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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers

Abstract Detection of gravitational waves (GWs) from neutron star-black hole (NSBH) standard sirens provides local measurements of the Hubble constant (H 0 ), regardless of the detection of an electromagnetic (EM) counterpart, given that matter effects can be exploited to break the redshift degeneracy of the GW waveforms. The distinctive merger morphology and the high-redshift detectability of tidally disrupted NSBH make them promising candidates for this method. Also, the detection prospects of an EM counterpart for these systems will be limited toz< 0.8 in the optical, in the era of future GW detectors. Using recent constraints on the equation of state of NSs from multi-messenger observations of NICER and LIGO/Virgo/KAGRA, we show the prospects of measuringH 0 solely from GW observation of NSBH systems, achievable by the Einstein telescope (ET) and Cosmic Explorer (CE) detectors. We first analyze individual events to quantify the effect of high-frequency (≥500 Hz) tidal distortions on the inference of NS tidal deformability parameter (Λ) and hence onH 0 . We find that disruptive mergers can constrain Λ up to  ( 60 % ) more precisely than nondisruptive ones. However, this precision is not sufficient to place stringent constraints on theH 0 from individual events. By performing Bayesian analysis on simulated NSBH data (up toN= 100 events, corresponding to a day of observation) in the ET+CE detectors, we find that NSBH systems enable unbiased 4%–13% precision on the estimate ofH 0 (68% credible interval). This is a similar measurement precision found in studies analyzing NSBH mergers with EM counterparts in the LVKC O5 era.

Astronomy & Astrophysics↗
DOE OSTI2025

Multimodality in the Search for New Physics in Pulsar Timing Data and the Case of Kination-amplified Gravitational-wave Background from Inflation

We investigate the kination-amplified inflationary gravitational-wave background (GWB) interpretation of the signal recently reported by various pulsar timing array (PTA) experiments. Kination is a post-inflationary phase in the expansion history dominated by the kinetic energy of some scalar field, characterized by a stiff equation of state w = 1. Within the inflationary GWB model, we identify two modes that can fit the current data sets (NANOGrav and EPTA) with equal likelihood: the kination-amplification (KA) mode and the ordinary, no-kination-amplification (no-KA) mode. The multimodality of the likelihood motivates a Bayesian analysis with nested sampling. We analyze the free spectra of current PTA data and mock free spectra constructed with higher signal-to-noise ratios using nested sampling. The analysis of the mock spectrum designed to be consistent with the best fit to the NANOGrav 15 yr (NG15) data successfully reveals the expected bimodal posterior for the first time while excluding the reheating mode that appears in the fit to the current NG15 data, making a case for our correct and comprehensive treatment of potential multimodal posteriors arising from future PTA data sets. The resultant Bayes factor is $\mathcal{B}$ $\equiv$ Z no–KA /Z KA = 2.9 ± 1.9, indicating comparable statistical significance between the two modes. Given the theoretical model-building challenges of producing highly blue-tilted primordial tensor spectra, the KA mode has the advantage of requiring less blue primordial spectra, compared with the no-KA mode. The synergy between future cosmic microwave background polarization, pulsar timing, and laser interferometer measurements of gravitational waves will help resolve the ambiguity implied by the multimodal posterior in PTA-only searches.

Cosmology↗
DOE OSTI2025

DESI Spectroscopy of HETDEX Emission-line Candidates. I. Line Discrimination Validation

The Hobby–Eberly Dark Energy Experiment (HETDEX) is an untargeted spectroscopic galaxy survey that uses Lyα-emitting galaxies (LAEs) as tracers of 1.9 < z < 3.5 large-scale structure. Most detections consist of a single emission line, whose identity is inferred via a Bayesian analysis of ancillary data. To determine the accuracy of these line identifications, HETDEX detections were observed with the Dark Energy Spectroscopic Instrument (DESI). In two DESI pointings, high-confidence spectroscopic redshifts are obtained for 1157 sources, including 982 LAEs. The DESI spectra are used to evaluate the accuracy of the HETDEX object classifications and tune the methodology to achieve the HETDEX science requirement of ≲2% contamination of the LAE sample by low-redshift emission-line galaxies, while still assigning 96% of the true Lyα emission sample with the correct spectroscopic redshift. We compare emission-line measurements between the two experiments assuming a simple Gaussian line fitting model. Fitted values for the central wavelength of the emission line, the measured line flux, and line widths are consistent between the surveys within uncertainties. Derived spectroscopic redshifts, from the two classification pipelines, when both agree as an LAE classification, are consistent to within $\langle$Δz/(1 + z)$\rangle$ = 6.9 × 10 −5 with an rms scatter of 3.3 × 10 −4 . Data are available at https://data.desi.lbl.gov/desi/public/dr1/vac/dr1/hetdex.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Autodifferentiable Spectrum Model for High-dispersion Characterization of Exoplanets and Brown Dwarfs

