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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A NEW METHOD TO QUANTIFY AND REDUCE THE NET PROJECTION ERROR IN WHOLE-SOLAR-ACTIVE-REGION PARAMETERS MEASURED FROM VECTOR MAGNETOGRAMS

Projection errors limit the use of vector magnetograms of active regions (ARs) far from the disk center. In this Letter, for ARs observed up to 60° from the disk center, we demonstrate a method for measuring and reducing the projection error in the magnitude of any whole-AR parameter that is derived from a vector magnetogram that has been deprojected to the disk center. The method assumes that the center-to-limb curve of the average of the parameter’s absolute values, measured from the disk passage of a large number of ARs and normalized to each AR’s absolute value of the parameter at central meridian, gives the average fractional projection error at each radial distance from the disk center. To demonstrate the method, we use a large set of large-flux ARs and apply the method to a whole-AR parameter that is among the simplest to measure: whole-AR magnetic flux. We measure 30,845 SDO /Helioseismic and Magnetic Imager vector magnetograms covering the disk passage of 272 large-flux ARs, each having whole-AR flux >10{sup 22} Mx. We obtain the center-to-limb radial-distance run of the average projection error in measured whole-AR flux from a Chebyshev fit to the radial-distance plot of the 30,845 normalized measured values. The average projection error in the measured whole-AR flux of an AR at a given radial distance is removed by multiplying the measured flux by the correction factor given by the fit. The correction is important for both the study of the evolution of ARs and for improving the accuracy of forecasts of an AR’s major flare/coronal mass ejection productivity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.

Subasi, Omer↗
DOE OSTI2024

Lattice QCD estimates of thermal photon production from the QGP

Thermal photons produced in heavy-ion collision experiments are an important observable for understanding quark-gluon plasma (QGP). The thermal photon rate from the QGP at a given temperature can be calculated from the spectral function of the vector current correlator. Extraction of the spectral function from the lattice correlator is known to be an ill-conditioned problem, as there is no unique solution for a spectral function for a given lattice correlator with statistical errors. The vector current correlator, on the other hand, receives a large ultraviolet contribution from the vacuum, which makes the extraction of the thermal photon rate difficult from this channel. We therefore consider the difference between the transverse and longitudinal part of the spectral function, only capturing the thermal contribution to the current correlator, simplifying the reconstruction significantly. The lattice correlator is calculated for light quarks in quenched QCD at T = 470 MeV ( ∼ 1.5 T c ), as well as in 2 + 1 flavor QCD at T = 220 MeV ( ∼ 1.2 T p c ) with m π = 320 MeV . In order to quantify the nonperturbative effects, the lattice correlator is compared with the corresponding NLO + LPM LO estimate of correlator. The reconstruction of the spectral function is performed in several different frameworks, ranging from physics-informed models of the spectral function to more general models in the Backus-Gilbert method and Gaussian process regression. We find that the resulting photon rates agree within errors. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2014

SU-E-J-123: Targeting Accuracy of Image-Guided Radiosurgery for Intracranial Lesions

Purpose: To evaluate the setup accuracies of image-guided intracranial radiosurgery across several different linear accelerator platforms. Methods: A CT scan with a slice thickness of 1.0 mm was acquired of a Rando head phantom (The Phantom Laboratory) in a U-frame mask (BrainLAB AG). The phantom had three embedded BBs, simulating a central, left, and anterior lesion. The phantom was setup with each BB placed at the radiation isocenter under image guidance. Four different setup procedures were investigated: (1) NTX-ExacTrac: 6 degree-of-freedom (6D) correction on a Novalis Tx (BrainLAB AG) with ExacTrac localization (BrainLAB AG); (2) NTX-CBCT: 4D correction on the Novalis Tx with cone-beam computed tomography (CBCT); (3) TrueBeam-CBCT: 4D correction on a TrueBeam (Varian) with CBCT; (4) Edge-CBCT: 6D correction on an Edge (Varian) with CBCT. The experiment was repeated 5 times with different initial setup error at each BB location on each platform, and the mean (μ) and one standard deviation (σ) of the residual error was compared.The congruence between radiation and imaging isocenters on each platform was evaluated by acquiring Winston Lutz (WL) images of a WL jig followed by imaging using ExacTrac or CBCT. The difference in coordinates of the jig relative to radiation and imaging isocenters was then recorded. Results: Averaged over all three BB locations, the residual vector setup errors (μ±σ) of the phantom in mm were 0.6±0.2, 1.0±0.5, 0.2±0.1, and 0.3±0.1 on NTX-ExacTrac, NTX-CBCT, TrueBeam-CBCT, and Edge-CBCT, with their ranges in mm being 0.4∼1.1, 0.4∼1.9, 0.1∼0.5, and 0.2∼0.6, respectively. And imaging isocenter was found stable relative to radiation isocenter, with the congruence to radiation isocenter in mm being 0.6±0.1, 0.7±0.1, 0.3±0.1, 0.2±0.1, respectively, on the four systems in the same order. Conclusion: Millimeter accuracy can be achieved with image-guided radiosurgery for intracranial lesions based on this set of experiments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

