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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

VERY HIGH ENERGY γ-RAYS FROM THE UNIVERSE’S MIDDLE AGE: DETECTION OF THE z = 0.940 BLAZAR PKS 1441+25 WITH MAGIC

The flat-spectrum radio quasar PKS 1441+25 at a redshift of z = 0.940 is detected between 40 and 250 GeV with a significance of 25.5σ using the MAGIC telescopes. Together with the gravitationally lensed blazar QSO B0218+357 (z = 0.944), PKS 1441+25 is the most distant very high energy (VHE) blazar detected to date. The observations were triggered by an outburst in 2015 April seen at GeV energies with the Large Area Telescope on board Fermi. Multi-wavelength observations suggest a subdivision of the high state into two distinct flux states. In the band covered by MAGIC, the variability timescale is estimated to be 6.4 ± 1.9 days. Modeling the broadband spectral energy distribution with an external Compton model, the location of the emitting region is understood as originating in the jet outside the broad-line region (BLR) during the period of high activity, while being partially within the BLR during the period of low (typical) activity. The observed VHE spectrum during the highest activity is used to probe the extragalactic background light at an unprecedented distance scale for ground-based gamma-ray astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Influence of explicit Phaeocystis parameterizations on the global distribution of marine dimethyl sulfide

Dimethyl sulfide (DMS) is a biogenic organosulfur compound which contributes strongly to marine aerosol mass and the determination of cloud condensation nuclei over the remote oceans. Since uncertainties in DMS flux to the atmosphere lead to large variations in climate forcing, the global DMS distribution has been the subject of increasingly complex dynamic simulations. DMS concentrations are directly controlled by marine ecosystems. Phaeocystis is a major DMS producer but is often omitted from global reduced sulfur mechanisms. Here we incorporate this phytoplankton group into the marine ecosystem-biogeochemical module of the Community Earth System Model. To examine its role in the ocean sulfur cycle, an earlier DMS model has been enhanced to include new knowledge gained over the last few years. Results from the baseline run show that simulated Phaeocystis biomass generally agrees with observations, with high concentrations near the Antarctic continent and between 50° and 60° north. Given the new explicit Phaeocystis representation, the DMS distribution shows significant improvements, especially regarding the amplitude and location of high-latitude peaks. The simulated global mean surface DMS value is 2.26 nM, comparable to an estimate of 2.34 nM from the latest climatology extrapolated based on observations. The total oceanic DMS source to the atmosphere is 20.4 Tg S/yr, on the low side of previous estimates. Comparisons with and without Phaeocystis show that the group dominates DMS distributions in temperate and cold waters, contributing 13% of the global flux. The proportion may increase as sea ice declines and should be considered in climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Bias-hardened estimators of patchy screening profiles

Detecting anisotropic screening of the cosmic microwave background (CMB) holds the promise of revealing the distribution of gas in the Universe, characterizing the complex processes of galaxy formation and feedback, and studying the epoch of reionization. Estimators for inhomogeneous screening, including some recently proposed small-scale (stacked) estimators, are quadratic or higher order in the CMB temperature or polarization fields and are therefore subject to contamination from CMB lensing. We review the origin of this lensing bias and show that, when stacking on unWISE galaxies, the expected lensing bias dominates the signal if left unmitigated. Hardening techniques that null the lensing bias have been proposed for standard quadratic estimators, whereas only approximate methods have been proposed for stacked estimators. In conclusion, we review these techniques and apply the former to stacked estimators, presenting several strategies (including the optimal strategy) to null lensing contamination when stacking on any large-scale structure tracer.

