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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Variable Stars in the Giant Satellite Galaxy Antlia 2

We report 350 pulsating variable stars found in four DECam fields (~12 deg 2 ) covering the Antlia 2 satellite galaxy. The sample of variables includes 318 RR Lyrae stars and eight anomalous Cepheids in the galaxy. Reclassification of several objects designated previously to be RR Lyrae as anomalous Cepheids get rid of the satellite's stars intervening along the line of sight. This in turn removes the need for prolific tidal disruption of the dwarf, in agreement with the recently updated proper motion and pericenter measurements based on Gaia EDR3. There are also several bright foreground RR Lyrae stars in the field, and two distant background variables located ~45 kpc behind Antlia 2. We found RR Lyrae stars over the full search area, suggesting that the galaxy is very large and likely extends beyond our observed area. The mean period of the RRab in Antlia 2 is 0.599 days, while the RRc have a mean period of 0.368 days, indicating the galaxy is an Oosterhoff-intermediate system. The distance to Antlia 2 based on the RR Lyrae stars is 124.1 kpc (μ 0 = 20.47) with a dispersion of 5.4 kpc. We measured a clear distance gradient along the semimajor axis of the galaxy, with the southeast side of Antlia 2 being ~13 kpc farther away from the northwest side. This elongation along the line of sight is likely due to the ongoing tidal disruption of Ant 2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Bilinear dynamic mode decomposition for quantum control

Abstract Data-driven methods for establishing quantum optimal control (QOC) using time-dependent control pulses tailored to specific quantum dynamical systems and desired control objectives are critical for many emerging quantum technologies. We develop a data-driven regression procedure, bilinear dynamic mode decomposition (biDMD), that leverages time-series measurements to establish quantum system identification for QOC. The biDMD optimization framework is a physics-informed regression that makes use of the known underlying Hamiltonian structure. Further, the biDMD can be modified to model both fast and slow sampling of control signals, the latter by way of stroboscopic sampling strategies. The biDMD method provides a flexible, interpretable, and adaptive regression framework for real-time, online implementation in quantum systems. Further, the method has strong theoretical connections to Koopman theory, which approximates nonlinear dynamics with linear operators. In comparison with many machine learning paradigms minimal data is needed to construct a biDMD model, and the model is easily updated as new data is collected. We demonstrate the efficacy and performance of the approach on a number of representative quantum systems, showing that it also matches experimental results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Measurement of C P asymmetries in Λ b 0 → p h − decays

A search for C P violation in Λ b 0 → p K − and Λ b 0 → p π − decays is presented using the full Run 1 and Run 2 data samples of p p collisions collected with the LHCb detector, corresponding to an integrated luminosity of 9 fb − 1 at center-of-mass energies of 7, 8, and 13 TeV. For the Run 2 data sample, the C P -violating asymmetries are measured to be A C P p K − = ( − 1.4 ± 0.7 ± 0.4 ) % and A C P p π − = ( 0.4 ± 0.9 ± 0.4 ) % , where the first uncertainty is statistical and the second is systematic. Following significant improvements in the evaluation of systematic uncertainties compared to the previous LHCb measurement, the Run 1 dataset is reanalyzed to update the corresponding results. When combining the Run 2 and updated Run 1 measurements, the final results are found to be A C P p K − = ( − 1.1 ± 0.7 ± 0.4 ) % and A C P p π − = ( 0.2 ± 0.8 ± 0.4 ) % , constituting the most precise measurements of these asymmetries to date. © 2025 CERN, for the LHCb Collaboration 2025 CERN

Aaij, R. (ORCID:0000000305331952)↗
DOE OSTI2024

Thermal aging effects on crosslinked polyethylene cable insulation with decabromodiphenyl ether flame retardant alternative

