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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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NASA NTRS1999

Modeling the unidentified infrared emission with combinations of polycyclic aromatic hydrocarbons

The infrared emission band spectrum associated with many different interstellar objects can be modeled successfully by using combined laboratory spectra of neutral and positively charged polycyclic aromatic hydrocarbons (PAHs). These model spectra, shown here for the first time, alleviate the principal spectroscopic criticisms previously leveled at the PAH hypothesis and demonstrate that mixtures of free molecular PAHs can indeed account for the overall appearance of the widespread interstellar infrared emission spectrum. Furthermore, these models give us insight into the structures, stabilities, abundances, and ionization balance of the interstellar PAH population. These, in turn, reflect conditions in the emission zones and shed light on the microscopic processes involved in the carbon nucleation, growth, and evolution in circumstellar shells and the interstellar medium.

Spectrophotometry, Infrared↗
NASA NTRS1976

Planetary astronomy program

Visual photometric function data for Saturn's rings were analyzed in terms of elementary anisotropic scattering radiative transfer models which involve the Henyey-Greenstein function. Limits were placed on the combinations of single scattering albedo, and backscattering directivity, which are permitted by observation. Particles with lunar-like scattering properties were excluded by the analysis. Results are consistent with the ring particles being more-or-less pure, and spherical, conglomerates of H2O frost. Multicolor (5500 A-7600 A), narrow band (100 A), area scanning photometry was used to study the wavelength variation in the optical appearance of Uranus. Limb brightening was detected in two CH4 bands, i.e. 6190A and 7300A. Spectrophotometric observations and analysis of the asteroids and Raman spectroscopy of the atmosphere of Uranus are also briefly discussed.

Source record↗
DOE OSTI2024

A High-level Design for Bidirectional Data Streaming to High-Performance Computing Systems from External Science Facilities

Cutting-edge science is increasingly data-driven due to the emergence of scientific machine learning models that can guide scientists toward fruitful areas of exploration. Experimental science facilities such as light and neutron sources, particle colliders, and radio astronomy telescopes are also producing raw measurement data at rates that exceed available data storage and computing capacity at those facilities. As a result, scientific workflows are being developed that concurrently couple experiments at science facilities with high-performance computing (HPC) facilities to enable analysis of experimental data while the experiment is ongoing, and where analysis results are potentially fed back to the experiment for use in experimental control and/or steering in a time-sensitive manner. Our goal is to design, prototype, and deploy a new capability for the Oak Ridge Leadership Computing Facility (OLCF) that enables such workflows through support for bidirectional, memory-based streaming of data from external experiments into and out of OLCF HPC systems. This high-level design document describes the related work and motivating use cases that inform our understanding of the technical requirements for this capability, and describes a proposed architectural solution that meets these requirements and our plans for demonstrating the capability.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Atomic Data Needs for X-Ray Astronomy

This publication contains written versions of most of the invited talks presented at the workshop on Atomic Data Needs for X-ray Astronomy which was held at NASA's Goddard Space Flight Center on December 16-1 7 1999. The idea of hosting such a workshop emerged from an imminent need to update and complete current atomic datasets in anticipation of a new era of high quality X-ray spectra starting with the launching of Chandra and XMM-Newton observatories. At first, our vision of the workshop was of a short and limited attendance event, given the specialization of the topic. But it, was soon realized, from the response to the first workshop announcement, that the topic was of much interest, to researchers working in X-ray spectra (physicists and astronomers). As a result, the workshop grew to approximately 120 participants from several countries. The kind of atomic data that interests us are those parameters needed for analysis and modeling of spectra shortward of about about 100 A and relevant to ionic species of astronomical interest. The physical mechanisms of interest in the formation of spectra include photoionization. collisional ionization, recombination (radiative and dielectronic). collisional excitation (by electrons and protons). and radiative deexcitation. Unique to X-ray spectroscopy are the ionization and excitation processes from inner-closed shells. in addition to the challenges in interpret,ing the medium resolution (epsilon/delta epsilon is about 0.05 - 0.1) data obtained by current X-ray astronomy experiments. Line wavelengths are of interest too, particularly owing to the high resolution spectra from the new experiments. The workshop was divided into five major areas: Observational Spectroscopy, Theoretical Calculations of Atomic Data, Laboratory Measurements of Atomic Parameters. Spectra Modeling, and Atomic Databases. One comforting finding from the work shop is that the enthusiasm felt by X-ray astronomers about the new observational missions seems to be shared by theoretical and experimental physicists. Talks were presented about several exciting new projects and experimental and theoretical techniques devoted to X-ray spectroscopy. Simultaneously, several new tools for spectral analysis and modeling have recently been developed, together with improved atomic databases. These proceeding are expected to be of interests to producers and users of atomic data. Moreover. the contributions presented here have been written in a way that can be used by a general audience of scientists and graduate students in X-ray astronomy, modelling, and in computational and experimental atomic physics.