MO-FG-CAMPUS-JeP3-01: A Statistical Model for Analyzing the Rotational Error of Single Iso-Center Technique

Purpose: To develop a generalized statistical model that incorporates the treatment uncertainty from the rotational error of single iso-center technique, and calculate the additional PTV (planning target volume) margin required to compensate for this error. Methods: The random vectors for setup and additional rotation errors in the three-dimensional (3D) patient coordinate system were assumed to follow the 3D independent normal distribution with zero mean, and standard deviations σx, σy, σz, for setup error and a uniform σR for rotational error. Both random vectors were summed, normalized and transformed to the spherical coordinates to derive the chi distribution with 3 degrees of freedom for the radical distance ρ. PTV margin was determined using the critical value of this distribution for 0.05 significant level so that 95% of the time the treatment target would be covered by ρ. The additional PTV margin required to compensate for the rotational error was calculated as a function of σx, σy, σz and σR. Results: The effect of the rotational error is more pronounced for treatments that requires high accuracy/precision like stereotactic radiosurgery (SRS) or stereotactic body radiotherapy (SBRT). With a uniform 2mm PTV margin (or σx =σy=σz=0.7mm), a σR=0.32mm will decrease the PTV coverage from 95% to 90% of the time, or an additional 0.2mm PTV margin is needed to prevent this loss of coverage. If we choose 0.2 mm as the threshold, any σR>0.3mm will lead to an additional PTV margin that cannot be ignored, and the maximal σR that can be ignored is 0.0064 rad (or 0.37°) for iso-to-target distance=5cm, or 0.0032 rad (or 0.18°) for iso-to-target distance=10cm. Conclusions: The rotational error cannot be ignored for high-accuracy/-precision treatments like SRS/SBRT, particularly when the distance between the iso-center and target is large.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Method of Moments Wide Band Adaptive Rational Interpolation Method for High-Quality Factor Resonant Cavities

A new adaptive rational interpolation method is proposed to obtain the wideband frequency response of a resonant cavity simulated with the method of moments (MoM). This interpolation method uses both the Loewner matrix to construct a rational expression for the solution vector of MoM’s matrix system and an error estimator generated by the solution vectors and their derivatives. This error estimator is implemented in the adaptive procedure to gain a minimum set of frequencies and solution vectors required in the interpolation. The resulting set of frequencies and solution vectors is applied to interpolate other system variables, such as shielding effectiveness and input impedance. Here numerical results of a slotted cylindrical cavity supporting high-quality factor resonances are presented, showing that the new rational interpolation method is accurate and efficient in interpolating the complicated resonant response of the solution vector functions.

42 ENGINEERING↗
DOE OSTI2015

Balancing aggregation and smoothing errors in inverse models

Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

Turner, A. J.↗
DOE OSTI2020

A machine learning approach for increased throughput of density functional theory substitutional alloy studies

In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network (NN) approach to predict the initial position of minority and majority ions prior to DFT relaxation. The second advancement is to allow the NN to predict the total energy for every possibility minority ion position and select the most stable configuration in the absence of relaxing each trial minority configuration. A bismuth oxide materials system, (Bi x La y Yb z ) 2 MoO 6 , was used as the model system to demonstrate the developed methods and quantify the resulting computational speedup. Compared to a brute force method that requires the calculation of every permutation of minority configuration and subsequent DFT relaxation, a 1.3× speedup was realized if the NN predicted the initial configuration of ions prior to relaxation. Implementation of the second advancement allowed the NN to predict the total energy for all possible trial configurations and downselect the most stable configurations prior to relaxation, resulting in a speedup of approximately 37×. Validation was done by comparing position and energy between the NN and DFT predictions. A maximum position vector mean squared error (MSE) of 1.6 × 10 -2 and a maximum energy MSE of 2.3 × 10 -7 was predicted for the worst case configuration. This method demonstrates a significant computational speedup, which has the potential for even greater computational savings for larger compositional design spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-F-J-84: Comparison of Quantitative Deformable Image Registration Evaluation Tools: Application to Prostate IGART