Decabromodiphenyl ether (decaBDE) has been extensively used as a flame retardant in several applications, including nuclear electrical cable insulation. However, decaBDE has been identified as a persistent, bioaccumulative and toxic (PBT) substance, leading to regulatory scrutiny. The Environmental Protection Agency (EPA) published a regulation on January 6, 2021, aimed at phasing out the manufacturing, processing, and distribution of decaBDE. This rule set a compliance deadline of March 8, 2021, for the manufacture and processing of decaBDE, and an extended deadline of January 6, 2023, for specific applications including wire and cable insulation in nuclear power generation facilities. In response to such regulations, RSCC, a major supplier of safety-related electrical cables and associated products to the U.S. nuclear industry updated the formula of their crosslinked polyethylene (XLPE) insulation to replace the historically used decaBDE flame retardant with an acceptable alternative. This change from the previous decaBDE-containing XLPE prompted interest in comparative performance of the two material formulations, especially with respect to characteristics relevant to safety-related function such as thermal and radiation resistance. RSCC graciously provided samples of wire insulated with the decaBDE-containing XLPE formulation and corresponding wire insulated with XLPE of the new formulation, containing a decaBDE alternative. In this work we compare characteristics of the two formulations and a previously produced commercial version of the RSCC decaBDE-containing XLPE insulation subjected to thermal aging at 150 °C and 165 °C. The comparison was focused on mechanical durability, thermal stability in the oxidative environment, and chemical structures. Briefly, • Tensile elongation at break (EAB) results showed loss of mechanical elasticity with longer aging time, as expected. Aging time dependence of EAB did not differ between the decaBDE-containing and decaBDE-alternative samples. • Subtle differences between the two materials can be detected from Fourier-transform infrared spectroscopy (FTIR) absorbance spectra in the range below 1700 cm -1 , are assumed to be related to decomposition of flame retardant additives during thermal aging. • The oxidation induction time (OIT) data seemed to show that the unaged decaBDE-containing XLPE material is more thermally stable than the unaged decaBDE-alternative material, but the discrepancy in OIT decreased with aging time and the OIT values of the two materials became similar starting with the 4 th day of aging at 165 °C. This thermal aging investigation confirmed that the mechanical durability, a key property monitored for cable qualification, was not significantly affected by the modification of the formulation with a decaBDE alternative flame-retardant system in the investigated thermal aging conditions. Further studies on the same sets of materials exposed to thermal and gamma radiation aging would further inform comparison of the materials safety-related function.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Author Correction: Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle: High-Resolution Sampling Every 4 Hours Over 22 Days

In the version of this article initially published, two errors in authorship were made. First, Tatiana Rynearson of the School of Oceanography, University of Rhode Island was mistakenly omitted from the final author list. Second, Kurt LaButti of the Joint Genome Institute was mistakenly omitted from the final author list and replaces Alicia Clum due to a staffing change at the Joint Genome Institute. These authorship omissions were not identified until after the work had been published. We are updating the authorship to appropriately recognize the contributions of these authors. The error has been corrected in the PDF and HTML versions of the article.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

WHONDRS Surface Water Dissolved Organic Carbon and FTICR-MS across Stream Orders in Four United States Watersheds in 2019 and 2020 (v3)

This dataset supports the broader Watershed Rules of Life (WROL) study examining spatial and temporal biogeochemical and microbial relationships across the Connecticut River, Deschutes River, Gunnison River, and Willamette River watersheds. The dataset provides geochemistry and organic matter characterization data generated from surface water collected from August 2019 to September 2020 in Colorado, Connecticut, Oregon, and Vermont. Related data were collected and will be published separately in collaboration with P. A. Raymond and B. C. Crump as part of WROL. This dataset is comprised of one folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (6) surface water sampling protocol; (7) methods codes; (8) international generic sample number (IGSN) mapping file; and (9) a folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). The FTICR folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. This data package was originally published in October 2022. It was updated in July 2023 (v2) and again in November 2023 (v3). See the change history section in the readme for more details.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Equation of State Measurements on Iron Near the Melting Curve at Planetary Core Conditions by Shock and Ramp Compressions

