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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Efficient Monte Carlo event generation for neutrino-nucleus exclusive cross sections

Modern neutrino-nucleus cross section computations need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing normalizing flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than 98.4%, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

inverse-cnf

Inverse machine learning model using Conditional Normalizing Flow (CNF)

DeBardeleben, Nathan Andrew [Los Alamos National L↗
DOE OSTI2025

High-dimensional maximum-entropy phase space tomography

Reconstructing 4D or 6D phase space distributions from 1D or 2D measurements is a challenging inverse problem encountered in particle accelerators. Entropy maximization is an established method to incorporate prior information in the reconstruction, but it is typically infeasible in high-dimensional spaces. In this paper, I review two recent approaches to high-dimensional entropy maximization. The first approach utilizes differentiable simulations and a class of generative models known as normalizing flows, whereas the second approach employs the method of Lagrange multipliers and Markov Chain Monte Carlo (MCMC) sampling. My aim is to provide a short explanation of each method using a common notation. I conclude by mentioning several unsolved problems in phase space tomography.

Hoover, Austin [ORNL] (ORCID:0000000153136962)↗
DOE OSTI2025

Hierarchical Bayesian Modeling for Cosmology: Can NPE reliably replace MCMC?

Hierarchical neural posterior estimation has its place Hierarchical Bayesian Modeling (HBM) combined with MCMC algorithms has been shown to provide more robust and accurate inference for real-world phenomena in which nature takes a nested form. However, MCMC-based inference can be computationally expensive, and its performance often suffers for complex posterior geometries. These costs are especially pertinent for HBM. Studies have recently demonstrated the potential for a flexible, expressive, and amortized hierarchical neural posterior estimator (HNPE) built on Normalizing Flows. These studies have mostly been performed on simple datasets, or they focus on a single parameter from each level of the hierarchy. A systematic study analyzing how both hierarchical methods compare for more complex and realistic datasets is necessary before applying HNPE for scientific measurements. Here, we re-explore the theory behind HNPE and conduct comparative numerical experiments of HNPE and MCMC-based HBM methods on real and synthetic data, including strong gravitational lensing simulations. In particular, we use a suite of diagnostics to show trade-offs in terms of accuracy, precision, time to train or sample, reproducibility, and the need for expert domain knowledge. Especially for higher dimensional and complex posteriors, HNPE is expected to drastically improve on time for inference, accuracy, and precision with an upfront training time cost.

Hur, Rachel [Chicago U.] (ORCID:000900089890445X)↗
DOE OSTI2026

An Iterative Machine Learning Framework for Event Classification and Monte Carlo Tuning in SpinQuest

The E1039/SpinQuest experiment at Fermi National Accelerator Laboratory uses a 120~GeV proton beam from the Main Injector incident on transversely polarized proton and deuteron targets, using $NH_3$ and $ND_3$, respectively. In addition to measuring the Sivers asymmetry in Drell--Yan $pp$ and $pd$ scattering from sea quarks, SpinQuest will study transverse-spin effects, particularly the transverse single-spin asymmetry (TSSA) in $J/\psi$ production. The angular distributions from the $J/\psi$ decay could play an important role in understanding the gluon contribution to the proton spin structure. However, before extracting these angular distributions, it is necessary to isolate signal events originating from the target from events produced by other sources and from the combinatorial background. To effectively and accurately classify the target events, it is important to ensure that the simulated events are properly tuned to the experimental physics channels. We have introduced an iterative technique to match simulated and experimental events and to classify the physics channels using deep neural networks and a generative model based on normalizing flows.

Hossain, Forhad [Virginia U. (main)] (ORCID:000000↗
DOE OSTI2026

The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Speed Distributions

Dark matter direct detection experiments require information about the local dark matter speed distribution to produce constraints on dark matter candidates, or infer their properties in the event of a discovery. In this paper, we analyze how the uncertainty in the dark matter speed distribution near the Sun is affected by baryonic feedback, halo-to-halo variance, and halo mass. To do so, we harness the statistical power of the new DREAMS cold dark matter simulation suite, which is comprised of 1024 zoom-in Milky Way–mass halos with varied initial conditions as well as cosmological and astrophysical parameters. Applying a normalizing flows emulator to these simulations, we find that the uncertainty in the local dark matter speed distribution is dominated by halo-to-halo variance and, to a lesser extent, uncertainty in host halo mass. Uncertainties in supernova and black hole feedback (from the IllustrisTNG model in this case) are negligible in comparison. Using the DREAMS suite, we present a state-of-the-art prediction for the dark matter speed distribution in the Milky Way. Although the standard halo model is contained within the uncertainty of this prediction, individual galaxies may have distributions that differ from it. Lastly, we apply our DREAMS results to the XENON1T experiment and demonstrate that the astrophysical uncertainties are comparable to the experimental ones, solidifying previous results in the literature obtained with a smaller sample of simulated Milky Way–mass halos.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Splitting supersonic nozzle flow into separate jets by overexpansion into a multilobed divergent nozzle

Air flowing from a convergent nozzle at pressure ratios greater than 2.5 has been split into eight separate jets by overexpansion of the flow into a divergent, eight-lobed passage. The splitting of the flow is accompanied by a decrease in the nozzle axial centerline Mach number. This in part is due to the radial inflow of secondary air between the lobes toward the nozzle centerline. Each of the smaller jets is partially split after it leaves the end of the divergent lobed section of the nozzle, thus creating a velocity profile having 16 peaks. At a pressure ratio of 3.5 the flow decelerates to Mach 1 in three convergent nozzle throat diameters. Convergent nozzle flow normally requires 12 diameters to reach Mach 1. The nozzle has a sound attenuation of 12 decibels with a thrust loss of 9 percent for the best configuration tested.