Purpose: Errors in displacement vector fields (DVFs) generated by Deformable Image Registration (DIR) algorithms can give rise to significant uncertainties in contour propagation and dose accumulation in Image-Guided Adaptive Radiotherapy (IGART). The purpose of this work is to assess the accuracy of two DIR algorithms using a variety of quality metrics for prostate IGART. Methods: Pelvic CT images were selected from an anonymized database of nineteen prostate patients who underwent 8–12 serial scans during radiotherapy. Prostate, bladder, and rectum were contoured on 34 image-sets for three patients by the same physician. The planning CT was deformably-registered to daily CT using three variants of the Small deformation Inverse Consistent Linear Elastic (SICLE) algorithm: Grayscale-driven (G), Contour-driven (C, which utilizes segmented structures to drive DIR), combined (G+C); and also grayscale ITK demons (Gd). The accuracy of G, C, G+C SICLE and Gd registrations were evaluated using a new metric Edge Gradient Distance to Agreement (EGDTA) and other commonly-used metrics such as Pearson Correlation Coefficient (PCC), Dice Similarity Index (DSI) and Hausdorff Distance (HD). Results: C and G+C demonstrated much better performance at organ boundaries, revealing the lowest HD and highest DSI, in prostate, bladder and rectum. G+C demonstrated the lowest mean EGDTA (1.14 mm), which corresponds to highest registration quality, compared to G and C DVFs (1.16 and 2.34 mm). However, demons DIR showed the best overall performance, revealing lowest EGDTA (0.73 mm) and highest PCC (0.85). Conclusion: As expected, both C- and C+G SICLE more accurately reproduce manually-contoured target datasets than G-SICLE or Gd using HD and DSI metrics. In general, the Gd appears to have difficulty reproducing large daily position and shape changes in the rectum and bladder. However, Gd outperforms SICLE in terms of EGDTA and PCC metrics, possibly at the expense of topological quality of the estimated DVFs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Randomized Sketching Algorithms for Low-Memory Dynamic Optimization

This paper develops a novel limited-memory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective. While the control is often low dimensional, the state is typically more expensive to store. This paper suggests using randomized matrix approximation to compress the state as it is generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning

Quantum error mitigation (QEM) is critical in reducing the impact of noise in the pre-fault-tolerant era, and is expected to complement error correction in fault-tolerant quantum computing (FTQC). In this work, we propose a novel QEM approach, Q-Cluster, that uses unsupervised learning (clustering) to reshape the measured bit-string distribution. Our approach starts with a simplified bit-flip noise model. It first performs clustering on noisy measurement results, i.e., bit-strings, based on the Hamming distance. The centroid of each cluster is calculated using a qubit-wise majority vote. Next, the noisy distribution is adjusted with the clustering outcomes and the bitflip error rates using Bayesian inference. Our simulation results show that Q-Cluster can mitigate high noise rates (up to 40% per qubit) with the simple bit-flip noise model. However, real quantum computers do not fit such a simple noise model. To address the problem, we (a) apply Pauli twirling to tailor the complex noise channels to Pauli errors, and (b) employ a machine learning model, ExtraTrees regressor, to estimate an effective bit-flip error rate using a feature vector consisting of machine calibration data (gate & measurement error rates), circuit features (number of qubits, numbers of different types of gates, etc.) and the shape of the noisy distribution (entropy). Our experimental results show that our proposed Q-Cluster scheme improves the fidelity by a factor of 1.46x, on average, compared to the unmitigated output distribution, for a set of low-entropy benchmarks on five different IBM quantum machines. Our approach outperforms the state-of-art QEM approaches RZNE [28], M3 [24], Hammer [35], and QBEEP [33] by 1.26x,1.29x,1.47x, and 2.65 x, respectively.