The outer core of the Earth is composed primarily of liquid iron, and the inner core boundary is governed by the intersection of the melt line and the geotherm. While there are many studies on the thermodynamic equation of state for solid iron, the equation of state of liquid iron is relatively unexplored. In this work, we use dynamic compression to diagnose the high-pressure liquid equation of state of iron by utilizing the shock-ramp capability at Sandia National Laboratories’ Z-Machine. This technique enables measurements of material states off the Hugoniot by initially shocking samples and subsequently driving a further, shockless compression. Planetary studies benefit greatly from isentropic, off-Hugoniot experiments since they can cover pressure-temperature (P-T) conditions that are close to adiabatic profiles found in planetary interiors. We used this method to drive iron to P-T conditions similar to those of the Earth’s outer-inner core boundary, along an elevated-temperature isentrope in the liquid from 275 GPa to 400 GPa. We derive the equation of state using a hybrid backward integration – forward Lagrangian technique on particle velocity traces to determine the pressure-density history of the sample. Our results are in excellent agreement with SESAME 92141, a previously published equation of state table. With our data and previous experimental data on liquid iron we provide new information on the iron melting line and derive new parameters for a Vinet-based equation of state. The table and our parameterized equation of state are applied to provide an updated means of modeling the pressure, mass, and density of liquid iron cores in exoplanetary interiors.

58 GEOSCIENCES↗
DOE OSTI2023

Uncertainty Error Modeling for Non-Linear State Estimation With Unsynchronized SCADA and µPMU Measurements

Distribution systems of the future smart grid require enhancements to the reliability of distribution system state estimation (DSSE) in the face of low measurement redundancy, unsynchronized measurements, and dynamic load profiles. Micro phasor measurement units (µPMUs) facilitate co-synchronized measurements with high granularity, albeit at an often prohibitively expensive installation cost. Supervisory control and data acquisition (SCADA) measurements can supplement µPMU data, although they are received at a slower sampling rate. Further complicating matters is the uncertainty associated with load dynamics and unsynchronized measurements–not only are the SCADA and µPMU measurements not synchronized with each other, but the SCADA measurements themselves are received at different time intervals with respect to one another. This paper proposes a non-linear state estimation framework which models dynamic load uncertainty error by updating the variances of the unsynchronized measurements, leading to a time-varying system of weights in the weighted least squares state estimator. Case studies are performed on the 33-Bus Distribution System in MATPOWER, using Ornstein–Uhlenbeck stochastic processes to simulate dynamic load conditions.

Cooper, Austin↗
DOE OSTI2024

Newest 3-Flavor Neutrino Oscillation Results from NOvA

NOvA is a long-baseline accelerator neutrino experiment based at the Fermi National Accelerator Laboratory observing the appearance of electron (anti)neutrinos and disappearance of muon (anti) neutrinos. The experiment consists of two functionally identical liquid scintillator detectors; a 14 kton Far Detector is situated 810 km from the beam source, while a smaller Near Detector is only 1 km from the source and samples initial beam composition consisting of primarily muon (anti)neutrinos. Through these observations NOvA is probing outstanding questions in neutrino physics including the neutrino mass ordering and leptonic CP violation along with the θ23 neutrino mixing angle and the larger neutrino mass splitting Δm232. This talk will present the most recent neutrino oscillation results, which include a larger dataset, new analysis techniques, and updated systematic uncertainties.

Sutton, Andrew↗
DOE OSTI2023

Update on the Current R&D Activity in the U.S.

This presentation is to fulfil DOE commitment to GIF VHTR Materials PMB. Notched creep rupture testing: Weldment V-notch specimen test finished. 800C 60MPa, 15,716 hour rupture life. Two double V-notch specimen tests have finished. Crack growth tests: System modification was required for creep and creep-fatigue testing, Epsilon 3548COD Extensometer has been ordered, to allow load-line displacement measurements, to be paired with direct current potential drop already used to measure crack length, Sample redesigned in accordance to ASTM E2760 and E1457 to allow for both creep and creep-fatigue crack growth rate testing. Alloy 800H Weldments (1/2) and Alloy 800H Weldments (2/2), Variable Fatigue Testing, and Multi=axial Stress Relaxation. Continue work to finish 800H model, examine ?universal? constitutive model form.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Using Bayesian Methodology to Estimate Liquefied Natural Gas Leak Frequencies

This analysis provides estimates on the leak frequencies of nine components found in liquefied natural gas (LNG) facilities. Data was taken from a variety of sources, with 25 different data sets included in the analysis. A hierarchical Bayesian model was used that assumes that the log leak frequency follows a normal distribution and the logarithm of the mean of this normal distribution is a linear function of the logarithm of the fractional leak area. This type of model uses uninformed prior distributions that are updated with applicable data. Separate models are fit for each component listed. Five order-of-magnitude fractional leak areas are considered, based on the flow area of the component. Three types of supporting analyses were performed: sensitivity of the model to the data set used, sensitivity of the leak frequency estimates to differences in the model structure or prior distributions, and sufficiency of sample sized used for convergence. Recommended leak frequency distributions for all component types and leak sizes are given. These leak frequency predictions can be used for quantitative risk assessments in the future.