Kallman, Timothy↗
DOE OSTI2020

Fundamental Parameters of ∼30,000 M dwarfs in LAMOST DR1 Using Data-driven Spectral Modeling

M dwarfs are the most common type of star in the Galaxy, and because of their small size are favored targets for searches of Earth-sized transiting exoplanets. Current and upcoming all-sky spectroscopic surveys, such as the Large Sky Area Multi Fiber Spectroscopic Telescope (LAMOST), offer an opportunity to systematically determine physical properties of many more M dwarfs than has been previously possible. Here, we present new effective temperatures, radii, masses, and luminosities for 29,678 M dwarfs with spectral types M0–M6 in the first data release (DR1) of LAMOST. We derived these parameters from the supervised machine-learning code, The Cannon, trained with 1388 M dwarfs in the Transiting Exoplanet Survey Satellite Cool Dwarf Catalog that were also present in LAMOST with high signal-to-noise ratio (>250) spectra. Our validation tests show that the output parameter uncertainties are strongly correlated with the signal-to-noise of the LAMOST spectra, and we achieve typical uncertainties of 110 K in T{sub eff} (∼3%), 0.065 R{sub ⊙} (∼14%) in radius, 0.054 M{sub ⊙} (∼12%) in mass, and 0.012 L{sub ⊙} (∼20%) in luminosity. The model presented here can be rapidly applied to future LAMOST data releases, significantly extending the samples of well-characterized M dwarfs across the sky using new and exclusively data-based modeling methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics

Generating large-volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next-generation observations. In this work, we construct a novel fully convolutional variational autoencoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N -body simulations. After training the model on a single hydrodynamical simulation, we are able to probabilistically map new dark-matter-only simulations to corresponding full hydrodynamical outputs. By sampling over the latent space of our VAE, we can generate posterior samples and study the variance of the mapping. We find that our reconstructed field provides an accurate representation of the target hydrodynamical fields as well as reasonable variance estimates. This approach has promise for the rapid generation of mocks as well as for implementation in a full inverse model of observed data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Using machine learning to identify extragalactic globular cluster candidates from ground-based photometric surveys of M87

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87’s well-studied GC system to implement supervised machine learning (ML) classification algorithms – specifically random forest and neural networks – to identify GCs from foreground stars and background galaxies, using ground-based photometry from the Canada–France–Hawaii Telescope (CFHT). We compare these two ML classification methods to studies of ‘human-selected’ GCs and find that the best-performing random forest model can reselect 61.2 per cent ± 8.0 per cent of GCs selected from HST data (ACSVCS) and the best-performing neural network model reselects 95.0 per cent ± 3.4 per cent. When compared to human-classified GCs and contaminants selected from CFHT data – independent of our training data – the best-performing random forest model can correctly classify 91.0 per cent ± 1.2 per cent and the best-performing neural network model can correctly classify 57.3 per cent ± 1.1 per cent. ML methods in astronomy have been receiving much interest as Vera C. Rubin Observatory prepares for first light. The observables in this study are selected to be directly comparable to early Rubin Observatory data and the prospects for running ML algorithms on the upcoming data set yields promising results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state

The joint detection of the gravitational wave GW170817, of the short γ-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star (NS) merger observed on 2017 August 17, is a milestone in multimessenger astronomy and provides new constraints on the NS equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multicomponents models that also account for non-spherical ejecta. Observational data favour anisotropic geometries to spherically symmetric profiles, with a log-Bayes’ factor of ~104, and favour multicomponent models against single-component ones. The best-fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical–relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q < 1.54 and the reduced tidal parameter to $120\lt \tilde{\Lambda }\lt 1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M ⊙ to 12.2 ± 0.5 km (1σ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Old data, new forensics: The first second of SN 1987A neutrino emission

The next Milky Way supernova will be an epochal event in multimessenger astronomy, critical to tests of supernovae, neutrinos, and new physics. Realizing this potential depends on having realistic simulations of core collapse. Here, we investigate the neutrino predictions of modern models (1-, 2-, and 3-D) over the first ≃1 s, making the first detailed comparisons of these models to each other and to the SN 1987A neutrino data. Even with different methods and inputs, the models generally agree with each other. However, even considering the low neutrino counts, the models generally disagree with data. What can cause this? We show that neither neutrino oscillations nor different progenitor masses appear to be a sufficient solution. We outline urgently needed work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites

Abstract Ultra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Laboratory Data on the EUV/Soft-X-Ray Flux Problem of Fe XVIII and Fe XIX