42 ENGINEERING↗
DOE OSTI2015

SU-E-J-45: The Correlation Between CBCT Flat Panel Misalignment and 3D Image Guidance Accuracy

Purpose To simulate the impact of CBCT flat panel misalignment on the image quality, the calculated correction vectors in 3D image guided proton therapy and to determine if these calibration errors can be caught in our QA process. Methods The X-ray source and detector geometrical calibration (flexmap) file of the CBCT system in the AdaPTinsight software (IBA proton therapy) was edited to induce known changes in the rotational and translational calibrations of the imaging panel. Translations of up to ±10 mm in the x, y and z directions (see supplemental) and rotational errors of up to ±3° were induced. The calibration files were then used to reconstruct the CBCT image of a pancreatic patient and CatPhan phantom. Correction vectors were calculated for the patient using the software’s auto match system and compared to baseline values. The CatPhan CBCT images were used for quantitative evaluation of image quality for each type of induced error. Results Translations of 1 to 3 mm in the x and y calibration resulted in corresponding correction vector errors of equal magnitude. Similar 10mm shifts were seen in the y-direction; however, in the x-direction, the image quality was too degraded for a match. These translational errors can be identified through differences in isocenter from orthogonal kV images taken during routine QA. Errors in the z-direction had no effect on the correction vector and image quality.Rotations of the imaging panel calibration resulted in corresponding correction vector rotations of the patient images. These rotations also resulted in degraded image quality which can be identified through quantitative image quality metrics. Conclusion Misalignment of CBCT geometry can lead to incorrect translational and rotational patient correction vectors. These errors can be identified through QA of the imaging isocenter as compared to orthogonal images combined with monitoring of CBCT image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-C-17A-13: Uncertainty Evaluation of CT Image Deformable Registration for H and N Cancer Adaptive Radiotherapy

Purpose: To evaluate uncertainties of organ specific Deformable Image Registration (DIR) for H and N cancer Adaptive Radiation Therapy (ART). Methods: A commercial DIR evaluation tool, which includes a digital phantom library of 8 patients, and the corresponding “Ground truth Deformable Vector Field” (GT-DVF), was used in the study. Each patient in the phantom library includes the GT-DVF created from a pair of CT images acquired prior to and at the end of the treatment course. Five DIR tools, including 2 commercial tools (CMT1, CMT2), 2 in-house (IH-FFD1, IH-FFD2), and a classic DEMON algorithms, were applied on the patient images. The resulting DVF was compared to the GT-DVF voxel by voxel. Organ specific DVF uncertainty was calculated for 10 ROIs: Whole Body, Brain, Brain Stem, Cord, Lips, Mandible, Parotid, Esophagus and Submandibular Gland. Registration error-volume histogram was constructed for comparison. Results: The uncertainty is relatively small for brain stem, cord and lips, while large in parotid and submandibular gland. CMT1 achieved best overall accuracy (on whole body, mean vector error of 8 patients: 0.98±0.29 mm). For brain, mandible, parotid right, parotid left and submandibular glad, the classic Demon algorithm got the lowest uncertainty (0.49±0.09, 0.51±0.16, 0.46±0.11, 0.50±0.11 and 0.69±0.47 mm respectively). For brain stem, cord and lips, the DVF from CMT1 has the best accuracy (0.28±0.07, 0.22±0.08 and 0.27±0.12 mm respectively). All algorithms have largest right parotid uncertainty on patient #7, which has image artifact caused by tooth implantation. Conclusion: Uncertainty of deformable CT image registration highly depends on the registration algorithm, and organ specific. Large uncertainty most likely appears at the location of soft-tissue organs far from the bony structures. Among all 5 DIR methods, the classic DEMON and CMT1 seem to be the best to limit the uncertainty within 2mm for all OARs. Partially supported by research grant from Elekta.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-F-BRF-03: Quality Assurance of Deformable Image Registration in Radiotherapy

Purpose: To introduce methods to analyze Deformable Image Registration (DIR) and identify regions of potential DIR errors. Methods: DIR Deformable Vector Fields (DVFs) quantifying patient anatomic changes were evaluated using the Jacobian determinant and the magnitude of DVF curl as functions of tissue density and tissue type. These quantities represent local relative deformation and rotation, respectively. Large values in dense tissues can potentially identify non-physical DVF errors. For multiple DVFs per patient, histograms and visualization of DVF differences were also considered. To demonstrate the capabilities of methods, we computed multiple DVFs for each of five Head and Neck (H'N) patients (P1–P5) via a Fast-symmetric Demons (FSD) algorithm and via a Diffeomorphic Demons (DFD) algorithm, and show the potential to identify DVF errors. Results: Quantitative comparisons of the FSD and DFD registrations revealed <0.3 cm DVF differences in >99% of all voxels for P1, >96% for P2, and >90% of voxels for P3. While the FSD and DFD registrations were very similar for these patients, the Jacobian determinant was >50% in 9–15% of soft tissue and in 3–17% of bony tissue in each of these cases. The volumes of large soft tissue deformation were consistent for all five patients using the FSD algorithm (mean 15%±4% volume), whereas DFD reduced regions of large deformation by 10% volume (785 cm{sup 3}) for P4 and by 14% volume (1775 cm{sup 3}) for P5. The DFD registrations resulted in fewer regions of large DVF-curl; 50% rotations in FSD registrations averaged 209±136 cm{sup 3} in soft tissue and 10±11 cm{sup 3} in bony tissue, but using DFD these values were reduced to 42±53 cm{sup 3} and 1.1±1.5 cm{sup 3}, respectively. Conclusion: Analysis of Jacobian determinant and curl as functions of tissue density can identify regions of potential DVF errors by identifying non-physical deformations and rotations. Collaboration with Phillips Healthcare, as indicated in authorship.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Stochastic Vector Techniques in Ground-State Electronic Structure

