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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.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation

Strong gravitational lensing contains key information about cosmic acceleration. Modern and next-generation galaxy imaging surveys are expected to provide high-quality data on $\mathcal{O}(10^5)$ galaxy-galaxy lensing systems. The plethora and complexity of the data are likely to present computational challenges for parameter inference methods for fitting high-dimensional likelihoods, which are often analytically intractable. Neural Ratio Estimation (NRE) efficiently computes individual likelihood ratios that can be combined into population-level posteriors. We use simulations to study the capacity of NRE to jointly predict the dark energy equation-of-state parameter $w$ and the total matter density $Ω_{m}$ from lensing images and companion spectroscopic information. We also introduce a post hoc posterior coverage calibration procedure that mitigates the model overconfidence that is typically found in neural density estimation applications. Our experiments show that the errors on both parameters decrease with increasing inference population sizes. In particular, for 100 lenses in a standard $Λ$CDM Universe, our calibrated NRE model achieves median fractional uncertainty of $22.8\%$ in $w$ and $2.9\%$ in $Ω_{m}$. This proof of concept demonstrates a potentially scalable approach for efficient cosmological parameter inference with large populations of galaxy-scale lenses observed in future surveys.

Jarugula, Sreevani [Fermilab] (ORCID:0000000253867↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2025

Measurement of off-shell Higgs boson production in the $H^*\rightarrow ZZ\rightarrow 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector

A measurement of off-shell Higgs boson production in the $H^*\to ZZ\to 4\ell$ decay channel is presented. The measurement uses 140 fb −1 of proton–proton collisions at $\sqrt{s} = 13$ TeV collected by the ATLAS detector at the Large Hadron Collider and supersedes the previous result in this decay channel using the same dataset. The data analysis is performed using a neural simulation-based inference method, which builds per-event likelihood ratios using neural networks. The observed (expected) off-shell Higgs boson production signal strength in the $ZZ\to 4\ell$ decay channel at 68% CL is $0.87^{+0.75}_{-0.54}$ ($1.00^{+1.04}_{-0.95}$ ). The evidence for off-shell Higgs boson production using the $ZZ\to 4\ell$ decay channel has an observed (expected) significance of 2.5σ (1.3σ). The expected result represents a significant improvement relative to that of the previous analysis of the same dataset, which obtained an expected significance of 0.5σ. When combined with the most recent ATLAS measurement in the $ZZ\to 2\ell 2\nu$ decay channel, the evidence for off-shell Higgs boson production has an observed (expected) significance of 3.7σ (2.4σ). The off-shell measurements are combined with the measurement of on-shell Higgs boson production to obtain constraints on the Higgs boson total width. The observed (expected) value of the Higgs boson width at 68% CL is $4.3^{+2.7}_{-1.9}$ ($4.1^{+3.5}_{-3.4}$ ) MeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗
DOE OSTI2024

Estimating Sparse Direct Effects in Multivariate Regression With the Spike-and-Slab LASSO

The multivariate regression interpretation of the Gaussian chain graph model simultaneously parametrizes (i) the direct effects of p predictors on q outcomes and (ii) the residual partial covariances between pairs of outcomes. We introduce a new method for fitting sparse versions of these models with spike-and-slab LASSO (SSL) priors. We develop an Expectation Conditional Maximization algorithm to obtain sparse estimates of the p × q matrix of direct effects and the q × q residual precision matrix. Our algorithm iteratively solves a sequence of penalized maximum likelihood problems with self-adaptive penalties that gradually filter out negligible regression coefficients and partial covariances. Because it adaptively penalizes individual model parameters, our method is seen to outperform fixed-penalty competitors on simulated data. We establish the posterior contraction rate for our model, buttressing our method’s excellent empirical performance with strong theoretical guarantees. Using our method, we estimated the direct effects of diet and residence type on the composition of the gut microbiome of elderly adults.

EM algorithm↗
DOE OSTI2026

An Automated Probabilistic Asteroid Prediscovery Pipeline

We present an automated and probabilistic method to make prediscovery detections of near-Earth asteroids (NEAs) in archival survey images, with the goal of reducing orbital uncertainty immediately after discovery. We refit the Minor Planet Center's astrometry and propagate the full six-parameter covariance to survey epochs to define search regions. We build low-threshold source catalogs for viable images and evaluate every detected source in a search region as a candidate prediscovery. We eliminate false positives by refitting a new orbit to each candidate and probabilistically linking detections across images using a likelihood ratio. Applied to the Zwicky Transient Facility's (ZTF) imaging, we identify approximately 3000 recently discovered NEAs with prediscovery potential, including a doubling of the observational arc for about 500. We use archival ZTF imaging to make prediscovery detections of the potentially hazardous asteroid 2021 DG1, extending its arc by 2.5 yr and reducing future apparition sky plane uncertainty from many degrees to arcseconds. We also recover 2025 FU24 nearly 7 yr before its first known observation, when its sky plane uncertainty covers hundreds of square degrees across thousands of ZTF images. The method is survey agnostic and scalable, enabling rapid orbit refinement for new discoveries from Rubin, NEO Surveyor, and NEOMIR.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