03 NATURAL GAS↗
DOE OSTI2024

Machine learning framework for predicting uranium enrichments from M400 CZT gamma spectra

A machine learning framework was developed for predicting uranium enrichments from M400 CZT gamma spectra. This framework leverages the availability of a large amount of measured M400 gamma spectra and uses a recently updated version of Gamma Detector Response and Analysis Software (GADRAS) for gamma spectrum analysis and generation. It also leverages the existing machine learning modules in Python for gamma spectrum data processing, curation, model training, benchmarking, and optimization of the deep machine learning models. The framework is used to develop a deep learning model to analyze gamma spectra from a set of U 3 O 8 samples with enrichments ranging from 0.31 to 93.17% and UF 6 cylinders with enrichments ranging from 0.2 to 4.95%, and the model performance is tested using a set of measured spectra and the respective declared enrichment values. Results show that the model can correctly classify 99.35% of the U 3 O 8 sample enrichments, and can predict the samples’ enrichments within an average absolute error of 0.099% (in percentage points of enrichment). For the UF 6 cylinders, the average absolute error was approximately 0.03%, with an accuracy of 98% in classifying discrete enrichment values of UF 6 samples. Finally, the results also show that the model has performed significantly better in terms of predicting enrichments in UF 6 cylinders based on measured gamma spectra than the GEM code, with a standard deviation (of the relative errors) of 2.23% (compared with the 11.51% value for the GEM code) based on results from a set of test data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Modeling of ATR fuel in DOE Standard Canisters with Helium Backfilled Condition

Road-ready and final disposition packaging configurations for the advanced test reactor (ATR) fuel dictates storage within helium backfilled sealed DOE standard canisters. These sealed canisters are intended for extened (>50 year) dry storage). The typical packaging configuration for the 15-foot DOE canisters places 10 ATR elements within a Type 1a basket, and three baskets are loaded within each DOE canister. During in-reactor operations and cooling pond storage conditions, oxyhydroxide layers form on the surface of the aluminum clad fuel. These layers produce hydrogen gas over time due to the fuel’s radiation field. As part of the packing procedure, the ATR fuel should be dried to remove any residual physio-/chemi- sorbed water from the surface. As testing to the effectiveness of the drying procedure is still underway, this modeling will include results at fully saturated and fully dried conditions. In previous modeling efforts, the G-value for the production of hydrogen from the oxyhydroxide layers was assumed to be in argon environments as measured by Task 2 - Oxyhydroxide Layer Radiolytic Gas Generation Resolution. Previous experimental testing showed differences in the hydrogen generated based on the gaseous environment. In the associated experimental work, Task 2 - Oxyhydroxide Layer Radiolytic Gas Generation Resolution, additional tests were completed in a helium environment, and updated G-values for the radiolytic production of hydrogen from the oxyhydroxide layers were provided. These values are 28% and 58% higher than values for argon. In addition, a change to the modeling of the oxyhydroxide radiolysis has been made from previous reports. This change assumes the dependency of the dose rate on the overall reaction rate is applied to the total weight of the sample, rather than to just the weight of the oxyhydroxide layer. This decreases the dependency of the radiolytic reaction on the thickness of the oxyhydroxide layer. For a nominal scenario of stored ATR fuel, assuming the chemi-/physio- sorbed water have been fully removed, the internal canister pressure increases to 1.61 atm over a 50 year period, with a hydrogen mole percentage of 21%. As in previous modeling, any oxygen present is in negligible amounts (<1 ppt). If a small amount residual air is present, nitric acid can form up to 1300 ppm. For a scenario with high fuel decay heat, the model shows internal pressure increasing to 2.1 atm, with 39.3 mole percentage of hydrogen. In a scenario where significant chemi-/physio- sorbed water is present within the corrosion layer, the nominal scenario shows a pressure increase to 2.54 atm, with 21.1 mole percent hydrogen. The high decay heat case shows a pressure increase to 3.18 atm with 39.9 mole percent hydrogen.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Survey of Surveys: I. The largest compilation of radial velocities for the Galaxy

