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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Gaussian Process Regression Method for Costing SmallSat Bus Capabilities

NASA is responding to the growing interest in, andcapabilities of, small satellites for science applications with an increasingnumber and frequency of Announcements of Opportunityfor small satellite space missions. Estimating the probabilitythat these mission concepts will fit within the small cost capsof these opportunities is largely driven by the probability thatone of the burgeoning number of small satellite providers will beable to meet the payload’s accommodation requirements withinthe budget for the spacecraft. JPL has collected a databasecontaining technical specifications and cost of commerciallyavailable Smallsat buses across various vendors. The primarypurpose of the database is for use in JPL’s Team X architecturestudies to inform cost estimates of a spacecraft bus which fitsthe customer’s technical requirements for their payload andmission. Customer needs are often unique and don’t alignperfectly with an off-the-shelf commercial spacecraft bus, whichmotivates the need to develop a cost model across the continuoustechnical parameter space.Al’s Bus Cost Distribution Estimator (ABCDE) uses Gaussianprocess regression (GPR) to predict commercial Smallsat spacecraftbus cost based on a subset of a customer’s technicalrequirements (payload mass, payload power, delta V, pointingcontrol, and downlink rate). GPR is implemented in ABCDE asa Bayesian method which fits an implied multivariate regressionon the technical parameters and uses kriging to intentionally“overfit” the residuals. Overfitting the residuals allows costestimates to collapse in uncertainty closer to the data pointswhile maintaining larger uncertainty intervals in regions of parameterspace with fewer data records. The data used to fit thismodel is sensitive and represents cost estimates for off-the-shelfcommercial buses. GPR simultaneously protects the sensitivityof the database and uses the sparse nature of the database toaccount for uncertainty in cost in a useful way. For a givenset of customer technical requirements, the tool provides a costestimate distribution, the percentiles of which can be interpretedas a confidence level of finding a commercial bus under a specifiedcost cap. ABCDE dramatically pushes the boundaries ofspacecraft cost estimation models due to its Bayesian methodology(accounting for the maximum uncertainty in the underlyingregression), the mathematically advanced kriging methodology,and the novelty of its application in Team X architecture tradestudies.

Austin, Alex↗
DOE OSTI2020

Harnessing the power of ab initio calculations, distributed computing and machine learning to efficiently locate extreme molecules for use in carbon-based solar cells (Final Technical Report)

The use of high-throughput virtual screening (HTVS) tools is a powerful tool to expedite the materials discovery of commercially relevant materials. In previous years, our group has developed a molecular discovery platform to generate libraries in order to obtain suitable candidates for different applications, starting from the Harvard Clean Energy Project. This platform is suitable to test in-silico on traditional supercomputing clusters and shared resources, for example, in the IBM World Community Grid. In this project, we used the molecular discovery platform to create and screen a library of candidates of organic photovoltaic (OPVs) molecules. Based on a set of candidates created with combinations of molecular moieties, we were able to filter, by conformation stability, the energy of electronic orbitals and approximated power conversion efficiencies (PCE). To improve the predictions of orbital energies calculated and the PCEs, we used Gaussian Process regression and two sets of molecules. These sets correspond to electronic structure calculations of a higher level of theory and experimental PCE values, respectively. Finally, we selected a subset of the best candidates (molecules with a PCE higher than 10%) to understand its absorbance properties with TD-DFT. This project has demonstrated the capabilities of our molecular discovery platform for HTVS. Finally, machine learning can help us to introduce more complex effects included in bulk conditions and computational intensive calculations on models.

14 SOLAR ENERGY↗
NASA NTRS1993

Analysis of microstrip dipoles and slots transversely coupled to a microstrip line using the FDTD method

Printed dipole elements and their complement, linear slots, are elementary radiators that have found use in low-profile antenna arrays. Low-profile antenna arrays, in addition to their small size and low weight characteristics, offer the potential advantage of low-cost, high-volume production with easy integration with active integrated circuit components. The design of such arrays requires that the radiation and impedance characteristics of the radiating elements be known. The FDTD (Finite-Difference Time-Domain) method is a general, straight-forward implementation of Maxwell's equations and offers a relatively simple way of analyzing both printed dipole and slot elements. Investigated in this work is the application of the FDTD method to the analysis of printed dipole and slot elements transversely coupled to an infinite transmission line in a multilayered configuration. Such dipole and slot elements may be used in dipole and slot series-fed-type linear arrays, where element offsets and interelement line lengths are used to obtain the desired amplitude distribution and beam direction, respectively. The design of such arrays is achieved using transmission line theory with equivalent circuit models for the radiating elements. In an equivalent circuit model, the dipole represents a shunt impedance to the transmission line, where the impedance is a function of dipole offset, length, and width. Similarly, the slot represents a series impedance to the transmission line. The FDTD method is applied to single dipole and slot elements transversely coupled to an infinite microstrip line using a fixed rectangular grid with Mur's second order absorbing boundary conditions. Frequency-dependent circuit and scattering parameters are obtained by saving desired time-domain quantities and using the Fourier transform. A Gaussian pulse excitation is applied to the microstrip transmission line, where the resulting reflected signal due to the presence of the radiating element is used to determine the equivalent element impedance.

