Search NASA⌕ Search

SEARCH · Search NASA

Results for “modeling discrepancy”

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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Climatological Structures of the GRIPS Models: Mean States and Forcing

The GCM-Reality Intercomparison Project for SPARC (GRIPS) is assessing and monitoring the performance of state-of-the-art general circulation models (GCMs). A wide variety of tasks have been initiated. These are designed to: (1) assess the ability of the GCMs to represent the current climatological structure of the troposphere and middle atmosphere,(2) to compare their response to imposed forcing anomalies, and (3) to estimate the certainty with which future climate perturbations can be predicted. This paper is concerned with assessments of the climatological states in the GCM simulations. Comparing the simulations with observational datasets reveals considerable discrepancies in the modelled fields. While it might be anticipated that certain types of biases in the model simulations might be related to the formulation of different aspects of the numerical package (dynamical schemes, cloud schemes, radiation transfer, inclusion of gravity wave drag), there is no clear relationship between these features. This paper attempts to draw a more comprehensive picture of the GCMs'performance than has previously been shown, by comparing the dominant forcing mechanisms in the models with observational estimates, and relating model deficiencies to the differences in the physical mechanisms in the GCMS.

Pawson, Steven↗
DOE OSTI2020

An experimental investigation of flame and autoignition behavior of propane

We report autoignition delay time data are one important means to develop, quantify, and validate fundamental understanding of combustion chemistry at low temperatures (T<1200 K). However, low-temperature chemistry often has higher uncertainties and scatter in the experimental data compared with high-temperature ignition data (T>1200 K). In this study, autoignition properties of propane and oxygen mixtures were investigated using the University of Michigan rapid compression facility in order to understand the effects of ignition regimes on low-temperature ignition data. For the first time for propane, autoignition delay times were determined from pressure histories, and autoignition characteristics were simultaneously recorded using highspeed imaging of the test section through a transparent end-wall. Propane mixtures with fuel-to-O 2 equivalence ratios of φ = 0.25 and φ = 0.5 and O 2 -to-inert gas molar ratios of 1:3.76 were studied over the pressure range of 8.9 to 11.3 atm and the temperature range of 930 – 1070 K. The results showed homogeneous or strong autoignition occurred for all φ = 0.25 experiments, and inhomogeneous or mixed autoignition occurred for all φ = 0.5 experiments. While a limited temperature range is covered in the study, importantly the data span predicted transitions in autoignition behavior, allowing validation of autoignition regime hypotheses. Specifically, the results agree well with strong-autoignition limits proposed based on the Sankaran Criterion. The autoignition delay time data at the strong-ignition conditions are in excellent agreement with predictions using a well-validated detailed reaction mechanism from the literature and a zerodimensional modeling assumption. However, the experimental data at the mixed autoignition conditions were systematically faster than the model predictions, particularly at lower temperatures (T< ~970 K). The results are an important addition to the growing body of data in the literature that show mixed autoignition phenomena are important sources of the higher scatter observed in the low-temperature autoignition data for propane and other fuels. The results are discussed in terms of different methods to capture the effects of pre-autoignition heat release associated with mixed autoignition conditions and thereby address some of the discrepancies between kinetic modeling and experimental measurements.

42 ENGINEERING↗
NASA NTRS2004

Downscattering due to Wind Outflows in Compact X-ray Sources: Theory and Interpretation

A number of recent lines of evidence point towards the presence of hot, outflowing plasma from the central regions of compact Galactic and extragalactic X-ray sources. Additionally, it has long been noted that many of these sources exhibit an "excess" continuum component, above approx. 10 keV, usually attributed to Compton Reflection from a static medium. Motivated by these facts, as well as by recent observational constraints on the Compton reflection models - specifically apparently discrepant variability timescales for line and continuum components in some cases - we consider possible of effects of out-flowing plasma on the high-energy continuum spectra of accretion powered compact objects. We present a general formulation for photon downscattering diffusion which includes recoil and Comptonization effects due to divergence of the flow. We then develop an analytical theory for the spectral formation in such systems that allows us to derive formulae for the emergent spectrum. Finally we perform the analytical model fitting on several Galactic X-ray binaries. Objects which have been modeled with high-covering-fraction Compton reflectors, such as GS1353-64 are included in our analysis. In addition, Cyg X-3, is which is widely believed to be characterized by dense circumstellar winds with temperature of order 10(exp 6) K, provides an interesting test case. Data from INTEGRAL and RXTE covering the approx. 3 - 300 keV range are used in our analysis. We further consider the possibility that the widely noted distortion of the power-law continuum above 10 keV may in some cases be explained by these spectral softening effects.