BICEP/Keck. XX. Component-separated Maps of the Polarized Cosmic Microwave Background and Thermal Dust Emission Using Planck and BICEP/Keck Observations through the 2018 Observing Season

We present component-separated polarization maps of the cosmic microwave background (CMB) and Galactic thermal dust emission, derived using data from the BICEP/Keck experiments through the 2018 observing season and Planck. By employing a maximum-likelihood method that utilizes observing matrices, we produce unbiased maps of the CMB and dust signals. We outline the computational challenges and demonstrate an efficient implementation of the component map estimator. We show methods to compute and characterize power spectra of these maps, opening up an alternative way to infer the tensor-to-scalar ratio from our data. We compare the results of this map-based separation method with the baseline BICEP/Keck analysis. Our analysis demonstrates consistency between the two methods, finding an 84% correlation between the pipelines.

cosmic inflation↗
NASA NTRS2002

A statistical model for interpreting computerized dynamic posturography data

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

NASA Discipline Neuroscience↗
NASA NTRS2000

Prediction of thrombus-related mechanical prosthetic valve dysfunction using transesophageal echocardiography

Identification of thrombus-related mechanical prosthetic valve dysfunction (MPVD) has important therapeutic implications. We sought to develop an algorithm, combining clinical and echocardiographic parameters, for prediction of thrombus-related MPVD in a series of 53 patients (24 men, age 52 +/- 16 years) who had intraoperative diagnosis of thrombus or pannus from 1992 to 1997. Clinical and echocardiographic parameters were analyzed to identify predictors of thrombus and pannus. Prevalence of thrombus and diagnostic yields relative to the number of predictors were determined. There were 22 patients with thrombus, 19 patients with pannus, and 12 patients with both. Forty-two of 53 masses were visualized using transesophageal echocardiography (TEE), including 29 of 34 thrombi or both thrombi and panni and 13 of 19 isolated panni. Predictors of thrombus or mixed presentation include mobile mass (p = 0.009), attachment to occluder (p = 0.02), elevated gradients (p = 0.04), and an international normalized ratio of < or = 2.5 (p = 0.03). All 34 patients with thrombus or mixed presentation had > or = 1 predictor. The prevalence of thrombus in the presence of < or = 1, 2, and > or = 3 predictors is 14%, 69%, and 91%, respectively. Thus, TEE is sensitive in the identification of abnormal mass in the setting of MPVD. An algorithm based on clinical and transesophageal echocardiographic predictors may be useful to estimate the likelihood of thrombus in the setting of MPVD. In the presence of > or = 3 predictors, the probability of thrombus is high.

NASA Discipline Cardiopulmonary↗
NASA NTRS1997

Modeling the effects of exercise during 100% oxygen prebreathe on the risk of hypobaric decompression sickness

BACKGROUND: Several previous studies indicated that exercise during prebreathe with 100% O2 decreased the incidence of hypobaric decompression sickness (DCS). We report a meta-analysis of these investigations combined with a new study in our laboratory to develop a statistical model as a predictive tool for DCS. HYPOTHESIS: Exercise during prebreathe increases N2 elimination in a theoretical 360-min half-time compartment decreasing the incidence of DCS. METHODS: A dose-response probability tissue ratio (TR) model with 95% confidence limits was created for two groups, prebreathe with exercise (n = 113) and resting prebreathe (n = 113), using nonlinear regression analysis with maximum likelihood optimization. RESULTS: The model predicted that prebreathe exercise would reduce the residual N2 in a 360-min half-time compartment to a level analogous to that in a 180-min compartment. This finding supported the hypothesis. The incidence of DCS for the exercise prebreathe group was significantly decreased (Chi-Square = 17.1, p < 0.0001) from the resting prebreathe group. CONCLUSIONS: The results suggested that exercise during prebreathe increases tissue perfusion and N2 elimination approximately 2-fold and markedly lowers the risk of DCS. Based on the model, the prebreathe duration may be reduced from 240 min to a predicted 91 min for the protocol in our study, but this remains to be verified. The model provides a useful planning tool to develop and test appropriate prebreathe exercise protocols and to predict DCS risks for astronauts.

