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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 19 records

A simple electron counting model for half-Heusler surfaces

Heusler compounds are a ripe platform for discovery and manipulation of emergent properties in topological and magnetic heterostructures. In these applications, the surfaces and interfaces are critical to performance; however, little is known about the atomic-scale structure of Heusler surfaces and interfaces or why they reconstruct. Using a combination of molecular beam epitaxy, core-level and angle-resolved photoemission, scanning tunneling microscopy, and density functional theory, we map the phase diagram and determine the atomic and electronic structures for several surface reconstructions of CoTiSb (001), a prototypical semiconducting half-Heusler. At low Sb coverage, the surface is characterized by Sb-Sb dimers and Ti vacancies, while, at high Sb coverage, an adlayer of Sb forms. The driving forces for reconstruction are charge neutrality and minimizing the number of Sb dangling bonds, which form metallic surface states within the bulk bandgap. We develop a simple electron counting model that explains the atomic and electronic structure, as benchmarked against experiments and first-principles calculations. We then apply the model to explain previous experimental observations at other half-Heusler surfaces, including the topological semimetal PtLuSb and the half-metallic ferromagnet NiMnSb. The model provides a simple framework for understanding and predicting the surface structure and properties of these novel quantum materials.

36 MATERIALS SCIENCE↗

Poisson Log-Normal Process for Count Data Prediction

Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events. Traditional parametric approaches can be trained to generate integer-count predictions but may struggle with capturing complex, non-linear dependencies often observed in the data. Gaussian process (GP) regression provides a robust non-parametric alternative to modeling continuous data; however, it cannot generate integer outputs. We propose the Poisson Log-Normal (PoLoN) process, a framework that employs GP to model Poisson log-rates. As in GP regression, our approach relies on the correlations between data points captured via GP kernel structure rather than explicit functional parameterizations. We demonstrate that the PoLoN predictive distribution is Poisson-LogNormal and provide an algorithm for optimizing kernel hyperparameters. Furthermore, we adapt the PoLoN approach to the problem of detecting weak localized signals superimposed on a smoothly varying background - a task of considerable interest in many areas of science and engineering. Our framework allows us to predict the strength, location and width of the detected signals. We evaluate PoLoN's performance using both synthetic and real-world datasets, including the open dataset from CERN which was used to detect the Higgs boson at the Large Hadron Collider. Our results indicate that the PoLoN process can be used as a non-parametric alternative for analyzing, predicting, and extracting signals from integer-valued data.

Saha, Anushka [Rutgers U., Piscataway]↗

Development of response models for the Earth Radiation Budget Experiment (ERBE) sensors. Part 4: Preliminary nonscanner models and count conversion algorithms

Two count conversion algorithms and the associated dynamic sensor model for the M/WFOV nonscanner radiometers are defined. The sensor model provides and updates the constants necessary for the conversion algorithms, though the frequency with which these updates were needed was uncertain. This analysis therefore develops mathematical models for the conversion of irradiance at the sensor field of view (FOV) limiter into data counts, derives from this model two algorithms for the conversion of data counts to irradiance at the sensor FOV aperture and develops measurement models which account for a specific target source together with a sensor. The resulting algorithms are of the gain/offset and Kalman filter types. The gain/offset algorithm was chosen since it provided sufficient accuracy using simpler computations.

Halyo, Nesim↗

Alternate Approach to Multi Layer Insulation Modeling to Reduce Node Count

For models with a limitation on the overall node count, the typical approach to Multi-Layer Insulation (MLI) modeling may generate nodes that are necessary for the analysis, but do not represent components of particular interest. This leaves fewer nodes that can be utilized to model components of greater importance than the MLI. A common approach to modeling MLI is to include a separate MLI node representing the outer layer of the insulation and a radiative coupling based on the area multiplied by an effective emissivity. Therefore, wherever insulation is included, one node is needed for the underlying surface and another node for the insulation. Since many spacecraft and instruments include MLI covering a sizable portion of their designs, this may result in a considerable number of nodes being used for MLI. An alternate method to MLI modeling was developed that eliminates the MLI node, while still preserving the effect of the insulation for the underlying surface, thereby increasing the available nodes that could be used elsewhere in the model. This approach relies on preserving the baseline reflectivity, while reducing the absorptivity (based on the blanket effective emittance) and including a transparency. An inactive second surface is placed just behind the base surface that fully absorbs any energy that is transmitted without including its effect in the model. In essence, this approach applies only the energy that makes it through the blanket to the underlying surface. This method was tested out on the Roman Space Telescope model in local areas in preparation for its use in the generation of a launch model, which is constrained in the allowable node count. This paper documents the performance of the method and presents a comparison between the One-Node MLI method and the traditional two node MLI approach.

