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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Development of a Deployable Nonmetallic Boom for Reconfigurable Systems of Small Modular Spacecraft

Launch vehicle payload capacity and the launch environment represent two of the most operationally limiting constraints on space system mass, volume, and configuration. Large-scale space science and power platforms as well as transit vehicles have been proposed that greatly exceed single-launch capabilities. Reconfigurable systems launched as multiple small modular spacecraft with the ability to rendezvous, approach, mate, and conduct coordinated operations have the potential to make these designs feasible. A key characteristic of these proposed systems is their ability to assemble into desired geometric (spatial) configurations. While flexible and sparse formations may be realized by groups of spacecraft flying in close proximity, flyers physically connected by active structural elements could continuously exchange power, fluids, and heat (via fluids). Configurations of small modular spacecraft temporarily linked together could be sustained as long as needed with minimal propellant use and reconfigured as often as needed over extended missions with changing requirements. For example, these vehicles could operate in extremely compact configurations during boost phases of a mission and then redeploy to generate power or communicate while coasting and upon reaching orbit. In 2005, NASA funded Phase 1 of a program called Modular Reconfigurable High-Energy Technology Demonstrator Assembly Testbed (MRHE) to investigate reconfigurable systems of small spacecraft. The MRHE team was led by NASA's Marshall Space Flight Center and included Lockheed Martin's Advanced Technology Center (ATC) in Palo Alto and its subcontractor, ATK. One of the goals of Phase 1 was to develop an MRHE concept demonstration in a relevant 1-g environment to highlight a number of requisite technologies. In Phase 1 of the MRHE program, Lockheed Martin devised and conducted an automated space system assembly demonstration featuring multipurpose free-floating robots representing Spacecraft in the newly built Controls and Automation Laboratory (CAL) at the ATC. The CAL lab features a 12' x 24' granite air-bearing table and an overhead simulated starfield. Among the technologies needed for the concept demo were mating interfaces allowing the spacecraft to dock and deployable structures allowing for adjustable separation between spacecraft after a rigid connection had been established. The decision to use a nonmetallic deployable boom for this purpose was driven by the MRHE concept demo requirements reproduced in Table 1.

Rehnmark, Fredrik↗
NASA NTRS2020

Wildfire Smoke Particle Properties and Evolution, From Space-Based Multi-Angle Imaging II: The Williams Flats Fire during the FIREX-AQ Campaign

Although the characteristics of biomass burning events and the ambient ecosystem determine emitted smoke composition, the conditions that modulate the partitioning of black carbon (BC) and brown carbon (BrC) formation are not well understood, nor are the spatial or temporal frequency of factors driving smoke particle evolution, such as hydration, coagulation, and oxidation, all of which impact smoke radiative forcing. In situ data from surface observation sites and aircraft field campaigns offer deep insight into the optical, chemical, and microphysical traits of biomass burning (BB) smoke aerosols, such as single scattering albedo (SSA) and size distribution, but cannot by themselves provide robust statistical characterization of both emitted and evolved particles. Data from the NASA Earth Observing System’s Multi-Angle Imaging SpectroRadiometer (MISR) instrument can provide at least a partial picture of BB particle properties and their evolution downwind, once properly validated. Here we use in situ data from the joint NOAA/NASA 2019 Fire Influence on Regional to Global Environments Experiment-Air Quality (FIREX-AQ) field campaign to assess the strengths and limitations of MISR-derived constraints on particle size, shape, light-absorption, and its spectral slope, as well as plume height and associated wind vectors. Based on the satellite observations, we also offer inferences about aging mechanisms effecting downwind particle evolution, such as gravitational settling, oxidation, secondary particle formation, and the combination of particle aggregation and condensational growth. This work builds upon our previous study, adding confidence to our interpretation of the remote-sensing data based on an expanded suite of in situ measurements for validation. The satellite and in situ measurements offer similar characterizations of particle property evolution as a function of smoke age for the 06 August Williams Flats Fire, and most of the key differences in particle size and absorption can be attributed to differences in sampling and changes in the plume geometry between sampling times. Whereas the aircraft data provide validation for the MISR retrievals, the satellite data offer a spatially continuous mapping of particle properties over the plume, which helps identify trends in particle property downwind evolution that are ambiguous in the sparsely sampled aircraft transects. The MISR data record is more than two decades long, offering future opportunities to study regional wildfire plume behavior statistically, where aircraft data are limited or entirely lacking.