NASA Program Environmental Health↗
NASA NTRS1996

Two-stage model of radon-induced malignant lung tumors in rats: effects of cell killing

A two-stage stochastic model of carcinogenesis is used to analyze lung tumor incidence in 3750 rats exposed to varying regimens of radon carried on a constant-concentration uranium ore dust aerosol. New to this analysis is the parameterization of the model such that cell killing by the alpha particles could be included. The model contains parameters characterizing the rate of the first mutation, the net proliferation rate of initiated cells, the ratio of the rates of cell loss (cell killing plus differentiation) and cell division, and the lag time between the appearance of the first malignant cell and the tumor. Data analysis was by standard maximum likelihood estimation techniques. Results indicate that the rate of the first mutation is dependent on radon and consistent with in vitro rates measured experimentally, and that the rate of the second mutation is not dependent on radon. An initial sharp rise in the net proliferation rate of initiated cell was found with increasing exposure rate (denoted model I), which leads to an unrealistically high cell-killing coefficient. A second model (model II) was studied, in which the initial rise was attributed to promotion via a step function, implying that it is due not to radon but to the uranium ore dust. This model resulted in values for the cell-killing coefficient consistent with those found for in vitro cells. An "inverse dose-rate" effect is seen, i.e. an increase in the lifetime probability of tumor with a decrease in exposure rate. This is attributed in large part to promotion of intermediate lesions. Since model II is preferable on biological grounds (it yields a plausible cell-killing coefficient), such as uranium ore dust. This analysis presents evidence that a two-stage model describes the data adequately and generates hypotheses regarding the mechanism of radon-induced carcinogenesis.

NASA Program Radiation Health↗
NASA NTRS2005

Origin of the Eumetazoa: testing ecological predictions of molecular clocks against the Proterozoic fossil record

Molecular clocks have the potential to shed light on the timing of early metazoan divergences, but differing algorithms and calibration points yield conspicuously discordant results. We argue here that competing molecular clock hypotheses should be testable in the fossil record, on the principle that fundamentally new grades of animal organization will have ecosystem-wide impacts. Using a set of seven nuclear-encoded protein sequences, we demonstrate the paraphyly of Porifera and calculate sponge/eumetazoan and cnidarian/bilaterian divergence times by using both distance [minimum evolution (ME)] and maximum likelihood (ML) molecular clocks; ME brackets the appearance of Eumetazoa between 634 and 604 Ma, whereas ML suggests it was between 867 and 748 Ma. Significantly, the ME, but not the ML, estimate is coincident with a major regime change in the Proterozoic acritarch record, including: (i) disappearance of low-diversity, evolutionarily static, pre-Ediacaran acanthomorphs; (ii) radiation of the high-diversity, short-lived Doushantuo-Pertatataka microbiota; and (iii) an order-of-magnitude increase in evolutionary turnover rate. We interpret this turnover as a consequence of the novel ecological challenges accompanying the evolution of the eumetazoan nervous system and gut. Thus, the more readily preserved microfossil record provides positive evidence for the absence of pre-Ediacaran eumetazoans and strongly supports the veracity, and therefore more general application, of the ME molecular clock.

NASA Discipline Evolutionary Biology↗
NASA NTRS2008

The Limits of Coding with Joint Constraints on Detected and Undetected Error Rates

We develop a remarkably tight upper bound on the performance of a parameterized family of bounded angle maximum-likelihood (BA-ML) incomplete decoders. The new bound for this class of incomplete decoders is calculated from the code's weight enumerator, and is an extension of Poltyrev-type bounds developed for complete ML decoders. This bound can also be applied to bound the average performance of random code ensembles in terms of an ensemble average weight enumerator. We also formulate conditions defining a parameterized family of optimal incomplete decoders, defined to minimize both the total codeword error probability and the undetected error probability for any fixed capability of the decoder to detect errors. We illustrate the gap between optimal and BA-ML incomplete decoding via simulation of a small code.