Tulintseff, A. N.↗
DOE OSTI2023

Autonomous Intelligence Measurements and Sensor Systems (AIMS): Gaussian Processes in Remote Sensing: Literature Review

Power grid resilience and reliability is crucial to supporting critical infrastructure. Service interruptions can have devastating consequences to communication, emergency, and transportation services, just to name a few. In addition to infrastructure, power services are crucial to everyday life. Nearly every element of modern-day living relies on a stable and functioning power grid - heating and cooling systems to lighting, refrigeration, telecommunications, and internet access. Interruptions in the power supply can range from minor inconveniences, such as the temporary loss of internet or television services, to more significant problems, like the inability to access medical equipment or emergency services during critical situations. As climate conditions worsen, the reliability of the power grid becomes even more significant. Extreme weather events, such as hurricanes, wildfires, or severe storms, can cause serious damage to power infrastructure, leading to prolonged power outages. Rising temperatures and changing weather patterns can strain the grid, resulting in increased demand for electricity, stressing transmission and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A spatially regularized detector for emergent/re-emergent disease outbreaks

Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantum to classical parton evolution in the QGP

We study the time evolution of the density matrix of a high energy quark in the presence of a dense QCD background that is modeled as a stochastic Gaussian color field. At late times, we find that only the color singlet component of the quark’s reduced density matrix survives the in-medium evolution and that the density matrix becomes asymptotically diagonal in both transverse position and momentum spaces. In addition, we observe an accelerated entropy growth due to the larger phase space being explored by the quark and that the quantum and classical quark entropies converge at late times. We further observe that the quark state loses all memory of the initial condition. Combined with the fact that the reduced density matrix satisfies Boltzmann-diffusion transport, we conclude that the quark reduced density matrix can be interpreted as a classical phase space distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Consistency of cosmic shear analyses in harmonic and real space

ABSTRACT Recent cosmic shear studies have reported discrepancies of up to 1σ on the parameter ${S_{8}=\sigma _{8}\sqrt{{\Omega _{\rm m}}/0.3}}$ between the analysis of shear power spectra and two-point correlation functions, derived from the same shear catalogues. It is not a priori clear whether the measured discrepancies are consistent with statistical fluctuations. In this paper, we investigate this issue in the context of the forthcoming analyses from the third year data of the Dark Energy Survey (DES Y3). We analyse DES Y3 mock catalogues from Gaussian simulations with a fast and accurate importance sampling pipeline. We show that the methodology for determining matching scale cuts in harmonic and real space is the key factor that contributes to the scatter between constraints derived from the two statistics. We compare the published scales cuts of the KiDS, Subaru-HSC, and DES surveys, and find that the correlation coefficients of posterior means range from over 80 per cent for our proposed cuts, down to 10 per cent for cuts used in the literature. We then study the interaction between scale cuts and systematic uncertainties arising from multiple sources: non-linear power spectrum, baryonic feedback, intrinsic alignments, uncertainties in the point spread function, and redshift distributions. We find that, given DES Y3 characteristics and proposed cuts, these uncertainties affect the two statistics similarly; the differential biases are below a third of the statistical uncertainty, with the largest biases arising from intrinsic alignment and baryonic feedback. While this work is aimed at DES Y3, the tools developed can be applied to Stage-IV surveys where statistical errors will be much smaller.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1987

Effect of current on spectrum of breaking waves in water of finite depth

This paper presents an approximate method to compute the mean value, the mean square value and the spectrum of waves in water of finite depth taking into account the effect of wave breaking with or without the presence of current. It is assumed that there exists a linear and Gaussian ideal wave train whose spectrum is first obtained using the wave energy flux balance equation without considering wave breaking. The Miche wave breaking criterion for waves in finite water depth is used to limit the wave elevation and establish an expression for the breaking wave elevation in terms of the elevation and its second time derivative of the ideal waves. Simple expressions for the mean value, the mean square value and the spectrum are obtained. These results are applied to the case in which a deep water unidirectional wave train, propagating normally towards a straight shoreline over gently varying sea bottom of parallel and straight contours, encounters an adverse steady current whose velocity is assumed to be uniformly distributed with depth. Numerical results are obtained and presented in graphical form.