Cosmic microwave background↗
DOE OSTI2015

Breast dose in mammography is about 30% lower when realistic heterogeneous glandular distributions are considered

Purpose: Current dosimetry methods in mammography assume that the breast is comprised of a homogeneous mixture of glandular and adipose tissues. Three-dimensional (3D) dedicated breast CT (bCT) data sets were used previously to assess the complex anatomical structure within the breast, characterizing the statistical distribution of glandular tissue in the breast. The purpose of this work was to investigate the effect of bCT-derived heterogeneous glandular distributions on dosimetry in mammography. Methods: bCT-derived breast diameters, volumes, and 3D fibroglandular distributions were used to design realistic compressed breast models comprised of heterogeneous distributions of glandular tissue. The bCT-derived glandular distributions were fit to biGaussian functions and used as probability density maps to assign the density distributions within compressed breast models. The MCNPX 2.6.0 Monte Carlo code was used to estimate monoenergetic normalized mean glandular dose “DgN(E)” values in mammography geometry. The DgN(E) values were then weighted by typical mammography x-ray spectra to determine polyenergetic DgN (pDgN) coefficients for heterogeneous (pDgN{sub hetero}) and homogeneous (pDgN{sub homo}) cases. The dependence of estimated pDgN values on phantom size, volumetric glandular fraction (VGF), x-ray technique factors, and location of the heterogeneous glandular distributions was investigated. Results: The pDgN{sub hetero} coefficients were on average 35.3% (SD, 4.1) and 24.2% (SD, 3.0) lower than the pDgN{sub homo} coefficients for the Mo–Mo and W–Rh x-ray spectra, respectively, across all phantom sizes and VGFs when the glandular distributions were centered within the breast phantom in the coronal plane. At constant breast size, increasing VGF from 7.3% to 19.1% lead to a reduction in pDgN{sub hetero} relative to pDgN{sub homo} of 23.6%–27.4% for a W–Rh spectrum. Displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width in the superior and inferior directions, resulted in a 37.3% and a −26.6% change in the pDgN{sub hetero} coefficient, respectively, relative to the centered distribution for the Mo–Mo spectrum. Lateral displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width, resulted in a 1.5% change in the pDgN{sub hetero} coefficient relative to the centered distribution for the W–Rh spectrum. Conclusions: Introducing bCT-derived heterogeneous glandular distributions into mammography phantom design resulted in decreased glandular dose relative to the widely used homogeneous assumption. A homogeneous distribution overestimates the amount of glandular tissue near the entrant surface of the breast, where dose deposition is exponentially higher. While these findings are based on clinically measured distributions of glandular tissue using a large cohort of women, future work is required to improve the classification of glandular distributions based on breast size and overall glandular fraction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Regional Inertia Estimation Using Actual Event Measurements: Florida Case

As inverter-based resources’ integration in power grids increases, their uneven distribution across the electrical network leads to the formation of local regions that are weakly coupled to the larger interconnection. This signifies the necessity of regional frequency dynamics investigation and analyzing various inertia metrics. This paper presents a practical methodology for estimating regional rate-of-change of frequency (RoCoF) using actual event recordings, which is then used to evaluate regional inertia. Florida (FL) is selected as the region of interest due to its distinct regional frequency dynamics and the rising levels of solar generation. We identify and analyze confirmed events from 2017 to 2024 that occurred in FL. The results indicate that FL contributes about 14% to the total inertia of the US Eastern Interconnection, which approximately matches its share of generation capacity. Results also highlight seasonal fluctuations in energy generation, which play a significant role in influencing inertia and thus the RoCoF levels. This emphasizes the importance of estimating regional inertia to enhance grid operations for a future that focuses on distributed generation.

Dulal, Saurav [University of Tennessee, Knoxville ↗
DOE OSTI2019

Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection

Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Resilient Reinforcement in Secure State Estimation against Sensor Attacks with a priori Information

This paper deals with the state estimation problem in the presence of malicious sensor attacks. Unlike the existing work, in this paper, we address the problem with a priori information of the state to be estimated. Specifically, we address three prior knowledge: the sparsity information, (alpha, n_0)-sparsity information, and side information, and in each scenario, we show that the state can be reconstructed even if more sensors are compromised. This implies that the prior information reinforces the system resilience against sensor attacks. Then, an estimator under sensor attacks considering the information is developed and, under a certain condition, the estimator can be relaxed into a tractable convex optimization problem. Further, we extend this analysis to systems in presence of measurement noises, and it is shown that the prior information reduces the state-estimation error caused by the noise. Here, the numerical simulations in a diffusion process finally illustrate the reinforcement and error-reduction results with the information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC

Abstract. Improved quantification of sea spray aerosol concentration and size is important for determining aerosol effects on clouds and the climate, though attempts to accurately capture the size distribution of the sea spray mode remain limited by the availability of supermicron size distributions. In this work, we introduce a new approach to retrieving lognormal mode fit parameters for a sea spray aerosol mode by combining submicron size distributions with supermicron scattering measurements using a Mie inversion. Submicron size distributions were measured by an ultra-high-sensitivity aerosol spectrometer (UHSAS), and supermicron scattering was taken as the difference between <10 µm and <1 µm three-wavelength integrating nephelometer measurements (NEPH). This UHSAS-NEPH method was applied during background marine periods of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign on Ascension Island (November 2016–May 2017), when the contribution of sea spray aerosol was expected to represent a large fraction of the aerosol mass and total scattering. Lognormal sea spray modal parameters were retrieved from comparisons between nephelometer measurements and a lookup table of Mie theory-simulated scattering coefficients for low-error solutions that minimized the 0.4–1 µm residual in the UHSAS size distribution. We evaluated the UHSAS-NEPH method with a set of clean marine measurements in the North Atlantic that included supermicron size and chemical measurements, showing that measured supermicron size distributions are needed to constrain the sea spray number concentration but that mass concentration was reasonably characterized using supermicron scattering. For LASIC, the UHSAS-NEPH method retrieved sea spray mode properties for approximately 88 % of the background marine times when the scattering variability and total particle concentration were low (<± 5 Mm−1 and <400 cm−3, respectively), with mass mean diameter ranging from 0.6 to 1.9 µm (1.47 ± 0.17 µm), modal width ranging from 1.1 to 3.97 (2.4±0.3), and mass concentration ranging from 0.18 to 23.0 µg m−3 (8.37. ± 4.1 µg m−3). The measured nephelometer scattering at three wavelengths was found to constrain the mode width marginally at the largest particle sizes in the absence of additional size and chemical measurements for defining parameters for the Mie solutions. Comparing UHSAS-NEPH retrievals to those of a fitting algorithm applied only to the submicron UHSAS number size distribution showed that correlations between retrieved mass concentration and the available mass-based sea spray tracers (coarse scattering, wind speed, and chloride) are low when supermicron measurements are not considered. This work demonstrates the added value of supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available observationally constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Meta-uncertainty for particle image velocimetry

Uncertainty quantification for Particle Image Velocimetry (PIV) is critical for comparing experimentally measured flow fields with Computational Fluid Dynamics (CFD) results, and model design and validation. However, PIV features a complex measurement chain with coupled, non-linear error sources, and quantifying the uncertainty is challenging. Multiple assessments show that none of the current methods can reliably measure the actual uncertainty across a wide range of experiments, and estimates can vary. Because the current methods differ in assumptions regarding the measurement process and calculation procedures, it is not clear which method is best to use for an experiment where the error distribution is unknown. To address this issue, we propose a method to estimate an uncertainty method's sensitivity and reliability, termed the Meta-Uncertainty. The novel approach is automated, local, and instantaneous, and based on perturbation of the recorded particle images. We developed an image perturbation scheme based on adding random unmatched particles to the interrogation window pair considering the signal-to-noise (SNR) of the correlation plane. Each uncertainty scheme's response to several trials of random particle addition is used to estimate a reliability metric, defined as the rate of change of the inter-quartile range (IQR) of the uncertainties with increasing levels of particle addition. We also propose applying the meta-uncertainty as a weighting metric to combine uncertainty estimates from individual schemes, based on ideas from the consensus forecasting literature. We use planar and stereo PIV measurements across a range of canonical flows to assess the performance of the uncertainty schemes. Further, a novel method is introduced to assess an uncertainty scheme's performance based on a quantile comparison of the error and uncertainty distributions, generalizing the current method of comparing the RMS of the two distributions. Here, the results show that the combined uncertainty method outperforms the individual methods, and this work establishes the meta-uncertainty as a useful reliability assessment tool for PIV uncertainty quantification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Data-Driven Method for Estimating Behind-the-Meter Photovoltaic Generation in Hawaii