Undetected Error Rate↗
NASA NTRSDate not supplied

Mapping Mitigation Risk Consequence for Crew Needing Assistance or Rescue on the Lunar Surface

Extravehicular activity (EVA) on the lunar surface presents unique risks to crew with possibility for injury. Without appropriate assistance or rescue capability, inability to nominally ambulate and return to a lander, especially during early Artemis missions, could have catastrophic consequences. Mapping likelihood and consequence safety risk associated with identified injury scenarios establishes a baseline from which to assess potential mitigation solutions to ensure crew health and safety.

lunar surface↗
NASA NTRS2023

A Confidence-Based Approach to Including Survivors in a Probabilistic TID Failure Assessment

A probabilistic total ionizing dose (TID) failure assessment is extended to include survivor data, enabling the bounding of failure probability to a desired confidence level (CL) without failure data. The extension provides an avenue for analyzing microelectronics tested for TID without reaching a failure mode, a scenario often encountered by missions utilizing commercial-off-the-shelf (COTS) technologies. Using the type-I censored likelihood formulation and a realistic upper bound on expected device performance, the failure probability space is bounded by confidence contours within the context of a variable environment. The framework accommodates any type of distribution assumed for the part failure or the environment under consideration. Furthermore, the framework can be utilized pre-emptively to plan future device TID tests, minimizing costs while meeting survival requirements. Heritage data may also be used as survivors to further minimize testing costs when parts are from the same lot, but the amount of constraint derived from heritage is limited. Altogether, the framework enables a formal, mathematically rigorous analysis of radiation tolerant devices tested to a maximum dose, as well as flight heritage, in a hardness assurance methodology.

confidence↗
DOE OSTI2024

First Principles Study of Aluminum Doped Polycrystalline Silicon as a Potential Anode Candidate in Li‐ion Batteries

Addressing sustainable energy storage remains crucial for transitioning to renewable sources. While Li‐ion batteries have made significant contributions, enhancing their capacity through alternative materials remains a key challenge. Micro‐sized silicon is a promising anode material due to its tenfold higher theoretical capacity compared to conventional graphite. However, its substantial volumetric expansion during cycling impedes practical application due to mechanical failure and rapid capacity fading. A novel approach is proposed to mitigate this issue by incorporating trace amounts of aluminum into the micro‐sized silicon electrode using ball milling. Density functional theory (DFT) is employed to establish a theoretical framework elucidating how grain boundary sliding, a key mechanism involved in preventing mechanical failure is facilitated by the presence of trace aluminum at grain boundaries. This, in turn, reduces stress accumulation within the material, reducing the likelihood of failure. To validate the theoretical predictions, capacity retention experiments are conducted on undoped and Al‐doped micro‐sized silicon samples. In conclusion, the results demonstrate significantly reduced capacity fading in the doped sample, corroborating the theoretical framework and showcasing the potential of aluminum doping for improved Li‐ion battery performance.

25 ENERGY STORAGE↗
DOE OSTI2025

Mitigating Battery Cell Failure: Role of Ag‐Nanoparticle Fillers in Solid Electrolyte Dendrite Suppression

Abstract The development of solid‐state batteries (SSBs) with lithium Li metal anodes holds significant promise for enhancing the energy density and safety of next‐generation energy storage systems. However, their commercialization is hindered by challenges related to Li dendrite formation, which can lead to short circuits and battery failure. In this study, the role of Ag nanoparticles embedded within solid electrolytes (SE) is investigated in suppressing dendrite propagation. The results demonstrate that Ag nanoparticles effectively mitigate two key failure mechanisms: (1) dendrite growth within porous networks at low current densities and (2) stress intensification‐induced SE fracture at higher current densities (12 mA cm −2 ). Ex situ characterization using focused‐ion beam – scanning electron microscopy (FIB–SEM) and energy dispersive X‐ray spectroscopy (EDS,) reveals that Ag nanoparticles migrate alongside advancing Li dendrites, promoting homogeneous dendrite growth and reducing the likelihood of localized stress concentrations. Additionally, the incorporation of Ag nanoparticles is shown to facilitate a more uniform Li distribution toward the anode side, which can potentially enable the use of higher charging rates in SSBs. This study provides a new perspective on Li dendrite suppression and presents new opportunities for enhancing the performance and safety of SSBs.

Diallo, Mouhamad Said↗
DOE OSTI2024

Probabilistic Discrete‐Time Models for Spreading Processes in Complex Networks: A Review

Abstract Research into network dynamics of spreading processes typically employs both discrete and continuous time methodologies. Although each approach offers distinct insights, integrating them can be challenging, particularly when maintaining coherence across different time scales. This review focuses on the Microscopic Markov Chain Approach (MMCA), a probabilistic f ramework originally designed for epidemic modeling. MMCA uses discrete dynamics to compute the probabilities of individuals transitioning between epidemiological states. By treating each time step—usually a day—as a discrete event, the approach captures multiple concurrent changes within this time frame. The approach allows to estimate the likelihood of individuals or populations being in specific states, which correspond to distinct epidemiological compartments. This review synthesizes key findings from the application of this approach, providing a comprehensive overview of its utility in understanding epidemic spread.

Granell, Clara↗