We present an autodifferentiable spectral modeling of exoplanets and brown dwarfs. This model enables a fully Bayesian inference of the high-dispersion data to fit the ab initio line-by-line spectral computation to the observed spectrum by combining it with the Hamiltonian Monte Carlo in recent probabilistic programming languages. An open-source code, ExoJAX, developed in this study, was written in Python using the GPU/TPU compatible package for automatic differentiation and accelerated linear algebra, JAX. We validated the model by comparing it with existing opacity calculators and a radiative transfer code and found reasonable agreements for the output. As a demonstration, we analyzed the high-dispersion spectrum of a nearby brown dwarf, Luhman 16 A, and found that a model including water, carbon monoxide, and H 2 /He collision-induced absorption was well fitted to the observed spectrum (R = 10 5 and 2.28-2.30 μm). As a result, we found that T0 = 1295$^{-32}_{+35}$ K at 1 bar and C/O = 0.62 ± 0.03, which is slightly higher than the solar value. This work demonstrates the potential of a full Bayesian analysis of brown dwarfs and exoplanets as observed by high-dispersion spectrographs and also directly imaged exoplanets as observed by high-dispersion coronagraphy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

A new M dwarf debris disk candidate in a young moving group discovered with disk detective

We used the Disk Detective citizen science project and the BANYAN II Bayesian analysis tool to identify a new candidate member of a nearby young association with infrared excess. WISE J080822.18-644357.3, an M5.5-type debris disk system with significant excess at both 12 and 22 μm, is a likely member (∼90% BANYAN II probability) of the ∼45 Myr old Carina association. Since this would be the oldest M dwarf debris disk detected in a moving group, this discovery could be an important constraint on our understanding of M dwarf debris disk evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Thermal Effects in Binary Neutron Star Mergers

Abstract We study the impact of finite-temperature effects in numerical-relativity simulations of binary neutron star mergers with microphysical equations of state and neutrino transport in which we vary the effective nucleon masses in a controlled way. We find that, as the specific heat is increased, the merger remnants become colder and more compact due to the reduced thermal pressure support. Using a full Bayesian analysis, we demonstrate that this effect will be measurable in the postmerger gravitational wave signal with next-generation observatories at signal-to-noise ratios of 15.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Enhanced signal of momentum broadening in hard splittings for $γ$-tagged jets in a multistage approach

We investigate medium-induced modifications to jet substructure observables that characterize hard splitting patterns in central Pb-Pb collisions at the top energy of the Large Hadron Collider (LHC). Using a multistage Monte Carlo simulation of in-medium jet shower evolution, we explore flavor-dependent medium effects through simulations of inclusive and $γ$-tagged jets. The results show that quark jets undergo a non-monotonic modification compared to gluon jets in observables such as the Pb-Pb to $p$-$p$ ratio of the Soft Drop prong angle $r_g$, the relative prong transverse momentum $k_{T,g}$ and the groomed mass $m_g$ distributions. Due to this non-monotonic modification, $γ$-tagged jets, enriched in quark jets, provide surprisingly clear signals of medium-induced structural modifications, distinct from effects dominated by selection bias. This work highlights the potential of hard substructures in $γ$-tagged jets as powerful tools for probing the jet-medium interactions in high-energy heavy-ion collisions. All simulations for $γ$-tagged jet analyses carried out in this paper used triggered events containing at least one hard photon, which highlights the utility of these observables for future Bayesian analysis.

FOS: Physical sciences↗
DOE OSTI2022

Bayesian Inference of the Fluctuating Proton Shape in DIS and Hadronic Collisions

We determine the likelihood distribution for the model parameters describing the event-by-event fluctuating proton geometry at small $x$ by performing a Bayesian analysis within the Color Glass Condensate framework. The exclusive J/ψ production data from HERA is found to constrain the model parameters well, and we demonstrate that complementary constraints can be obtained from simulations of Pb+Pb collisions at the LHC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

A Bayesian Meta-Analysis of the Effect of Alcohol Use on HCV-Treatment Outcomes with a Comparison of Resampling Methods to Assess Uncertainty in Parameter Estimates.