Observations by the spacecraft Extreme Ultraviolet Explorer (EUVE), Far Ultraviolet Spectroscopic Explorer (FUSE), Chandra, and XMM-Newton of Capella (α Aurigae) have encountered problems with the relative intensities of the Fe XVIII and Fe XIX line emission in the soft-X-ray (XUV) and extreme-ultraviolet spectral ranges versus various model predictions based on theoretical atomic data. The reason may either lie in astrophysical phenomena, e.g., at emission or because of absorption by the interstellar medium, in the theoretical atomic data, in one or more spectrometer calibrations, or in the spectral modeling. Here, by measurements using an electron beam ion trap we provide laboratory data obtained under conditions that are reasonably close to stellar emission regions. The laboratory data on Fe XVIII and Fe XIX show line ratios that are rather similar to the observations of Capella. The measurements, therefore, rule out astrophysical phenomena and calibration errors and point to issues with the modeling of the observed Capella emission.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2009

A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

Bhaduri, Kanishka↗
DOE OSTI2022

Constraining low redshift [C II ] emission by cross-correlating FIRAS and BOSS data

ABSTRACT We perform a tomographic cross-correlation analysis of archival FIRAS data and the BOSS galaxy redshift survey to constrain the amplitude of [C II] 2P3/2 → 2P1/2 fine structure emission. Our analysis employs spherical harmonic tomography (SHT), which is based on the angular cross-power spectrum between FIRAS maps and BOSS galaxy over-densities at each pair of redshift bins, over a redshift range of 0.24 < z < 0.69. We develop the SHT approach for intensity mapping, where it has several advantages over existing power spectral estimators. Our analysis constrains the product of the [C II] bias and [C II] specific intensity, $b_{\rm [C \small{\rm II}]}I_{\rm [C \small{\rm II}]}$, to be <0.31 MJy/sr at z ≈ 0.35 and <0.28 MJy/sr at z ≈ 0.57 at $95{{\ \rm per\ cent}}$ confidence. These limits are consistent with most current models of the [C II] signal, as well as with higher-redshift [C II] cross-power spectrum measurements from the Planck satellite and BOSS quasars. We also show that our analysis, if applied to data from a more sensitive instrument such as the proposed PIXIE satellite, can detect pessimistic [C II] models at high significance.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2011

Development of a Multivariable Parametric Cost Analysis for Space-Based Telescopes

Over the past 400 years, the telescope has proven to be a valuable tool in helping humankind understand the Universe around us. The images and data produced by telescopes have revolutionized planetary, solar, stellar, and galactic astronomy and have inspired a wide range of people, from the child who dreams about the images seen on NASA websites to the most highly trained scientist. Like all scientific endeavors, astronomical research must operate within the constraints imposed by budget limitations. Hence the importance of understanding cost: to find the balance between the dreams of scientists and the restrictions of the available budget. By logically analyzing the data we have collected for over thirty different telescopes from more than 200 different sources, statistical methods, such as plotting regressions and residuals, can be used to determine what drives the cost of telescopes to build and use a cost model for space-based telescopes. Previous cost models have focused their attention on ground-based telescopes due to limited data for space telescopes and the larger number and longer history of ground-based astronomy. Due to the increased availability of cost data from recent space-telescope construction, we have been able to produce and begin testing a comprehensive cost model for space telescopes, with guidance from the cost models for ground-based telescopes. By separating the variables that effect cost such as diameter, mass, wavelength, density, data rate, and number of instruments, we advance the goal to better understand the cost drivers of space telescopes.. The use of sophisticated mathematical techniques to improve the accuracy of cost models has the potential to help society make informed decisions about proposed scientific projects. An improved knowledge of cost will allow scientists to get the maximum value returned for the money given and create a harmony between the visions of scientists and the reality of a budget.

Dollinger, Courtnay↗
NASA NTRS1972

Application of new radio tracking data types to critical spacecraft navigation problems

Earth-based radio tracking data types are considered, which involve simultaneous or nearly simultaneous spacecraft tracking from widely separated tracking stations. These data types are conventional tracking instrumentation analogs of the very long baseline interferometry (VLBI) of radio astronomy-hence the name quasi-VLBI. A preliminary analysis of quasi-VLBI is presented using simplified tracking data models. The results of accuracy analyses are presented for a representative mission, Viking 1975. The results indicate that, contingent on projected tracking system accuracy, quasi-VLBI can be expected to significantly improve navigation performance over that expected from conventional tracking data types.