Due to the increasing penetration of distributed behind-the-meter photovoltaic (PV) systems and the installed utility revenue metering limited to monitoring only the net power import/export of the household, it is increasingly challenging for utilities to effectively plan and operate the grid. This paper proposes a methodology that estimates behind-the-meter PV generation using a selected subset of monitored PV systems. It is a data-driven approach, and the PV output is estimated utilizing a statistic regression model. A Minimum Redundancy Maximum Relevance (MRMR) algorithm is applied to preselect the optimal subset of the monitored PV systems. The performance of this approach is compared with a spatial interpolation method and a model-based approach. The proposed method is validated using high-resolution meter data recorded from 18 residential rooftop PV systems located on the island of Maui, Hawaii.

Data-driven modeling↗
DOE OSTI2023

Parameter estimation with maximal updated densities

A recently developed measure-theoretic framework solves a stochastic inverse problem (SIP) for models where uncertainties in model output data are predominantly due to aleatoric (i.e., irreducible) uncertainties in model inputs (i.e., parameters). The subsequent inferential target is a distribution on parameters. Another type of inverse problem is to quantify uncertainties in estimates of “true” parameter values under the assumption that such uncertainties should be reduced as more data are incorporated into the problem, i.e., the uncertainty is considered epistemic. A major contribution of this work is the formulation and solution of such a parameter identification problem (PIP) within the measure-theoretic framework developed for the SIP. The approach is novel in that it utilizes a solution to a stochastic forward problem (SFP) to update an initial density only in the parameter directions informed by the model output data. In other words, this method performs “selective regularization” only in the parameter directions not informed by data. Further, the solution is defined by a maximal updated density (MUD) point where the updated density defines the measure-theoretic solution to the PIP. Another significant contribution of this work is the full theory of existence and uniqueness of MUD points for linear maps with Gaussian distributions. Data-constructed Quantity of Interest (QoI) maps are also presented and analyzed for solving the PIP within this measure-theoretic framework as a means of reducing uncertainties in the MUD estimate. We conclude with a demonstration of the general applicability of the method on two problems involving either spatial or temporal data for estimating uncertain model parameters. The first problem utilizes spatial data from a stationary partial differential equation to produce a MUD estimate of an uncertain boundary condition. The second problem utilizes temporal data obtained from the state-of-the-art ADvanced CIRCulation (ADCIRC) model to obtain a MUD estimate of uncertain wind drag coefficients for a simulated extreme weather event near the Shinnecock Inlet located in the Outer Barrier of Long Island, NY, USA.

42 ENGINEERING↗
DOE OSTI2018

Measurements of I/SVOCs in biomass-burning smoke using solid-phase extraction disks and two-dimensional gas chromatography

Biomass-burning organic-aerosol (OA) emissions are known to exhibit semi-volatile behavior that impacts OA loading during plume transport. Because such semi-volatile behavior depends in part on OA composition, improved speciation of intermediate and semi-volatile organic compounds (I/SVOCs) emitted during fires is needed to assess the competing effects of primary OA volatilization and secondary OA production. In this study, 18 laboratory fires were sampled in which a range of fuel types were burned. Emitted I/SVOCs were collected onto Teflon filters and solid-phase extraction (SPE) disks to qualitatively characterize particulate and gaseous I/SVOCs, respectively. Derivatized filter extracts were analyzed using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS). Quality control tests were performed using biomass-burning relevant standards and demonstrate the utility of SPEdisks for untargeted analysis of air samples. The observed chromatographic profiles of I/SVOCs in coniferous fuel-derived smoke samples were well correlated with each other, but poorly correlated with other fuel types(e.g., herbaceous and chaparral fuels). Emissions of benzenediol isomers were also shown to be fuel dependent. The combined Teflon and SPE filter data captured differences in gas-particle partitioning of the benzene diolisomers, with hydroquinone having a significantly higher particle-phase fraction than catechol due to its lower volatility. Additionally, the speciated volatility distribution of I/SVOCs in smoke from a rotten-log fire was estimated to evaluate the composition of potentially volatilized primary OA, which was entirely attributed to oxygenated (or other heteroatomic) compounds. The isomer-dependent partitioning and the speciated volatility distributions both suggest the need for better understanding of gas-phase and heterogenous reaction pathways of biomass-burning-derived I/SVOCs in order to represent the atmospheric chemistry of smoke in models.