Titarchuk, Lev↗
NASA NTRS2003

The Ionized Gas and Nuclear Environment in NGC 3783: Variability and Modeling of the 900 ks CHANDRA Spectrum - IV

We present a detailed analysis of the 900 ks spectrum of NGC3783 obtained by Chandra in 2000-2001 (Kaspi et al. 2002). We split the data in various ways to look for time dependent and luminosity dependent spectral variations. This analysis, the measured equivalent widths of a large number of X-ray lines, and our photoionization calculations, lead us to the following conclusions: 1) NGC 3783 fluctuated in luminosity, by a factor N 1.5, during individual 170 ks observations. The fluctuations were not associated with significant spectral variations. 2) On a longer time scale, of 20-120 days, we discovered two very different spectral shapes that are noted the high state and the low state spectra. The observed changes between the two can be described as the appearance and disappearance of a soft continuum component. The spectral variations are not related, in a simple way, to the brightening or the fading of the short wavelength continuum, as observed in other objects. NGC3783 seems to be the first AGN to show this unusual behavior. 3) The appearance of the soft continuum component is consistent with beeing the only spectral variation and there is no need to invoke changes in the absorber s opacity. In particular, all absorption lines with reliable measurements show the same equivalent width, within the observational uncertainties, during high and low states. 4) Photoionization model calculations show that a combination of three ionization components, each split into two kinematic components, explain very well the intensity of almost all absorption lines and the bound-free absorption. The components span a large range of ionization and a total column of about 3 x 10(exp 22) per square centimeter Moreover, all components are thermally stable and are situated on the vertical branch of the stability curve.. This means that they are in pressure equilibrium and perhaps occupy the same volume of space. This is the first detection of such a multi-component equilibrium gas in AGN. 5) The only real discrepancy between the model and the observations is the wavelength of the iron M-shell UTA feature. This is most likely due to an underestimation of the dielectronic recombination O VI and discuss its possible origin. 6) The lower limit on the distance of the absorbing gas in NGC3783 is between 0.2 and 3.2 pc, depending of the specific ionization component. The constant pressure assumption imposes an upper limit of about 25 pc on the distance of the least ionized gas from the central sourec.

Netzer, Hagai↗
DOE OSTI2024

Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution. Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O ( n log n ) instead of the typical O ( n 3 ) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Finally, although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Feedback between radiation and transport in photovoltaics

By building on the generalized Hovel model, this work develops an analytical solution to unify photon recycling, light trapping, and carrier transport in solar cells. Enhanced transport due to photon recycling diffusion is considered. While photon recycling diffusion is often negligible, it can significantly support transport in cases where charge diffusion is low but radiative efficiency is high. Next, the diffusion equation is solved to determine carrier collection for planar and textured cells. Results elucidate novel features in the external quantum efficiency (EQE) curve that occur for textured cells with high absorption and limited transport. Then, the effect of limited transport on the radiative recombination of solar cells is investigated in detail. While the traditional treatment of detailed balance uses the applied voltage to calculate a cell's radiative recombination, the present approach uses the spatially resolved quasi-Fermi-level splitting (QFLS). Further, the discrepancy between the models is shown to be inconsequential to performance for cells with low radiative recombination (Si) or with high mobilities (lowly doped GaAs). However, for cells with high radiative recombination and low mobility (highly doped GaAs, polycrystalline CdTe, and thin-film perovskites), incorporating variation in QFLS in the determination of radiative recombination can become necessary for accurate photovoltaic modeling.