Thermal↗

Cluster evolution and microwave source counts

We present the modeled counts for the expected Sunyaev-Zel'dovich microwave sources associated with clusters of galaxies, predicted for experiments with arcminute-scale spatial resolution, assuming self-similar cluster evolution, for different spectra of the primordial density fluctuations and values of the cosmological density parameter Omega. Our simulations show that the source counts should be a powerful test of the evolution of very high redshift clusters. Experiments with 1 - 2 min spatial resolution, with moderate sensitivity but covering a large area of the sky, would be most effective for studying the SZ source population. Recent arcminute-scale radio experiments, the Owens Valley Radio Observatory (OVRO) RING experiment and VLA deep imaging, achieved sensitivity and sky coverage close to that needed for the detection of negative sources associated with very distant clusters. From the absence of cluster detections in these experiments, we rule out, with 90% confidence, models with Omega less than 0.3 and n = +1 as predicting too many bright sources; or there is no hot gas in clusters more distant than z(sub max) = 5 in such models. If the single negative source detected in the RING experiment is a distant cluster, the Omega = 1, n = -2 model also may be ruled out as it predicts too few sources. The new generation of telescopes, including the new SUZIE and Ryle instruments, will soon be able to detect distant clusters. The cluster population in the past has been modeled by scaling the observed present-day sample of X-ray clusters back to high redshifts, an approach which makes the best use of the observed cluster gas parameters, and makes the simulations robust to the assumed evolution at very early epochs. Although the pure self-similar model may be incompatible with the variety of observed evolutionary effects, we show that reasonable modifications to the intracluster gas history in that model, proposed to reconcile the self-similar evolution of cluster mass and the observed evolution of their X-ray luminosity, do not considerably change our microwave predictions made using the pure self-similar model. That is, the results of our simulations are applicable to the wide class of evolutionary models in which the cluster gas mass times gas temperature evolves as the dark mass times cluster virial temperature.

Markevitch, M.↗

A galaxy model from two micron all sky survey star counts in the whole sky, including the plane

We use the star count model of Ortiz and Lépine to perform an unprecedented exploration of the most important Galactic parameters comparing the predicted counts with the Two Micron All Sky Survey observed star counts in the J, H, and K{sub S} bands for a grid of positions covering the whole sky. The comparison is made using a grid of lines of sight given by the HEALPix pixelization scheme. The resulting best-fit values for the parameters are: 2120 ± 200 pc for the radial scale length and 205 ± 40 pc for the scale height of the thin disk, with a central hole of 2070{sub −800}{sup +2000} pc for the same disk, 3050 ± 500 pc for the radial scale length and 640 ± 70 pc for the scale height of the thick disk, 400 ± 100 pc for the central dimension of the spheroid, 0.0082 ± 0.0030 for the spheroid to disk density ratio, and 0.57 ± 0.05 for the oblate spheroid parameter.

79 ASTRONOMY AND ASTROPHYSICS↗

State-to-State Molecular Reactions in the Ultracold Regime (Final Scientific Report)