In the present-day panorama of large spectroscopic surveys, the amount, diversity, and complexity of the available data continuously increase. The overarching goal of studying the formation and evolution of our Galaxy is hampered by the heterogeneity of instruments, selection functions, analysis methods, and measured quantities. Here, we present a comprehensive catalogue, the Survey of Surveys (SoS), built by homogeneously merging the radial velocity (RV) determinations of the largest ground-based spectroscopic surveys to date, such as APOGEE, GALAH, Gaia-ESO, RAVE, and LAMOST, using Gaia as a reference. This pilot study serves to prove the concept and to test the methodology that we plan to apply in the future to the stellar parameters and abundance ratios as well. We have devised a multi-staged procedure that includes: (i) the cross match between Gaia and the spectroscopic surveys using the official Gaia cross-match algorithm, (ii) the normalisation of uncertainties using repeated measurements or the three-cornered hat method, (iii) the cross calibration of the RVs as a function of the main parameters on which depend (magnitude, effective temperature, surface gravity, metallicity, and signal-to-noise ratio) to remove trends and zero point offsets, and (iv) the comparison with external high-resolution samples, such as the Gaia RV standards and the Geneva-Copenhagen survey, to validate the homogenisation procedure and to calibrate the RV zero-point of the SoS catalogue. We provide the largest homogenised RV catalogue to date, containing almost 11 million stars, of which about half come exclusively from Gaia and half in combination with the ground-based surveys. We estimate the accuracy of the RV zero-point to be about 0.16–0.31 km s –1 and the RV precision to be in the range 0.05–1.50 km s –1 depending on the type of star and on its survey provenance. We validate the SoS RVs with open clusters from a high resolution homogeneous samples and provide the systemic velocity of 55 individual open clusters. Additionally, we provide median RVs for 532 clusters recently discovered by Gaia data. The SoS is publicly available and ready to be applied to various research projects, such as the study of star clusters, Galactic archaeology, stellar streams, or the characterisation of planet-hosting stars, to name a few. We also plan to include survey updates and more data sources in future versions of the SoS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Projected Multi-Agent Consensus Equilibrium (PMACE) With Application to Ptychography

Multi-Agent Consensus Equilibrium (MACE) formulates an inverse imaging problem as a balance among multiple update agents such as data-fitting terms and denoisers. However, each such agent operates on a separate copy of the full image, leading to redundant memory use and slow convergence when each agent affects only a small subset of the full image. In this article, we extend MACE to Projected Multi-Agent Consensus Equilibrium (PMACE), in which each agent updates only a projected component of the full image, thus greatly reducing memory use for some applications. We describe PMACE in terms of an equilibrium problem and an equivalent fixed-point problem and show that in most cases the PMACE equilibrium is not the solution of an optimization problem. To demonstrate the value of PMACE, we apply it to the problem of ptychography, in which a sample is reconstructed from the diffraction patterns resulting from coherent X-ray illumination at multiple overlapping spots. In our PMACE formulation, each spot corresponds to a separate data-fitting agent, with the final solution found as an equilibrium among all the agents. In conclusion, our results demonstrate that the PMACE reconstruction algorithm generates more accurate reconstructions at a lower computational cost than existing ptychography algorithms when the spots are sparsely sampled.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

GPU acceleration of Swendsen–Wang dynamics

When simulating a lattice system near its critical temperature, local algorithms for modeling the system’s evolution can introduce very large autocorrelation times into sampled data. Here, this critical slowing down places restrictions on the analysis that can be completed in a timely manner of the behavior of systems around the critical point. Because it is often desirable to study such systems around this point, a new algorithm must be introduced. Therefore, we turn to cluster algorithms, such as the Swendsen–Wang algorithm and the Wolff clustering algorithm. They incorporate global updates which generate new lattice configurations with little correlation to previous states, even near the critical point. We look to accelerate the rate at which these algorithm are capable of running by implementing and benchmarking a parallel implementation of each algorithm designed to run on GPUs under NVIDIA’s CUDA framework. A 17 and 90 fold increase in the computational rate was, respectively, experienced when measured against the equivalent algorithm implemented in serial code.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