biomass burning↗
DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

SDSS-IV MaNGA: Bayesian analysis of the star formation history of low-mass galaxies in the local Universe

ABSTRACT We measure the star formation histories (SFHs) of a sample of low-mass galaxies with M* < 109 M⊙ from the SDSS-IV MaNGA survey. The large number of IFU spectra for each galaxy are either combined to reach a high signal to noise ratio or used to investigate spatial variations. We use Bayesian inferences based on full spectrum fitting. Our analysis based on Bayesian evidence ratio indicates a strong preference for a model that allows the presence of an old stellar population, and that an improper model for the SFH can significantly underestimate the old population in these galaxies. The addition of near-infrared photometry to the constraining data can further distinguish between different SFH model families and significantly tighten the constraints on the mass fraction in the old population. On average more than half of the stellar mass in present-day low-mass galaxies formed at least 8 Gyr ago, while about 30 per cent within the past 4 Gyr. Satellite galaxies on average have formed their stellar mass earlier than central galaxies. The radial dependence of the SFH is quite weak. Our results suggest that most of the low-mass galaxies have an early episode of active star formation that produces a large fraction of their present stellar mass.

Zhou, Shuang↗
DOE OSTI2023

Lieb-Schultz-Mattis, Luttinger, and 't Hooft - anomaly matching in lattice systems

We analyze lattice Hamiltonian systems whose global symmetries have ’t Hooft anomalies. As is common in the study of anomalies, they are probed by coupling the system to classical background gauge fields. For flat fields (vanishing field strength), the nonzero spatial components of the gauge fields can be thought of as twisted boundary conditions, or equivalently, as topological defects. The symmetries of the twisted Hilbert space and their representations capture the anomalies. We demonstrate this approach with a number of examples. In some of them, the anomalous symmetries are internal symmetries of the lattice system, but they do not act on-site. (We clarify the notion of “on-site action.”) In other cases, the anomalous symmetries involve lattice translations. Using this approach we frame many known and new results in a unified fashion. In this work, we limit ourselves to 1+1d systems with a spatial lattice. In particular, we present a lattice system that flows to the c=1 c = 1 compact boson system with any radius (no BKT transition) with the full internal symmetry of the continuum theory, with its anomalies and its T-duality. As another application, we analyze various spin chain models and phrase their Lieb-Shultz-Mattis theorem as an ’t Hooft anomaly matching condition. We also show in what sense filling constraints like Luttinger theorem can and cannot be viewed as reflecting an anomaly. As a by-product, our understanding allows us to use information from the continuum theory to derive some exact results in lattice model of interest, such as the lattice momenta of the low-energy states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1988

ORFEUS focal plane instrumentation: The Berkeley spectrometer

A spectrograph for the ORFEUS mission that incorporates four varied line-space, spherically figured diffraction gratings was designed. The ORFEUS, a 1-m normal incidence telescope is equipped with 2 focal plane spectrographs. The Berkeley spectrograph was developed with an optimizing raytracing computer code. Each grating accepts the light from 20 percent of the aperture of the telescope primary mirror and has a unique set of characteristics to cover a sub-bandpass within the 390 to 1200 A spectral range. Two photon-counting detectors incorporating a time delay readout system are used to record the spectra from all four gratings simultaneously. The nominal design achieves a spectral resolution (FWHM) in excess of 5500 at all wavelengths within the bandpass. The resolution is limited primarily by the detector spatial resolution. The 1 sigma astigmatism of this design varies between 13 and 150 micrometer on the same focal surface. An independent, direct imaging system tracks the drift of the target within the spectrometer aperture and allows measurement of the misalignment between the telescope optical axis and that of the external star tracker. The resolution and astigmatism achievable with this design are superior to those of a standard Rowland spectrograph designed with the same constraints.