Huff, R. G.↗
NASA NTRS1972

Experiments on a turbulent jet in a cross flow.

Results are reported of experiments on turbulent circular jets issuing into a cross flow, both for unheated and heated jets. Longitudinal and transverse distributions of velocity, temperature, and turbulence intensity are presented. The velocity distributions depend mainly on the ratio of jet to cross flow momentum flux. The temperature distributions depend also on the density ratio. The jet structure is dominated by a vortex wake which forms behind the jet, which is evident both from the detail measurements and from smoke photographs of the flow. The experiments also indicate that the components of cross flow normal and parallel to the jet trajectory independently control the entrainment rate.

Kamotani, Y.↗
NASA NTRS1977

Theoretical study of the effect of ground proximity on the induced efficiency of helicopter rotors

A study of rotors in forward flight within ground effect showed that the ground-induced interference is an upwash and a decrease in forward velocity. The interference velocities are large, oppose the normal flow through the rotor, and have large effects on the induced efficiency. Hovering with small ground clearances may result in significant blade stall. As speed is increased from hover in ground effect, power initially increases rather than decreases. At very low heights above the ground, the power requirements become nonlinear with speed as a result of the streamwise interference. The streamwise interference becomes greater as the wake approaches the ground and eventually distorts the wake to form the ground vortex which contributes to certain observed directional stability problems.

Heyson, H. H.↗
NASA NTRS1980

Toward the use of similarity theory in two-phase choked flows

Comparison of two phase choked flows in normalized coordinates were made between pure components and available data using a reference fluid to compute the thermophysical properties. The results are favorable. Solution of the governing equations for two LNG mixtures show some possible similarities between the normalized choked flows of the two mixtures, but the departures from the pure component loci are significant.

Hendericks, R. C.↗
NASA NTRS1981

Wind tunnel supplementary Mach number minimum section insert

A device is described which changes the Mach number capability of a wind tunnel without permanently altering the existing nozzle of the tunnel. An insert is removably attached to the wall of the existing nozzle expansion area thereby creating a second minimum section upstream of the model test section. The added insert may be removed without complicated and expensive changes to the basic wind tunnel. In one embodiment, a removable insert is disposed within wind tunnel nozzle walls with a portion of the flow boundary layer being bled off from the tunnel via passageway and tunnel exit to reduce the extent of separated flow normally occuring upstream of the insert contraction section.

Couch, L. M.↗
NASA NTRS1980

Toward the use of similarity theory in two-phase choked flows

Comparison of two-phase choked flows in normalized coordinates were made between pure components and available data using a reference fluid to compute the thermophysical properties. The results are favorable. Solution of the governing equations for two LNG mixtures show some possible similarities between the normalized choked flows of the two mixtures, but the departures from the pure component locii are significant.

Hendricks, R. C.↗
NASA NTRS1961

Heat Transfer at the Reattachment Zone of Separated Laminar Boundary Layers

The flow and heat transfer are analyzed at the reattachment zone of two-dimensional separated laminar boundary layers. The fluid is considered to be flowing normal to the wall at reattachment. An approximate expression is derived for the heat transfer in the reattachment region and a calculated value is compared with an experimental measurement.

Chung, Paul M.↗
NASA NTRS1976

Beach Erosion

Two miles of beach at Cape Canaveral eroded by construction of a port and jetties was recently restored. Such work in harbors of many cities often disrupts normal flow of sand for many miles along coasts. Brevard County, FL residents now enjoy a 400 ft. wide public beach in an area in imminent danger of destructive erosion just a year previously. Before and after aerial photos show how more than two miles of beach were rebuilt with 2.7 million cubic yards of sand helping abate the erosion problem caused by construction of jetties. NASA volunteered its remote-sensing technology and instrumented aircraft to provide low-altitude color infrared photography about every three months since 1972.

Source record↗
DOE OSTI2026

Learning the generating functional for variance reduction in lattice QCD

The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators. We present a methodology that leverages machine-learned normalizing flows to reduce the variance of arbitrary $N$-point correlation functions of bosonic operators in lattice gauge field theory calculations by encoding a representation of the generating functional. We show that it is possible to systematically approach noiseless estimators of correlation functions in this framework. We demonstrate this methodology with applications to calculations of glueball correlation functions and Wilson loops in Quantum Chromodynamics and Yang-Mills theory. The results show up to three orders of magnitude variance reduction.

Abbott, Ryan [Columbia U.] (ORCID:0000000258778005↗
DOE OSTI2026

Probabilistic Inference of Low-Surface-Brightness Galaxy Morphological Parameters Using Simulation-Based Inference

Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.

Batbayar, Bilguun [U. Chicago (main)]↗
NASA NTRS1974

Normalizing parameters for the critical flow rate of simple fluids through nozzles

It is shown that two-phase critical nozzle flow of simple fluids almost obeys the principle of corresponding states. Quantum fluid departures from the principle are resolved as a function of temperature for para-hydrogen and helium. The critical flow rates are normalized, using a normalizing parameter for which the critical flow rates of all simple fluids reduce to a single isothermal curve. The expression obtained for the normalizing parameter is shown to provide good agreement with the experiment for the critical flow rates of nitrogen, oxygen, and para-hydrogen.

Hendricks, R. C.↗