Ondrasik, V. J.↗
DOE OSTI2022

Anomalies in the topology of the temperature fluctuations in the cosmic microwave background: An analysis of the NPIPE and FFP10 data releases

We present a topological analysis of the temperature fluctuation maps from the Planck 2020 Data Release 4 NPIPE dataset and the Planck 2018 Data Release 3 FFP10 dataset. We performed a multiscale analysis in terms of the homology characteristics of the maps, invoking relative homology to account for the analysis in the presence of masks. We performed our analysis for a range of smoothing scales spanning sub- and super-horizon scales corresponding to a full width at half maximum (FWHM) of 5',10',20',40',80',160',320', and 640', and employed simulations based on the standard model for comparison, which assumes the initial fluctuation field to be an isotropic and homogeneous Gaussian random field. Examining the behavior of topological components, represented by the 0D homology group, we find the observations to be approximately 2σ or less deviant from the simulations for all resolutions and scales for the NPIPE dataset. For the FFP10 dataset, we detect a 2.96σ deviation between the observations and simulations at N = 128, FWHM = 80'. For the topological loops, represented by the first homology group, the simulations and observations are consistent within 2σ for most resolutions and scales for both the datasets. However, for the NPIPE dataset, we observe a high deviation between the observation and simulations in the number of loops at FWHM = 320', but at a low dimensionless threshold ν = –2.5. Under a Gaussian assumption, this would amount to a deviation of ~4σ. However, the distribution in this bin is manifestly non-Gaussian and does not obey Poisson statistics either. In the absence of a true theoretical understanding, we simply note that the significance is higher than what may be resolved by 600 simulations, yielding an empirical p-value of at most 0.0016. Specifically in this case, our tests indicate that the numbers arise from a statistically stable regime, despite being based on small numbers. For the FFP10 dataset, the differences are not as strong as for the NPIPE dataset, indicating a 2.77σ deviation at this resolution and threshold. The Euler characteristic, which is the alternating sum of the ranks of relative homology groups, reflects the deviations in the components and loops. To assess the significance of combined levels for a given scale, we employed the empirical and theoretical versions of the χ 2 test as well as the nonparametric Tukey depth test. Although all statistics exhibit a stable distribution, we favor the empirical version of the χ 2 test in the final interpretation, as it indicates the most conservative differences. For the NPIPE dataset, we find that the components and loops differ at more than 95%, but agree within the 99% confidence level with respect to the base model at N = 32, FWHM = 320'. The Euler characteristic at this resolution displays a per mil deviation. In contrast, the FFP10 dataset shows that the observations are consistent with the base model within the 95% confidence level, at this and smaller scales. This is consistent with the observations of the Planck analysis pipeline via Minkowski functionals. For the largest smoothing scale, N = 16, FWHM = 640', both datasets exhibit an anomalous behavior of the loops, where FFP10 data exhibit a deviation that is larger by an order of magnitude than that of the NPIPE dataset. In contrast, the values for the topological components and the Euler characteristic agree between observations and model to within a confidence level of 99%. However, for the largest scales, the statistics are based on low numbers and may have to be regarded with caution. Even though both datasets exhibit mild to significant discrepancies, they also exhibit contrasting behaviors at various instances. Therefore, we do not find it feasible to convincingly accept or reject the null hypothesis. Disregarding the large-scale anomalies that persist at similar scales in WMAP and Planck, observations of the cosmic microwave background are largely consistent with the standard cosmological model within 2σ.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Mirror twin Higgs cosmology: constraints and a possible resolution to the H$_{0}$ and S$_{8}$ tensions

The mirror twin Higgs model (MTH) is a solution to the Higgs hierarchy problem that provides well-predicted cosmological signatures with only three extra parameters: the temperature of the twin sector, the abundance of twin baryons, and the vacuum expectation value (VEV) of twin electroweak symmetry breaking. These parameters specify the behavior of twin radiation and the acoustic oscillations of twin baryons, which lead to testable effects on the cosmic microwave background (CMB) and large-scale structure (LSS). While collider searches can only probe the twin VEV, through a fit to cosmological data we show that the existing CMB (Planck18 TTTEEE+lowE+lowT+lensing) and LSS (KV450) data already provide useful constraints on the remaining MTH parameters. Additionally, we show that the presence of twin radiation in this model can raise the Hubble constant H$_{0}$ while the scattering twin baryons can reduce the matter fluctuations S$_{8}$, which helps to relax the observed H$_{0}$ and S$_{8}$ tensions simultaneously. This scenario is different from the typical ΛCDM + ΔN$_{eff}$ model, in which extra radiation helps with the Hubble tension but worsens the S$_{8}$ tension. For instance, when including the SH0ES and 2013 Planck SZ data in the fit, we find that a universe with ≳ 20% of the dark matter comprised of twin baryons is preferred over ΛCDM by ~ 4σ. If the twin sector is indeed responsible for resolving the H$_{0}$ and S$_{8}$ tensions, future measurements from the Euclid satellite and CMB Stage 4 experiment will further measure the twin parameters to O(1 - 10%)-level precision. Our study demonstrates how models with hidden naturalness can potentially be probed using precision cosmological data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