Hurwitz, Mark↗
NASA NTRS2003

A New Fitting Procedure for the Blended He I 1083 nm Multiplet

The He I 1083 nm multiplet is a powerful tool for observing the outer solar atmosphere but is difficult to analyze because the lines are weak, highly variable, and spectrally blended, both internally and with other neighboring solar and telluric lines. After separation from nearby spectral features, two components of the He I multiplet are resolved. Fitting these lines with two unconstrained Gaussian profiles always gives a ratio of major to minor component of less than half the value which would be expected for optically thin lines. One possibility for explaining the discrepancy between the weakness of the lines and the ratio of the spectral components is to assume that the line formation regions are concentrated in laterally unresolved, optically thick structures with small filling factor. However, we present here a least-squares fitting technique using cubic splines with fixed breakpoints with the constraint that the blend is the sum of three identically shaped profiles shifted in wavelength according to the atomic structure of the blend and weighted by the corresponding statistics weights, in agreement with optically thin line formation. The basis functions for the fitting procedure have no built-in spectral symmetry or shape. The resulting underlying profiles tend to be asymmetric with excess absorption to the blue, consistent with formation by "hot" and "cool" spatial elements within the observed volume, with the hotter regions having differential motion toward the observer. The results agree well with NASA/XSO Spectromagnetograph observations in quiet sun and coronal holes. Partial funding of this research was provided through the NASA Sun-Earth Connection SR&T program.

Jones, H. P.↗
NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 6

Contents include the following: A Model for Multiple Populations of Presolar Diamonds. Characterization of Martian North Polar Geologic Units Using Mars Odyssey THEMIS Data. Effect of Flow on the Internal Structure of the Martian North Polar Layered Deposits. Elemental Abundance Distributions in Basalt Clays and Meteorites: Is It a Biosignature? Early Results on the Saturn System from the Composite Infrared Spectrometer. NanoSIMS D/H Imaging of Isotopically Primitive Interplanetary Dust Particles. Presolar (Circumstellar and Interstellar) Phases in Renazzo: The Effects of Parent Body Processing. Catastrophic Disruption of Hydrated Targets: Implications for the Hydrated Asteroids and for the Production of Interplanetary Dust Particles. Chemical and Mineralogical Analyses of Particles from the Stratospheric Collections Coinciding with the 2002 Leonid Storm and the 2003 Comet Grigg-Skjellerup Trail Passage. An Analysis of the Solvus in the CaS-MnS System. ESA s SMART-1 Mission at the Moon: First Results, Status and Next Steps. Europa Analog Ice-splitting Measurements and Experiments with Ice-Hunveyor on the Frozen Balaton-Lake, Hungary. Chromium on Eros: Further Evidence of Ordinary Chondrite Composition. Dust Devil Tracks on Mars: Observation and Analysis from Orbit and the Surface. Spatial Variation of Methane and Other Trace Gases Detected on Mars: Interpretation with a General Circulation Model. Mars Water Ice and Carbon Dioxide Seasonal Polar Caps: GCM Modeling and Comparison with Mars Express Omega Observations. Component Separation of OMEGA Spectra with ICA. Clathrate Formation in the Near-Surface Environment of Titan. Space Weathering: A Proposed Laboratory Approach to Explaining the Sulfur Depletion on Eros. Sample Collection from Small Airless Bodies: Examination of Temperature Constraints for the TGIP. Sample Collector for the Hera Near-Earth Asteroid Sample Return Mission. A Rugged Miniature Mass-Spectrometer for Measuring Aqueous Geochemistry on Mars. Martian and Lunar Pyroxene Microstructures Studied by Single-Crystal X-Ray Diffraction.