09 BIOMASS FUELS↗
DOE OSTI2019

The Shape and Density Evolution of Snow Aggregates

Abstract Snow aggregates evolve into a variety of observed shapes and densities. Despite this diversity, models and observational studies employ fractal or Euclidean geometric measures that are assumed universal for all aggregates. This work therefore seeks to improve understanding and representation of snow aggregate geometry and its evolution by characterizing distributions of both observed and Monte Carlo–generated aggregates. Two separate datasets of best-fit ellipsoid estimates derived from Multi-Angle Snowflake Camera (MASC) observations suggest the use of a bivariate beta distribution model for capturing aggregate shapes. Product moments of this model capture shape effects to within 4% of observations. This mathematical model is used along with Monte Carlo simulated aggregates to study how combinations of monomer properties affect aggregate shape evolution. Plate aggregates of any aspect ratio produce a consistent ellipsoid shape evolution whereas thin column aggregates evolve to become more spherical. Thin column aggregates yield fractal dimensions much less than the often-assumed value of 2.0. Ellipsoid densities and fractal analogs of density (lacunarity) are much more variable depending on combinations of monomer size and shape. Simple mathematical scaling relationships can explain the persistent triaxial ellipsoid shapes that appear in both observed and modeled aggregates. Overall, both simulations and observations prove aggregates are rarely oblate. Therefore, the use of the proposed bivariate ellipsoid distribution in models will allow for similar-sized aggregates to exhibit a realistic dispersion of masses and fall speeds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Frequency Event Categorization in Power Distribution Systems Using Micro PMU Measurements

Frequency is an important metric for power stability. To enhance the situational awareness in power distribution systems, this article proposes a data-driven approach that utilizes the data from micro phasor measurement units (μPMU) to categorize frequency events observed in distribution feeders. The proposed approach has three layers, each of which analyzes if a frequency event is propagated from the transmission system, formed inside a distribution feeder or falsely produced by instantaneous frequency (IF) estimation algorithms, respectively. In the first layer, the Granger causality is utilized to compare the causal relations from measurements across different locations to decide if an event is propagated from the transmission side. In the second layer, a trained sparse coding dictionary is utilized to unmix the physical causes of a frequency event. In the third layer, a moving z-score based event-pair detector is applied to identify if a frequency event is algorithm-induced. The proposed approach is tested using the μPMU measurements collected from Lawrence Berkeley National Laboratory (LBNL) and Riverside Public Utility (RPU).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Characterization of an aluminum alloy hemispherical shell fabricated via direct metal laser melting

The ability of additive manufacturing to directly fabricate complex shapes provides characterization challenges for part qualification. The orientation of the microstructures produced by these processes will change relative to the surface normal of a complex part. In this work, the microscopy and x-ray tomography of an AlSi10Mg alloy hemispherical shell fabricated using powder bed metal additive manufacturing are used to illustrate some of these challenges. The shell was manufactured using an EOS M280 system in combination with EOS-specified powder and process parameters. The layer-by-layer process of building the shell with the powder bed additive manufacturing approach results in a position-dependent microstructure that continuously changes its orientation relative to the shell surface normal. X-ray tomography was utilized to examine the position-dependent size and distribution of porosity and surface roughness in the 98.6% dense part. Optical and electron microscopy were used to identify global and local position-dependent structures, grain morphologies, chemistry, and precipitate sizes and distributions. The rapid solidification processes within the fusion zone (FZ) after the laser transit results in a small dendrite size. Cell spacings taken from the structure in the middle of the FZ were used with published relationships to estimate a cooling rate of ~9 × 10 5 K/s. Uniformly-distributed, nanoscale Si precipitates were found within the primary α-Al grains. A thin, distinct boundary layer containing larger α-Al grains and extended regions of the nanocrystalline divorced eutectic material surrounds the FZ. Moreover, subtle differences in the composition between the latter layer and the interior of the FZ were noted with scanning transmission electron microscopy (STEM) spectral imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Comparision of the different probability distributions for earthquake hazard assessment in the North Anatolian Fault Zone

