data selection for the experiment and the differences between the reconstruction tools.

Dillon, Brayden [Michigan State U.]↗
DOE OSTI2025

Data Selection Improvement For MicroBooNE

Data selection is an extremely important part of data analysis for any experiment. Finding a physics result is often the result of sifting through a massive amount of data, keeping data that we believe to be signal, and throwing out data we do not. This process is called data selection. Creating a selection algorithm is an intensive process that must balance keeping enough data to have statistics and maximizing the signal purity of that data. We also need to choose the right reconstruction method, a tool to take raw data from the detector and convert it into physics results. In this study, we used three different reconstruction tools, Pandora, WireCell, and LANTERN, for the MicroBooNE experiment in conjunction to improve the selection algorithm for analysis. For the case of this study, we look into the charged current N proton 0 pions (CCNp0$\pi$) interaction channel. This is the dominant channel for the Short Baseline Neutrino (SBN) program and is expected to be a large contributor to the Deep Underground Neutrino Experiment (DUNE). We first investigated each of the three tools to find out more about their strengths and weaknesses as reconstructions, and compared them to the truth information directly from the MicroBooNE simulation pipeline. We then put together a direct comparison of the three methods to find which method or combination of methods would return the best result for us. While the study is ongoing, we have learned a lot about data selection for the experiment and the differences between the reconstruction tools.

Dillon, Brayden [Fermilab]↗
DOE OSTI2025

Development of thin-gap GEM- μ RWELL hybrid detectors

Micro Pattern Gaseous Detectors (MPGDs) are used for tracking in High Energy Physics and Nuclear Physics because of their large area, excellent spatial resolution capabilities and low cost. However, for high energy charged particles impacting at a large angle with respect to the axis perpendicular to detector plane, the spatial resolution degrades significantly because of the long trail of ionization charges produced in clusters all along the track in the drift region of the detector. The long ionization charge trail results in registering hits from large number of strips in the readout plane which makes it challenging to precisely reconstruct the particle’s position using simple center of gravity algorithm. As a result, the larger the drift gap, the more severe the deterioration of spatial resolution for inclined tracks. For the same reason, the position resolution is also severely degraded in a large magnetic field, where the Lorentz E x B effect causes the ionization charges to follow a curved and longer path in the detector gas volume. In this paper, we report on the development of thin-gap MPGDs as a way to maintain excellent spatial resolution capabilities of MPGD detectors over a wide angular range of incoming particles. In a thin-gap MPGD, the thickness of the gas volume in the drift region is reduced from typically 3 mm to 1 mm or less. We present preliminary test beam results demonstrating the improvement in spatial resolution from 400 m with a standard 3 mm gap μ RWELL prototype to ~140 μ m with a double amplification GEM- μ RWELL thin-gap hybrid detector. Here, we also discuss the impact of a thin-gap drift volume on other aspects of the performance of MPGD technologies such as the efficiency and detector stability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

model-of-perception

Algorithms for perceptually grounded evaluation of scientific visualizations, using neural reconstruction and embedding models to quantify clarity and interpretability.

Bujack, Roxana↗