Source record↗
NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗
DOE OSTI2020

Nearest neighbour distributions: New statistical measures for cosmological clustering

ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

A function space approach to state and model error estimation for elliptic systems

An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed.

Rodriguez, G.↗
NASA NTRS1983

A function space approach to state and model error estimation for elliptic systems

An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal esimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed. While this paper focuses on theoretical developments, applications arising in the area of large structure static shape determination are contained in a closely related paper (Rodriguez and Scheid, 1982).

Rodriguez, G.↗
NASA NTRS1990

Visions of visualization aids: Design philosophy and experimental results

Aids for the visualization of high-dimensional scientific or other data must be designed. Simply casting multidimensional data into a two- or three-dimensional spatial metaphor does not guarantee that the presentation will provide insight or parsimonious description of the phenomena underlying the data. Indeed, the communication of the essential meaning of some multidimensional data may be obscured by presentation in a spatially distributed format. Useful visualization is generally based on pre-existing theoretical beliefs concerning the underlying phenomena which guide selection and formatting of the plotted variables. Two examples from chaotic dynamics are used to illustrate how a visulaization may be an aid to insight. Two examples of displays to aid spatial maneuvering are described. The first, a perspective format for a commercial air traffic display, illustrates how geometric distortion may be introduced to insure that an operator can understand a depicted three-dimensional situation. The second, a display for planning small spacecraft maneuvers, illustrates how the complex counterintuitive character of orbital maneuvering may be made more tractable by removing higher-order nonlinear control dynamics, and allowing independent satisfaction of velocity and plume impingement constraints on orbital changes.

Ellis, Stephen R.↗
NASA NTRS2002

A Sensitive VLA Search for Small-Scale Glycine Emission Toward OMC-1

We have conducted a deep Q-band (lambda-7 mm) search with the Very Large Array (VLA) toward OMC-1 for the lowest energy conformation (conformer I) of glycine (NH2CH2COOH) in four rotational transitions: the 6(sub 15)- 5(sub 14), 6(sub 24)-5(sub 23), 7(sub 17- 6(sub 16), and 7(sub 07)-6(sub 06). Our VLA observations sample the smallest-scale structures to date in the search for glycine toward OMC-1. No glycine emission features were detected. Thus if glycine exists in OMC-1, either it is below our detection limit, or it is more spatially extended than other large molecules in this source, or it is primarily in its high energy form (conformer II). Our VLA glycine fractional abundance limits in OMC-1 are comparable to those determined from previous IRAM 30m measurements -- somewhat better or worse depending on the specific source model -- and the entire approximately 1 foot primary beam of the VLA was searched while sensitive to an areal spatial scale approximately 150 times smaller than the 24 inch beam of the IRAM single-element telescope. In the course of this work, we detected and imaged the 4(sub 14)-3(sub 13) A and E transitions of methyl formate (HCOOCH3) and also the 2(sub 02) - 1(sub 01) transition of formic acid (HCOOH). Since formic acid is a possible precursor to glycine, our glycine limits and formic acid results provide a constraint on this potential formation chemistry route for glycine in OMC-1.

Hollis, J. M.↗
DOE OSTI2020

Using quasar X-ray and UV flux measurements to constrain cosmological model parameters