Tung, C. C.↗
NASA NTRS2012

The Temperature and Distribution of Organic Molecules in the Inner Regions of T Tauri Disks

"High-resolution NIR spectroscopic observations of warm molecular gas emission from young circumstellar disks allow us to constrain the temperature and composition of material in the inner planet-forming region. By combining advanced data reduction algorithms with accurate modeling of the terrestrial atmospheric spectrum and a novel double-differencing data analysis technique, we have achieved very high-contrast measurements (S/N approx. 500-1000) of molecular emission at 3 microns. In disks around low-mass stars, we have achieved the first detections of emission from HCN and C2H2 at near-infrared wavelengths from several bright T Tauri stars using the CRIRES spectrograph on the Very Large Telescope and NIRSPEC spectrograph on the Keck Telescope. We spectrally resolve the line shape, showing that the emission has both a Keplerian and non-Keplerian component as observed previously for CO emission. We used a simplified single-temperature local thermal equilibrium (LTE) slab model with a Gaussian line profile to make line identifications and determine a best-fit temperature and initial abundance ratios, and we then compared these values with constraints derived from a detailed disk radiative transfer model assuming LTE excitation but utilizing a realistic temperature and density structure. Abundance ratios from both sets of models are consistent with each other and consistent with expected values from theoretical chemical models, and analysis of the line shapes suggests that the molecular emission originates from within a narrow region in the inner disk (R < 1 AU)."

Mandell, Avi↗
NASA NTRS2016

Performance Metrics, Error Modeling, and Uncertainty Quantification

A common set of statistical metrics has been used to summarize the performance of models or measurements-­ the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying un­certainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model should be an alternative to the metrics-based approach. The error-modeling meth­odology is applicable to both linear and nonlinear errors, while the metrics are only meaningful for linear errors. In addition, the error model expresses the error structure more naturally, and directly quantifies uncertainty. This argument is further explained by highlighting the intrinsic connections between the performance metrics, the error model, and the joint distribution between the data and the reference.

Quantification↗
DOE OSTI2026

Determination of proton PDF uncertainties with Markov chain Monte Carlo

We present an analysis of parton distribution functions (PDFs) of the proton using Markov chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes’ theorem and, thus, provides a means to directly sample the underlying probability distribution—in this case, the probability distribution of the PDF parameters. This allows for a straightforward propagation of the resulting uncertainties into any PDF-dependent observable, preserving their simple probabilistic interpretation. In our analysis we include a broad set of deep inelastic scattering data from HERA, BCDMS and NMC experiments along with the Drell-Yan, 𝑊 and 𝑍 boson data from LHC and Tevatron experiments, which combined with theoretical calculations at next-to-next-to-leading order in QCD allow for realistic determination of PDFs. The main focus of this analysis is to explore alternative methods for PDF uncertainty estimation that are more firmly grounded in statistical principles. We show that the flexibility of the Bayes framework, allowing one, e.g., to account for non-Gaussianity or inconsistencies of datasets, is crucial to extract realistic uncertainties when such assumptions are not fulfilled. We also demonstrate that MCMC allows one to determine the Δ⁢𝜒 2 value corresponding to a given confidence level in the sample, which can, in turn, be used as a statistically well-founded tolerance criterion used in the Hessian method, thus addressing one of its main long-standing drawbacks.

Risse, Peter Clemens [Universität Münster (Germany↗
NASA NTRS2023

STXM-C-XANES, IR Microspectroscopy, and Raman Microspectroscopy Analysis of Organic Matter in the Chwichiya 002 C3.00-Ungrouped Chondrite