We have achieved the proposed goal to experimentally probe the AB + CD and AB + C types of reactions with state-to-state resolution, which we also compared to advanced theoretical calculations to help elucidate the role of quantum mechanics in the processes of bond breakage and formation. Our approach uses reactants that are prepared at ultracold temperatures (< 1µK) such that the quantum effects of translational motion are an important factor. Specific example reactions, including the potassium-rubidium metathesis reaction KRb + KRb → K 2 + Rb 2 as well as the atom exchange reaction Rb + KRb → Rb2 + K, are chosen because the technology of quantum internal and motional state control of these types of molecules is particularly advanced. The results for the entire funding period are fruitful. For the majority of this grant, we have constructed a one-of-the-kind quantum degenerate gas apparatus that integrates ion detection and velocity map imaging capabilities, allowing us to explore the KRb + KRb → K 2 + Rb 2 bimolecular reaction in detail. Specifically, we first verified such a reaction indeed proceed at ultracold temperatures by direct detection of reaction products. We then mapped out the complete product state distribution, which was compared to a state-counting model based on statistical theory. Our results show an overall agreement with the statistical state counting model, but also reveal several deviating state-pairs. An exact quantum calculation for molecule-molecule collisions, that is needed to understand these deviations, is however beyond the current state-of-the-art. Beside scrutinizing the reaction products, we also directly observe the reaction intermediate complex, which was quite a surprise to us. The intermediate complexes are long-lived and can interact with the inferred light that we use to trap the ultracold gas. After molecule-molecule collisions, we then explored the more theoretically tractable Rb + KRb reaction, which is endothermic. Surprisingly, we observed an exceedingly long-lived KRb$^*_2$ collisional complexes, with our experimentally measured complex lifetime deviating from conventional theoretical calculations by five orders of magnitude. This discrepancy has motivated many explorations of possible underlying causes, though no model yet captures this phenomenon completely. In the final year and the work that continues today, we extend upon these atom-molecule collision experiments to explore the origin of the long-lived KRb$^*_2$ complex lifetime and develop means to control the outcome of the reaction complex. The 5-year funded work advanced our understanding of chemical reactions at the lowest possible temperatures and at the same time opened up many new questions that are beyond our initial imaginations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Building an Efficient Cluster Cosmology Software Package for Modeling Cluster Counts and Lensing

We introduce a software suite developed for galaxy cluster cosmological analysis with the Dark Energy Survey Data. Cosmological analyses based on galaxy cluster number counts and weak-lensing measurements need efficient software infrastructure to explore an increasingly large parameter space, and account for various cosmological and astrophysical effects. Our software package is designed to model the cluster observables in a wide-field optical survey, including galaxy cluster counts, their averaged weak-lensing masses, or the cluster's averaged weak-lensing radial signals. To ensure maximum efficiency, this software package is developed in C++ in the CosmoSIS software framework, making use of the CUBA integration library. We also implement a testing and validation scheme to ensure the quality of the package. We demonstrate the effectiveness of this development by applying the software to the Dark Energy Survey Year 1 galaxy cluster cosmological data sets, and acquired cosmological constraints that are consistent with the fiducial Dark Energy Survey analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Density split statistics: Joint model of counts and lensing in cells

We present density split statistics, a framework that studies lensing and counts-in-cells as a function of foreground galaxy density, thereby providing a large-scale measurement of both 2-point and 3-point statistics. Our method extends our earlier work on trough lensing and is summarized as follows: given a foreground (low redshift) population of galaxies, we divide the sky into subareas of equal size but distinct galaxy density. We then measure lensing around uniformly spaced points separately in each of these subareas, as well as counts-in-cells statistics (CiC). The lensing signals trace the matter density contrast around regions of fixed galaxy density. Through the CiC measurements this can be related to the density profile around regions of fixed matter density. Together, these measurements constitute a powerful probe of cosmology, the skewness of the density field and the connection of galaxies and matter. In this paper we show how to model both the density split lensing signal and CiC from basic ingredients: a non-linear power spectrum, clustering hierarchy coefficients from perturbation theory and a parametric model for galaxy bias and shot-noise. Furthermore, using N-body simulations, we demonstrate that this model is sufficiently accurate for a cosmological analysis on year 1 data from the Dark Energy Survey.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Time bomb or hidden treasure? Characteristics of junk TVs and of the US households who store them

Highlights: ► US households are storing 84.1 million broken or obsolete (junk) TVs. ► They represent 2.12 million metric tons of scrap. ► The value of these materials is approximately $21 per TV. ► Our count models characterize US households who store junk TVs. ► Our results are useful for designing more effective TV recycling programs. - Abstract: Within the growing stockpile of electronic waste (e-waste), TVs are especially of concern in the US because of their number (which is known imprecisely), their low recycling rate, and their material content: cathode ray tube televisions contain lead, and both rear projection and flat panel displays contain mercury, in addition to other potentially toxic materials. Based on a unique dataset from a 2010 survey, our count models show that pro-environmental behavior, age, education, household size, marital status, gender of the head of household, dwelling type, and geographic location are statistically significant variables for explaining the number of broken or obsolete (junk) TVs stored by US households. We also estimate that they are storing approximately 84.1 million junk TVs, which represents 40 pounds of scrap per household. Materials in each of these junk TVs are worth $21 on average at January 2012 materials prices, which sets an upper bound on collecting and recycling costs. This information should be helpful for developing more effective recycling strategies for TVs in the e-waste stream.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