14 SOLAR ENERGY↗
NASA NTRS2011

Impact of January 2005 Solar Proton Events on Chlorine Species

Sudden changes in stratospheric chlorine species in the polar northern atmosphere, caused by the Solar Proton Events (SPEs) of 17 and 20 January 2005, have been investigated and compared with version 4 of the Whole Atmosphere Community Climate Model (WACCM4). We used Aura Microwave Limb Sounder (MLS) measurements to monitor the variability of ClO, HCl, HOCl and Michelson Interferometer for Passive Atmospheric Sounder (MIPAS) on ENVISAT to retrieve ClONO2. SPE-induced chlorine activation has been identified. HCl decrease occurred at nearly all the investigated altitudes with the lowest values (of less than 0.25 ppbv) on 21 January. HOCl was found to be the main active chlorine species under nighttime conditions (with increases of more than 0.2 ppbv) whereas both HOCL and CLO enhancements (about 0.1 ppbv) have been observed at the polar night terminator. Further, small ClO decreases (of less than 0.1 ppbv) and ClONO2 enhancements (~0.2 ppbv) have been observed at higher latitudes (i.e., at nighttime) roughly above 2hPa. While WACCM4 reproduces most of the SPE-induced variability in the chlorine species fairly well, in some particular regions discrepancies between the modeled and measured temporal evolution of the abundances of chlorine species were found. HOCl changes are modelled very well with respect to both magnitude and geographic distribution. ClO decreases are reproduced at high latitudes, whereas ClO enhancements in the terminator region are underestimated and attributed to background variations. WACCM4 also reproduces the HCl depletion in the mesosphere but it does not show the observed decrease below about 2 hPa. Finally, WACCM4 simulations indicate that the observed ClONO2 increase is dominated by background variability, although SPE-induced production might contribute by 0.1 ppbv.

Damiani, A.↗
NASA NTRS2012

Impact of January 2005 Solar Proton Events on Chlorine Species

Sudden changes in stratospheric chlorine species in the polar northern atmosphere, caused by the Solar Proton Events (SPEs) of 17 and 20 January 2005, have been investigated and compared with version 4 of the Whole Atmosphere Community Climate Model (WACCM4). We used Aura Microwave Limb Sounder (MLS) measurements to monitor the variability of ClO, HCl, HOCl and Michelson Interferometer for Passive Atmospheric Sounder (MIPAS) on ENVISAT to retrieve ClONO2. SPE-induced chlorine activation has been identified. HCl decrease occurred at nearly all the investigated altitudes (i.e., 10-0.5 hPa) with the strongest decrease (of about 0.25 ppbv) on 21 January. HOCl was found to be the main active chlorine species under nighttime conditions (with increases of more than 0.2 ppbv) whereas both HOCl and ClO enhancements (about 0.1 ppbv) have been observed at the polar night terminator. Further, small ClO decreases (of less than 0.1 ppbv) and ClONO2 enhancements (about 0.2 ppbv) have been observed at higher latitudes (i.e., at nighttime) roughly above 2 hPa. While WACCM4 reproduces most of the SPE-induced variability in the chlorine species fairly well, in some particular regions discrepancies between the modeled and measured temporal evolution of the abundances of chlorine species were found. HOCl changes are modelled very well with respect to both magnitude and geographic distribution. ClO decreases are reproduced at high latitudes, whereas ClO enhancements in the terminator region are underestimated and attributed to background variations. WACCM4 also reproduces the HCl depletion in the mesosphere but it does not show the observed decrease below about 2 hPa. Finally, WACCM4 simulations indicate that the observed ClONO2 increase is dominated by background variability, although SPE-induced production might contribute by 0.1 ppbv.