In this study we examined and compared the three different probabilistic distribution methods for determining the best suitable model in probabilistic assessment of earthquake hazards. We analyzed a reliable homogeneous earthquake catalogue between a time period 1900-2015 for magnitude M ≥ 6.0 and estimated the probabilistic seismic hazard in the North Anatolian Fault zone (39°-41° N 30°-40° E) using three distribution methods namely Weibull distribution, Frechet distribution and three-parameter Weibull distribution. The distribution parameters suitability was evaluated Kolmogorov-Smirnov (K-S) goodness-of-fit test. We also compared the estimated cumulative probability and the conditional probabilities of occurrence of earthquakes for different elapsed time using these three distribution methods. We used Easyfit and Matlab software to calculate these distribution parameters and plotted the conditional probability curves. We concluded that the Weibull distribution method was the most suitable than other distribution methods in this region.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

The Power of DESI for Photometric Redshift Calibration: A Case Study with KiDS-1000

Accurate redshift estimates are a critical requirement for weak lensing surveys and one of the main uncertainties in constraints on dark energy and large-scale cosmic structure. In this paper, we study the potential to calibrate photometric redshift (photo-z) distributions for gravitational lensing using the Dark Energy Spectroscopic Instrument (DESI). Since beginning its science operations in 2021, DESI has collected more than 50 million redshifts, adding about one million monthly. In addition to its large-scale structure samples, DESI has also acquired over 256k high-quality spectroscopic redshifts (spec-zs) in the COSMOS and XMM and VVDS fields. This is already a factor of 3 larger than previous spec-z calibration compilations in these two regions. Here, we explore calibrating photo-zs for the subset of KiDS-1000 galaxies that fall into joint self-organizing map (SOM) cells overlapping the DESI COSMOS footprint using the DESI COSMOS observations. Estimating the redshift distribution in KiDS-1000 with the new DESI data, we find broad consistency with previously published results while also detecting differences in the mean redshift in some tomographic bins with an average shifts of Delta Mean(z) = -0.028 in the mean and Delta Median(z) = +0.011 in the median across tomographic bins. However, we also find that incompleteness per SOM cell, i.e., groups of galaxies with similar colors and magnitudes, can modify n(z) distributions. Finally, we comment on the fact that larger photometric catalogs, aligned with the DESI COSMOS and DESI XMM and VVDS footprints, would be needed to fully exploit the DESI dataset and would extend the coverage to nearly eight times the area of existing 9-band photometry.

Blanco, Diana [UC, Santa Cruz; UC, Santa Cruz, Ins↗
DOE OSTI2020

Lyα Observations of Comet C/2013 A1 (Siding Spring) Using MAVEN IUVS Echelle

The close approach of comet C/2013 A1 (Siding Spring) to Mars in 2014 October provided a unique opportunity to observe a dynamically new Oort cloud comet with potential for interaction with a planet’s atmosphere. The water-originating hydrogen coma of the comet extended to over 20 million km from the nucleus. Determining the properties of this coma contributes to characterizing the comet’s water content and production rate. The present study analyzes a unique data set of high spectral resolution UV observations of comet C/2013 A1 Siding Spring measured by the Mars Atmosphere and Volatile Evolution spacecraft. The Siding Spring observations capture Lyα emissions from the Martian corona, the interplanetary medium, as well as the cometary H and D reservoirs. The isolated cometary spectra are analyzed to reveal a velocity distribution of H atoms that are consistent with model estimates of H{sub 2}O photodissociated H emissions and of OH photodissociated H emissions, Doppler shifted from the main comet H emission line center by 18 km s{sup −1} and 8 km s{sup −1}, respectively. The variations in comet H brightness with distance from the nucleus are used to constrain cometary water production to a rate of 0.5 × 10{sup 28} molecules s{sup −1} at a time when Siding Spring was at 1.5 au, pre-perihelion.

79 ASTRONOMY AND ASTROPHYSICS↗