Chwichiya 002 is a C3.00-ungrouped carbonaceous chondrite discovered in Western Sahara in 2018. The presence of hydrated phases in Chwichiya 002 was not confirmed by XRD analysis, and only a faint water and Si-O band attributed to phyllosilicate were observed by infrared transmission spectroscopy, indicating a very low degree of aqueous alteration (Meteoritical Bulletin, 2020). The structural order of polycyclic aromatic hydrocarbons evaluated by Raman spectroscopy indicates that the effect of thermal metamorphism is less severe than in the Semarkona meteorite, an LL3.00 ordinary chondrite (Meteoritical Bulletin, 2020). Chiwichiya 002 is a very primitive carbonaceous chondrite and an important information source for the early stage of the evolution of organic matter in the solar system. In this study, we investigated the molecular structure and distribution of organic matter in Chiwichiya 002 by Carbon X-ray Absorption Near Edge Structure (C-XANES) analysis using a Scanning Transmission X-ray Microscope (STXM). The STXM-C-XANES analysis has a spatial resolution of 20~100 nm and can observe the spatial distribution of organic materials in microstructures. In addition, to investigate the degree of thermal metamorphism of the organic matter, micro-IR, and micro-Raman spectroscopic analyses were performed. In addition, the Murchison CM2 chondrite was also analyzed for comparison. To evaluate contamination during analytical procedures, antigorite, which was heated in air at 500°C for 5 hours to eliminate organic matter was also analyzed in the same manner. The samples were milled into 100 nm-thick, thin sections using a Focused Ion Beam (FIB), and C-XANES spectra were obtained using the STXM at the High Energy Accelerator Research Organization, Photon Factory (PF), BL-19B. The obtained C-XANES spectra were smoothed and then fitted with Gaussian functions using the method of Le Guillou et al. (2018). The C-XANES showed that the ratio of oxygenated functional group (ketone, phenolic) carbons in Chwichiya 002 was lower than in Murchison, whereas the ratio of aliphatic carbons was almost the same. This may reflect the low degree of aqueous alteration of Chwichiya 002. In addition, contamination peaks were detected at 287.3-287.7 eV and 288.3-288.7 eV in the C-XANES spectra of some of the antigorite samples. The source of these peaks is under investigation. Raman spectroscopic analysis showed that the intensity ratio of the D band to the G band of Chwichiya 002 was slightly higher than that of Murchison. IR absorption spectra showed aromatic absorption at 1630 cm -1 , but there was little absorption due to aliphatic C-H around 2900 cm -1 . These results suggest that the organic matter in Chwichiya 002 may be more heated than in Murchison.

Chwichiya 002↗
NASA NTRS1992

Forster energy transfer in chlorosomes of green photosynthetic bacteria

Energy transfer properties of whole cells and chlorosome antenna complexes isolated from the green sulfur bacteria Chlorobium limicola (containing bacteriochlorophyll c), Chlorobium vibrioforme (containing bacteriochlorophyll d) and Pelodictyon phaeoclathratiforme (containing bacteriochlorophyll e) were measured. The spectral overlap of the major chlorosome pigment (bacteriochlorophyll c, d or, e) with the bacteriochlorophyll a B795 chlorosome baseplate pigment is greatest for bacteriochlorophyll c and smallest for bacteriochlorophyll e. The absorbance and fluorescence spectra of isolated chlorosomes were measured, fitted to gaussian curves and the overlap factors with B795 calculated. Energy transfer times from the bacteriochlorophyll c, d or e to B795 were measured in whole cells and the results interpreted in terms of the Forster theory of energy transfer.

NASA Program Exobiology↗
DOE OSTI2024

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Globally optimal interferometry with lossy twin Fock probes

Parity or quadratic spin (e.g., J z 2 ) readouts of a Mach–Zehnder (MZ) interferometer probed with a twin Fock (TF) input state allow saturating the optimal sensitivity attainable among all mode-separable states with a fixed total number of particles but only when the interferometer phase θ is near zero. When more general Dicke state probes are used, the parity readout saturates the quantum Fisher information (QFI) at θ = 0, whereas better-than-standard quantum limit performance of the J z 2 readout is restricted to an o ( N ) occupation imbalance. We show that a method of moments readout of two quadratic spin observables J z 2 and J + 2 + J − 2 is globally optimal for Dicke state probes; i.e., the error saturates the QFI for all θ . In the lossy setting, we derive the time-inhomogeneous Markov process describing the effect of particle loss on TF states, showing that the method of moments readout of four at-most-quadratic spin observables is sufficient for globally optimal estimation of θ when two or more particles are lost. The analysis culminates in a numerical calculation of the QFI matrix for distributed MZ interferometry on the four-mode state | N 4 , N 4 , N 4 , N 4 〉 and its lossy counterparts, showing that an advantage for the estimation of any linear function of the local MZ phases θ 1 and θ 2 (compared to independent probing of the MZ phases by two copies of | N 4 , N 4 〉 ) appears when more than one particle is lost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