There is mounting evidence that alcohol use is significantly linked to lower HCV treatment response rates in interferon-based therapies, though some of the evidence is conflicting. Furthermore, although health care providers recommend reducing or abstaining from alcohol use prior to treatment, many patients do not succeed in doing so. The goal of this meta-analysis was to systematically review and summarize the Englishlanguage literature up through January 30, 2015 regarding the relationship between alcohol use and HCV treatment outcomes, among patients who were not required to abstain from alcohol use in order to receive treatment. Seven pertinent articles studying 1,751 HCV-infected patients were identified. Log-ORs of HCV treatment response for heavy alcohol use and light alcohol use were calculated and compared. We employed a hierarchical Bayesian meta-analytic model to accommodate the small sample size. The summary estimate for the log-OR of HCV treatment response was -0.775 with a 95% credible interval of (-1.397, -0.236). The results of the Bayesian meta-analysis are slightly more conservative compared to those obtained from a boot-strapped, random effects model. We found evidence of heterogeneity (Q = 14.489, p = 0.025), accounting for 60.28% of the variation among log-ORs. Meta-regression to capture the sources of this heterogeneity did not identify any of the covariates investigated as significant. This meta-analysis confirms that heavy alcohol use is associated with decreased HCV treatment response compared to lighter levels of alcohol use. Further research is required to characterize the mechanism by which alcohol use affects HCV treatment response.

alcohol↗
DOE OSTI2014

Developing a Comprehensive Risk Assessment Framework for Geological Storage CO 2

The operational risks for CCS projects include: risks of capturing, compressing, transporting and injecting CO₂; risks of well blowouts; risk that CO 2 will leak into shallow aquifers and contaminate potable water; and risk that sequestered CO 2 will leak into the atmosphere. This report examines these risks by using information on the risks associated with analogue activities such as CO 2 based enhanced oil recovery (CO 2 -EOR), natural gas storage and acid gas disposal. We have developed a new analysis of pipeline risk based on Bayesian statistical analysis. Bayesian theory probabilities may describe states of partial knowledge, even perhaps those related to non-repeatable events. The Bayesian approach enables both utilizing existing data and at the same time having the capability to adsorb new information thus to lower uncertainty in our understanding of complex systems. Incident rates for both natural gas and CO 2 pipelines have been widely used in papers and reports on risk of CO 2 pipelines as proxies for the individual risk created by such pipelines. Published risk studies of CO 2 pipelines suggest that the individual risk associated with CO2 pipelines is between 10 -3 and 10 -4 , which reflects risk levels approaching those of mountain climbing, which many would find unacceptably high. This report concludes, based on a careful analysis of natural gas pipeline failures, suggests that the individual risk of CO 2 pipelines is likely in the range of 10-6 to 10-7, a risk range considered in the acceptable to negligible range in most countries. If, as is commonly thought, pipelines represent the highest risk component of CCS outside of the capture plant, then this conclusion suggests that most (if not all) previous quantitative- risk assessments of components of CCS may be orders of magnitude to high. The potential lethality of unexpected CO 2 releases from pipelines or wells are arguably the highest risk aspects of CO 2 enhanced oil recovery (CO2-EOR), carbon capture, and storage (CCS). Assertions in the CCS literature, that CO 2 levels of 10% for ten minutes, or 20 to 30% for a few minutes are lethal to humans, are not supported by the available evidence. The results of published experiments with animals exposed to CO 2 , from mice to monkeys, at both normal and depleted oxygen levels, suggest that lethal levels of CO 2 toxicity are in the range 50 to 60%. These experiments demonstrate that CO 2 does not kill by asphyxia, but rather is toxic at high concentrations. It is concluded that quantitative risk assessments of CCS have overestimated the risk of fatalities by using values of lethality a factor two to six lower than the values estimated in this paper. In many dispersion models of CO 2 releases from pipelines, no fatalities would be predicted if appropriate levels of lethality for CO 2 had been used in the analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

On the Methodology to Calculate the Covariance of Estimated Resonance Parameters

Principles to determine resonance parameters and their covariance from experimental data are discussed. Different methods to propagate the covariance of experimental parameters are compared. A full Bayesian statistical analysis reveals that the level to which the initial uncertainty of the experimental parameters propagates, strongly depends on the experimental conditions. For high precision data the initial uncertainties of experimental parameters, like a normalization factor, has almost no impact on the covariance of the parameters in case of thick sample measurements and conventional uncertainty propagation or full Bayesian analysis. The covariances derived from a full Bayesian analysis and least-squares fit are derived under the condition that the model describing the experimental observables is perfect. When the quality of the model can not be verified a more conservative method based on a renormalization of the covariance matrix is recommended to propagate fully the uncertainty of experimental systematic effects. Finally, neutron resonance transmission analysis is proposed as an accurate method to validate evaluated data libraries in the resolved resonance region.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Statistical Significance Testing for Mixed Priors: A Combined Bayesian and Frequentist Analysis