Herein we review a suite of stochastic vector computational approaches for studying the electronic structure of extended condensed matter systems. These techniques help reduce algorithmic complexity, facilitate efficient parallelization, simplify computational tasks, accelerate calculations, and diminish memory requirements. While their scope is vast, we limit our study to ground-state and finite temperature density functional theory (DFT) and second-order many-body perturbation theory. More advanced topics, such as quasiparticle (charge) and optical (neutral) excitations and higher-order processes, are covered elsewhere. We start by explaining how to use stochastic vectors in computations, characterizing the associated statistical errors. Next, we show how to estimate the electron density in DFT and discuss effective techniques to reduce statistical errors. Finally, we review the use of stochastic vectors for calculating correlation energies within the second-order Møller-Plesset perturbation theory and its finite temperature variational form. Example calculation results are presented and used to demonstrate the efficacy of the methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

$B \to \pi \ell \nu$ and $B_s \to K \ell \nu$ form factors and $|V_{ub}|$ from 2+1-flavor lattice QCD with domain-wall light quarks and relativistic heavy quarks

We calculate the form factors for B → πℓν and B s → Kℓν decay in dynamical lattice quantum chromodynamics (QCD) using domain-wall light quarks and relativistic b-quarks. We use the (2+1)-flavor gauge-field ensembles generated by the RBC and UKQCD collaborations with the domain-wall fermion action and Iwasaki gauge action. For the b-quarks we use the anisotropic clover action with a relativistic heavy-quark interpretation. We analyze data at two lattice spacings of a ≈ 0.11, 0.086 fm with unitary pion masses as light as M π ≈ 290 MeV. We simultaneously extrapolate our numerical results to the physical light-quark masses and to the continuum and interpolate in the pion/kaon energy using SU(2) “hard-pion” chiral perturbation theory for heavy-light meson form factors. We provide complete systematic error budgets for the vector and scalar form factors f + (q 2 ) and f 0 (q 2 ) for both B → πℓν and B s → Kℓν at three momenta that span the q 2 range accessible in our numerical simulations. Next we extrapolate these results to q 2 = 0 using a model-independent z-parametrization based on analyticity and unitarity. We present our final results for f + (q 2 ) and f 0 (q 2 )as the coefficients of the series in z and the matrix of correlations between them; this provides a parametrization of the form factors valid over the entire allowed kinematic range. Our results agree with other three-flavor lattice-QCD determinations using staggered light quarks, and have comparable precision, thereby providing important independent cross-checks. Both B → πℓν and B s → Kℓν decays enable determinations of the Cabibbo-Kobayashi-Maskawa matrix element |V ub |. Furthermore, we perform a combined z-fit of our numerical B → πℓν form-factor data with the experimental measurements of the branching fraction from BABAR and Belle leaving the relative normalization as a free parameter; we obtain |V ub | = 3.61(32)×10 -3 , where the error includes statistical and all systematic uncertainties. The same approach can be applied to the decay B s → Kℓν to provide an alternative determination of |V ub | once the process has been measured experimentally. In anticipation of future experimental measurements, we make predictions for B → πℓν and B s → Kℓν differential branching fractions and forward-backward asymmetries in the Standard Model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Structure Factors of Neutron Matter at Finite Temperature

Here, we compute continuum and infinite volume limit extrapolations of the structure factors of neutron matter at finite temperature and density. Using a lattice formulation of leading-order pionless effective field theory, we compute the momentum dependence of the structure factors at finite temperature and at densities beyond the reach of the virial expansion. The Tan contact parameter is computed and the result agrees with the high momentum tail of the vector structure factor. All errors, statistical and systematic, are controlled for. This calculation is a first step towards a model-independent understanding of the linear response of neutron matter at finite temperature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