Damiani, A.↗
NASA NTRS2004

A Review of the Match Technique as Applied to AASE-2/EASOE and SOLVE/THESEO 2000

We apply the GSFC trajectory model with a series of ozonesondes to derive ozone loss rates in the lower stratosphere for the AASE-2/EASOE mission (January - March 1992) and for the SOLVE/THESEO 2000 mission (January - March 2000) in an approach similar to Match. Ozone loss rates are computed by comparing the ozone concentrations provided by ozonesondes launched at the beginning and end of the trajectories connecting the launches. We investigate the sensitivity of the Match results on the various parameters used to reject potential matches in the original Match technique and conclude that only a filter based on potential vorticity changes along the calculated back trajectory seems necessary. Our study also demonstrates that calculated ozone loss rates can vary by up to a factor of two depending upon the precise trajectory paths calculated for each trajectory. As a result an additional systematic error might need to be added to the statistical uncertainties published with previous Match results. The sensitivity to the trajectory path is particularly pronounced in the month of January, the month during which the largest ozone loss rate discrepancies between photochemical models and Match are found. For most of the two study periods, our ozone loss rates agree with those previously published. Notable exceptions are found for January 1992 at 475 K and late February/early March 2000 at 450 K, both periods during which we find less loss than the previous studies. Integrated ozone loss rates in both years compare well with those found in numerous other studies and in a potential vorticity/potential temperature approach shown previously and in this paper. Finally, we suggest an alternate approach to Match using trajectory mapping that appears to more accurately reflect the true uncertainties associated with Match and reduces the dependence upon filters that may bias the results of Match through the rejection of greater than or equal to 80% of the matched sonde pairs and >99% of matched observations.

Morris, Gary A.↗
NASA NTRS2012

Assessing Spectral Shortwave Cloud Observations at the Southern Great Plains Facility

The Atmospheric Radiation Measurement (ARM) program (now Atmospheric System Research) was established, in part, to improve radiation models so that they could be used reliably to compute radiation fluxes through the atmosphere, given knowledge of the surface albedo, atmospheric gases, and the aerosol and cloud properties. Despite years of observations, discrepancies still exist between radiative transfer models and observations, particularly in the presence of clouds. Progress has been made at closing discrepancies in the spectral region beyond 3 micron, but the progress lags at shorter wavelengths. Ratios of observed visible and near infrared cloud albedo from aircraft and satellite have shown both localized and global discrepancies between model and observations that are, thus far, unexplained. The capabilities of shortwave surface spectrometry have been improved in recent years at the Southern Great Plains facility (SGP) of the ARM Climate Research Facility through the addition of new instrumentation, the Shortwave Array Spectroradiometer, and upgrades to existing instrumentation, the Shortwave Spectroradiometer and the Rotating Shadowband Spectroradiometer. An airborne-based instrument, the HydroRad Spectroradiometer, was also deployed at the ARM site during the Routine ARM Aerial Facility Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign. Using the new and upgraded spectral observations along with radiative transfer models, cloud scenes at the SGP are presented with the goal of characterizing the instrumentation and the cloud fields themselves.

McBride, P. J.↗
DOE OSTI2025

Experimental study of energy-dependent angular broadening of MeV electron beams for high-resolution imaging in thick samples

In scanning transmission electron microscopy (STEM), spatial resolution is primarily influenced by the projected size of the electron probe within the specimen. In thin samples, a large semi-convergence angle enables a tightly focused beam and sub-nanometer resolution. However, in thick specimens, resolution is fundamentally limited by transverse beam broadening from multiple large-angle scattering events—for example, a probe with 10 mrad angular divergence can broaden by ∼100 nm over a 10 μm path. Since this broadening scales inversely with beam energy, MeV-STEM offers a promising route for high-resolution imaging in thick materials. To quantitatively assess this effect, we performed high-precision measurements at UCLA’s PEGASUS beamline, characterizing beam divergence and intensity profiles for 3–8 MeV electrons transmitted through a wedged-silicon sample of varying thickness. Our results reconcile discrepancies among analytical models and validate Monte Carlo simulations. Here, we find that increasing beam energy from 3.0 to 5.8 MeV reduces angular broadening by a factor of 2.6, with diminishing returns observed at 7.6 MeV. These findings provide a quantitative framework for optimizing MeV-STEM parameters in high-resolution imaging of thick biological and microelectronic specimens, and for guiding beam energy selection in other advanced imaging modes beyond STEM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R S ), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R S are irreconcilable. When we estimate GPP based on R S measurements and some assumptions about R S :GPP ratios, we found the resulted global GPP values (bootstrap mean ${149}^{+29}_{-23}$ Pg C yr ⁻1 ) are significantly higher than most GPP estimates reported in the literature (${113}^{+18}_{-18}$ Pg C yr ⁻1 ). Similarly, historical GPP estimates imply a soil respiration flux (Rs GPP , bootstrap mean of ${68}^{+10}_{-8}$ Pg C yr ⁻1 ) statistically inconsistent with most published R S values (${87}^{+9}_{-8}$ Pg C yr ⁻1 ), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R S :GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei

The limits of the nuclear landscape are determined by nuclear binding energies. Beyond the proton drip lines, where the separation energy becomes negative, there is not enough binding energy to prevent protons from escaping the nucleus. Predicting properties of unstable nuclear states in the vast territory of proton emitters poses an appreciable challenge for nuclear theory as it often involves far extrapolations. In addition, significant discrepancies between nuclear models in the proton-rich territory call for quantified predictions. With the help of Bayesian methodology, we mix a family of nuclear mass models corrected with statistical emulators trained on the experimental mass measurements, in the proton-rich region of the nuclear chart. Separation energies were computed within nuclear density functional theory using several Skyrme and Gogny energy density functionals. We also considered mass predictions based on two models used in astrophysical studies. Quantified predictions were obtained for each model using Bayesian Gaussian processes trained on separation-energy residuals and combined via Bayesian model averaging. Here, we obtained a good agreement between averaged predictions of statistically corrected models and experiment. In particular, we quantified model results for one- and two-proton separation energies and derived probabilities of proton emission. This information enabled us to produce a quantified landscape of proton-rich nuclei. The most promising candidates for two-proton decay studies have been identified. Finally, the methodology used in this work has broad applications to model-based extrapolations of various nuclear observables. It also provides a reliable uncertainty quantification of theoretical predictions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Nonlocal nucleon matrix elements in the rest frame

Extracting parton structure from lattice quantum chromodynamics (QCD) calculations requires studying the coordinate scale 𝑧 3 dependence of the matrix elements of bilocal operators. The most significant contribution comes from the 𝑧 3 dependence induced by ultraviolet (UV) renormalization of the Wilson line. We demonstrate that the next-to-leading order perturbative calculations of the renormalization factor can describe, to a few percent accuracy, the logarithm of the lattice QCD rest frame matrix elements with separations up to distances of 0.6 fm on multiple lattice spacings. The residual discrepancies can be modeled by a leading effect from the structure of the nucleon.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗
DOE OSTI2024

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized numerical linear algebra. We analyze the properties of estimated probabilities of logistic regression when leverage scores are used to sample observations, and prove that accurate approximations can be achieved with a sample whose size is much smaller than the total number of observations. To further validate our theoretical findings, we conduct comprehensive empirical evaluations. Overall, our work sheds light on the potential of using randomized sampling approaches to efficiently approximate the estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets.

Chowdhury, Agniva↗
DOE OSTI2024

Simulation Study of High-Precision Characterization of MeV Electron Interactions for Advanced Nano-Imaging of Thick Biological Samples and Microchips

The resolution of a mega-electron-volt scanning transmission electron microscope (MeV-STEM) is primarily governed by the properties of the incident electron beam and angular broadening effects that occur within thick biological samples and microchips. A precise understanding and mitigation of these constraints require detailed knowledge of beam emittance, aberrations in the STEM column optics, and energy-dependent elastic and inelastic critical angles of the materials being examined. This simulation study proposes a standardized experimental framework for comprehensively assessing beam intensity, divergence, and size at the sample exit. This framework aims to characterize electron-sample interactions, reconcile discrepancies among analytical models, and validate Monte Carlo (MC) simulations for enhanced predictive accuracy. Our numerical findings demonstrate that precise measurements of these parameters, especially angular broadening, are not only feasible but also essential for optimizing imaging resolution in thick biological samples and microchips. By utilizing an electron source with minimal emittance and tailored beam characteristics, along with amorphous ice and silicon samples as biological proxies and microchip materials, this research seeks to optimize electron beam energy by focusing on parameters to improve the resolution in MeV-STEM/TEM. This optimization is particularly crucial for in situ imaging of thick biological samples and for examining microchip defects with nanometer resolutions. Our ultimate goal is to develop a comprehensive mapping of the minimum electron energy required to achieve a nanoscale resolution, taking into account variations in sample thickness, composition, and imaging mode.

36 MATERIALS SCIENCE↗