ABSTRACT Risaliti and Lusso have compiled X-ray and UV flux measurements of 1598 quasars (QSOs) in the redshift range 0.036 ≤ z ≤ 5.1003, part of which, z ∼ 2.4 − 5.1, is largely cosmologically unprobed. In this paper we use these QSO measurements, alone and in conjunction with baryon acoustic oscillation (BAO) and Hubble parameter [H(z)] measurements, to constrain cosmological parameters in six different cosmological models, each with two different Hubble constant priors. In most of these models, given the larger uncertainties, the QSO cosmological parameter constraints are mostly consistent with those from the BAO + H(z) data. A somewhat significant exception is the non-relativistic matter density parameter Ωm0 where QSO data favour Ωm0 ∼ 0.5 − 0.6 in most models. As a result, in joint analyses of QSO data with H(z) + BAO data the 1D Ωm0 distributions shift slightly towards larger values. A joint analysis of the QSO + BAO + H(z) data is consistent with the current standard model, spatially-flat ΛCDM, but mildly favours closed spatial hypersurfaces and dynamical dark energy. Since the higher Ωm0 values favoured by QSO data appear to be associated with the z ∼ 2 − 5 part of these data, and conflict somewhat with strong indications for Ωm0 ∼ 0.3 from most z < 2.5 data as well as from the cosmic microwave background anisotropy data at z ∼ 1100, in most models, the larger QSO data Ωm0 is possibly more indicative of an issue with the z ∼ 2 − 5 QSO data than of an inadequacy of the standard flat ΛCDM model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Linear point and sound horizon as purely geometric standard rulers

The baryon acoustic oscillations feature (BAO) imprinted in the clustering correlation function is known to furnish us cosmic distance determinations that are independent of the cosmological-background model and the primordial perturbation parameters. These measurements can be accomplished rigorously by means of the purely geometric BAO methods. To date two different purely geometric BAO approaches have been proposed. The first exploits the linear-point standard ruler. The second, called correlation-function model-fitting, exploits the sound-horizon standard ruler. A key difference between them is that, when estimated from clustering data, the linear point makes use of a cosmological-model-independent procedure to extract the ratio of the ruler to the cosmic distance, while the correlation-function model-fitting relies on a phenomenological cosmological model for the correlation function. Nevertheless the two rulers need to be precisely defined independently of any specific observable (e.g., the BAO). We define the linear point and sound horizon and we fully characterize and compare the two rulers’ cosmological-parameter dependence. We find that they are both geometrical (i.e., independent of the primordial cosmological parameters) within the required accuracy, and that they have the same parameter dependence for a wide range of parameter values. We estimate the rulers’ best-fit values and errors, given the cosmological constraints obtained by the Planck Satellite team from their measurements of the cosmic microwave background temperature and polarization anisotropies. We do this for three different cosmological models encompassed by the purely geometric BAO methods. In each case we find that the relative errors of the two rulers coincide and they are insensitive to the assumed cosmological model. Interestingly both the linear point and the sound horizon shift by 0.5σ when we do not fix the spatial geometry to be flat in Λ CDM. Finally, this points toward a sensitivity of the rulers to different cosmological models when they are estimated from the cosmic microwave background.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.

Almeida, Sílvia (ORCID:0000000320636826)↗
NASA NTRS2014

Searching for Dark Matter Annihilation in the Smith High-Velocity Cloud

Recent observations suggest that some high-velocity clouds may be confined by massive dark matter halos. In particular, the proximity and proposed dark matter content of the Smith Cloud make it a tempting target for the indirect detection of dark matter annihilation. We argue that the Smith Cloud may be a better target than some Milky Way dwarf spheroidal satellite galaxies and use gamma-ray observations from the Fermi Large Area Telescope to search for a dark matter annihilation signal. No significant gamma-ray excess is found coincident with the Smith Cloud, and we set strong limits on the dark matter annihilation cross section assuming a spatially extended dark matter profile consistent with dynamical modeling of the Smith Cloud. Notably, these limits exclude the canonical thermal relic cross section (approximately 3 x 10 (sup −26) cubic centimeters per second) for dark matter masses less than or approximately 30 gigaelectronvolts annihilating via the B/B- bar oscillation or tau/antitau channels for certain assumptions of the dark matter density profile; however, uncertainties in the dark matter content of the Smith Cloud may significantly weaken these constraints.

gamma rays: ISM aEuro" ISM: clouds↗