The local hole: a galaxy underdensity covering 90 per cent of sky to ≈200 Mpc

We investigate the ‘Local Hole’, an anomalous underdensity in the local galaxy environment, by extending our previous galaxy K-band number-redshift and number-magnitude counts to ≈90 per cent of the sky. Our redshift samples are taken from the 2MASS Redshift Survey (2MRS) and the 2M++ catalogues, limited to K < 11.5. We find that both surveys are in good agreement, showing an ≈21–22 per cent underdensity at z < 0.075 when compared to our homogeneous counts model that assumes the same luminosity function (LF) and other parameters as in our earlier papers. Using the Two Micron All Sky Survey (2MASS) for n(K) galaxy counts, we measure an underdensity relative to this model of 20±2 per cent at K < 11.5, which is consistent in both form and scale with the observed n(z) underdensity. To examine further the accuracy of the counts model, we compare its prediction for the fainter n(K) counts of the Galaxy and Mass Assembly (GAMA) survey. We further compare these data with a model assuming the parameters of a previous study where little evidence for the Local Hole was found. At 13 < K < 16, we find a significantly better fit for our galaxy counts model, arguing for our higher LF normalization. Although our implied underdensity of ≈20 per cent means local measurements of the Hubble Constant have been overestimated by ≈3 per cent, such a scale of underdensity is in tension with a global ΛCDM cosmology at an ≈3σ level.

79 ASTRONOMY AND ASTROPHYSICS↗

QuantifyML: How good is my machine learning model?

This paper presents an approach, QuantifyML, which employs model counting to assess the learnability and robustness of machine learning models. Typically the efficacy of machine learning models is determined by computing their accuracy statistically on test data sets. However, this may be misleading, if the test data is not representative of the problem that is being studied. Further, two different models may have the same accuracy on a given data set, measured statistically, but may be very different in their behavior on unseen data. Also, models with high accuracy could have poor adversarial robustness. In QuantifyML, our goal is to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into a C program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise counts w.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, expressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the robustness of trained models around given inputs. Our evaluation demonstrates these applications of QuantifyML on decision trees and neural networks trained to learn relational properties of graphs, for which we know the ground truth, and to perform image classification, for which we do not have the ground truth, but we can quantify local robustness.

Deep Neural Networks↗

QuantifyML: How Good is My Machine Learning Model?

We present QuantifyML, which applies model counting to assess the learn ability, safety, and robustness of machine learning models. Typically, the efficacy of machine learning models is determined by computing their accuracy statistically on test datasets. However, this may be misleading, if the test data is not representative of the problem that is being studied. With QuantifyML we aim to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into aC program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise countsw.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, ex-pressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the safety and robustness of trained models.

Deep Neural Networks↗

FOREGROUND PREDICTIONS FOR THE COSMIC MICROWAVE BACKGROUND POWER SPECTRUM FROM MEASUREMENTS OF FAINT INVERTED RADIO SOURCES AT 5 GHz

We present measurements of a population of matched radio sources at 1.4 and 5 GHz down to a flux limit of 1.5 mJy in 7 deg{sup 2} of the NOAO Deep Field South. We find a significant fraction of sources with inverted spectral indices that all have 1.4 GHz fluxes less than 10 mJy and are therefore too faint to have been detected and included in previous radio source count models that are matched at multiple frequencies. Combined with the matched source population at 1.4 and 5 GHz in 1 deg{sup -2} in the ATESP survey, we update models for the 5 GHz differential number counts and distributions of spectral indices in 5 GHz flux bins that can be used to estimate the unresolved point source contribution to the cosmic microwave background temperature anisotropies. We find a shallower logarithmic slope in the 5 GHz differential counts than in previously published models for fluxes {approx}< 100 mJy as well as larger fractions of inverted spectral indices at these fluxes. Because the Planck flux limit for resolved sources is larger than 100 mJy in all channels, our modified number counts yield at most a 10% change in the predicted Poisson contribution to the Planck temperature power spectrum. For a flux cut of 5 mJy with the South Pole Telescope and a flux cut of 20 mJy with the Atacama Cosmology Telescope, we predict a {approx}30% and {approx}10% increase, respectively, in the radio source Poisson power in the lowest frequency channels of each experiment relative to that predicted by previous models.

79 ASTRONOMY AND ASTROPHYSICS↗