In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates Occam’s razor via the multiplicity or trials factor in the look-elsewhere effect. However, if the prior is not known completely, the frequentist hypothesis test via the false-positive rate is a better approach, as it is less sensitive to the prior choice. We argue that when only partial prior information is available, it is best to combine the two methodologies by using the Bayes factor as a test statistic in the frequentist analysis. We show that the standard frequentist maximum likelihood-ratio test statistic corresponds to the Bayes factor with a non-informative Jeffrey’s prior. We also show that mixed priors increase the statistical power in frequentist analyses over the maximum likelihood test statistic. We develop an analytic formalism that does not require expensive simulations and generalize Wilks’ theorem beyond its usual regime of validity. In specific limits, the formalism reproduces existing expressions, such as the p-value of linear models and periodograms. We apply the formalism to an example of exoplanet transits, where multiplicity can be more than 10 7 . We show that our analytic expressions reproduce the $p$-values derived from numerical simulations. We offer an interpretation of our formalism based on the statistical mechanics. We introduce the counting of states in a continuous parameter space using the uncertainty volume as the quantum of the state. We show that both the $p$-value and Bayes factor can be expressed as an energy versus entropy competition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Improved estimates of the Milky Way’s stellar mass and star formation rate from hierarchical Bayesian meta-analysis

We present improved estimates of several global properties of the Milky Way, including its current star formation rate (SFR), the stellar mass contained in its disk and bulge+bar components, as well as its total stellar mass. We do so by combining previous measurements from the literature using a hierarchical Bayesian (HB) statistical method that allows us to account for the possibility that any value may be incorrect or have underestimated errors. We show that this method is robust to a wide variety of assumptions about the nature of problems in individual measurements or error estimates. Ultimately, our analysis yields an SFR for the Galaxy of M-dot {sub ⋆}=1.65±0.19 M{sub ⊙} yr{sup −1}, assuming a Kroupa initial mass function (IMF). By combining HB methods with Monte Carlo simulations that incorporate the latest estimates of the Galactocentric radius of the Sun, R{sub 0}, the exponential scale length of the disk, L{sub d}, and the local surface density of stellar mass, Σ{sub ⋆}(R{sub 0}), we show that the mass of the Galactic bulge+bar is M{sub ⋆}{sup B}=0.91±0.07×10{sup 10} M{sub ⊙}, the disk mass is M{sub ⋆}{sup D}=5.17±1.11×10{sup 10} M{sub ⊙}, and their combination yields a total stellar mass of M{sub ⋆}=6.08±1.14×10{sup 10} M{sub ⊙} (assuming a Kroupa IMF and an exponential disk profile). This analysis is based upon a new compilation of literature bulge mass estimates, normalized to common assumptions about the stellar IMF and Galactic disk properties, presented herein. We additionally find a bulge-to-total mass ratio for the Milky Way of B/T=0.150{sub −0.019}{sup +0.028} and a specific SFR of M-dot {sub ⋆}/M{sub ⋆}=2.71±0.59×10{sup −11} yr{sup −1}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

High-Temperature Sapphire Pressure Sensors for Harsh Environments

The project aimed to develop and characterize an optical pressure sensor capable of withstanding high temperatures, up to 1000 °C. Survivability in harsh environments was enabled using sapphire for both the substrate and the optical fibers, therefore mitigating thermal expansion mismatch. Such material cannot be machined through conventional microfabrication techniques, and unique additive and subtractive methods had to be studied and implemented. Subtractive methods included laser-machining the sapphire substrate using a picosecond laser machining tool. At first, the parameters of importance were theoretically and experimentally evaluated such that the fluence, the pulse spacing, and the number of passes were found to strongly influence the laser ablation process. A simulation tool was designed to predict the final pattern when inputting a set of parameters and a profile. This model was complemented with laser ablation material physics models and Bayesian uncertainty analysis for one dimensional and three dimensional ablation processes. An additive process called thermocompression bonding was studied in order to seal the diaphragm substrate to the back cavity. At first, bonding strength using this technique was examined using the chevron-notch method that allow to determine the fracture toughness of this bond. This parameter is independent of the geometry used and therefore can be extended to other geometries used in actual sensor bonding. A theoretical and experimental study of internal damage caused by the laser micromachining of the sapphire material was carried out. The research identified enhanced strength and fracture toughness along laser ablated surfaces. This was determined using bend bar specimens and Vicker’s microindentations. Softening of the plasticity behavior (as indicated by nanoindentation experiments) was also investigated using finite deformation plasticity modeling via a contact mechanics finite element simulation. Good correlations between experiments and the model were achieved to determine material parameters that can be used in future MEMS based sensor design and modeling to minimize sensor failure loads. The enhanced strength and fracture toughness was determined to be due to compressive residual stress near laser ablated surfaces. This was confirmed through x-ray spectra taken along different crystal structure planes in combination and Bayesian uncertainty analysis. The Bayesian analysis inferred the entire residual strain tensor (six components) and the residual stress tensor. The results illustrated compressive in-plane stress was the most likely cause of enhanced strength and toughness after laser machining. Due to major equipment failures, a parallel route for fabricating the sensor was taken, using commercially available sapphire removal method (subtractive) and ceramic adhesive bonding (additive). The fully-packaged sensor utilized laser micromachining for the sapphire substrate fabrication and was comprised only of high-temperature-compatible materials. Calibration tests of the sensor in both room-temperature and high-temperature plane-wave tubes showed good linearity and consistent frequency response, in the tested frequency range. The sensor also showed good thermal stability in tests up to 300 °C, proving the fiber-optic lever and its packaging process to be a promising approach for high-temperature measurements. The calibration from 23 °C to 300 °C gave a raw sensitivity of 3.9 × 10 -7 V/Pa and a normalized sensitivity of 9.8 × 10 -8 Pa -1 which were consistent within the temperature range. The sensor had a resonance at 16.3 kHz and a flat band (±3 dB) up to 10.3 kHz. The noise floor of the sensor at 1 kHz was 316 nV/Hz 1/2 , resulting in a MDP of 0.8 Pa. Previously, diaphragm delamination due to CTE mismatch was found at 500 °C, causing packaging failure. Unfortunately high temperature validation in hot jet environment could not be made in time. In the future, it is suggested that the two sapphire substrates be bonded by thermal compression bonding with an intermediate platinum layer instead of using an alumina-based adhesive, and therefore achieve higher temperature capacity up to 1000 °C.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Goal-oriented a-posteriori estimation of model error as an aid to parameter estimation

In this work, a Bayesian model calibration framework is presented that utilizes goal-oriented a-posterior error estimates in quantities of interest (QoIs) for classes of high-fidelity models characterized by PDEs. It is shown that for a large class of computational models, it is possible to develop a computationally inexpensive procedure for calibrating parameters of high-fidelity models of physical events when the parameters of low-fidelity (surrogate) models are known with acceptable accuracy. The main ingredients in the proposed model calibration scheme are goal-oriented a-posteriori estimates of error in QoIs computed using a so-called lower fidelity model compared to those of an uncalibrated higher fidelity model. The estimates of error in QoIs are used to define likelihood functions in Bayesian inversion analysis. A standard Bayesian approach is employed to compute the posterior distribution of model parameters of high-fidelity models. As applications, parameters in a quasi-linear second-order elliptic boundary-value problem (BVP) are calibrated using a second-order linear elliptic BVP. In a second application, parameters of a tumor growth model involving nonlinear time-dependent PDEs are calibrated using a lower fidelity linear tumor growth model with known parameter values.

A-posterior estimates↗
DOE OSTI2015

UNSUPERVISED TRANSIENT LIGHT CURVE ANALYSIS VIA HIERARCHICAL BAYESIAN INFERENCE

Historically, light curve studies of supernovae (SNe) and other transient classes have focused on individual objects with copious and high signal-to-noise observations. In the nascent era of wide field transient searches, objects with detailed observations are decreasing as a fraction of the overall known SN population, and this strategy sacrifices the majority of the information contained in the data about the underlying population of transients. A population level modeling approach, simultaneously fitting all available observations of objects in a transient sub-class of interest, fully mines the data to infer the properties of the population and avoids certain systematic biases. We present a novel hierarchical Bayesian statistical model for population level modeling of transient light curves, and discuss its implementation using an efficient Hamiltonian Monte Carlo technique. As a test case, we apply this model to the Type IIP SN sample from the Pan-STARRS1 Medium Deep Survey, consisting of 18,837 photometric observations of 76 SNe, corresponding to a joint posterior distribution with 9176 parameters under our model. Our hierarchical model fits provide improved constraints on light curve parameters relevant to the physical properties of their progenitor stars relative to modeling individual light curves alone. Moreover, we directly evaluate the probability for occurrence rates of unseen light curve characteristics from the model hyperparameters, addressing observational biases in survey methodology. We view this modeling framework as an unsupervised machine learning technique with the ability to maximize scientific returns from data to be collected by future wide field transient searches like LSST.

79 ASTRONOMY AND ASTROPHYSICS↗