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DOE OSTI2023

A Preferential Growth Channel for Supermassive Black Holes in Elliptical Galaxies at z ≲ 2

The assembly of stellar and supermassive black hole (SMBH) mass in elliptical galaxies since z ~ 1 can help to diagnose the origins of locally observed correlations between SMBH mass and stellar mass. We therefore construct three samples of elliptical galaxies, one at z ~ 0 and two at 0.7 ≲ z ≲ 2.5, and quantify their relative positions in the M BH -M * plane. Using a Bayesian analysis framework, we find evidence for translational offsets in both stellar mass and SMBH mass between the local sample and both higher-redshift samples. The offsets in stellar mass are small, and consistent with measurement bias, but the offsets in SMBH mass are much larger, reaching a factor of 7 between z ~ 1 and z ~ 0. The magnitude of the SMBH offset may also depend on redshift, reaching a factor of ~20 at z ~ 2. The result is robust against variation in the high- and low-redshift samples and changes in the analysis approach. The magnitude and redshift evolution of the offset are challenging to explain in terms of selection and measurement biases. We conclude that either there is a physical mechanism that preferentially grows SMBHs in elliptical galaxies at z ≲ 2, or that selection and measurement biases are both underestimated, and depend on redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRSDate not supplied

A New Statistical Estimate of the Radar Coverage of the Low Earth Orbit Debris Environment

For over three decades, the NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar, Haystack Ultrawideband Satellite Imaging Radar (HUSIR), and Haystack Auxiliary (HAX) radar assets to collect data on the low Earth orbit (LEO) debris environment. Each radar, with its unique beamwidth, altitude and inclination coverage, and limiting size threshold, operates in a beam park mode to statistically sample the orbital debris population in LEO. Provided that these assets are shared with other users, the orbital debris data collection is not continuous; rather, intermittent data collects are acquired and sent to the NASA ODPO. To understand the sampling process conducted by each radar over time and any related observational biases, a Statistical Confirmation of Radar Uniformity or Bias (SCRUB) code has been implemented to model the coverage of these assets for informing future operations, as well as usage of the data collected from these ground-based sensors. For this analysis, Right Ascension of the Ascending Node (RAAN) is used as a metric to measure statistical coverage. A complete survey of the LEO environment is understood to be measurements that sufficiently sample all values of RAAN for each altitude-inclination pair visible from the radar asset. Regions of incompleteness or statistical bias can help inform future observation campaigns. The SCRUB tool evaluates the coverage of the LEO environment, not by examining individual objects that may pass through a sensor’s field of view (FOV), but by determining which orbit planes pass through the FOV. Once a pointing geometry for a radar site, observation time, and range extent are configured by the user, SCRUB computes the inclinations and altitudes that are visible by the sensor. For each inclination-altitude pair, there is a distinct pair of possible RAAN values, corresponding to the ascending and descending orbit passing through that point in the sensor cone. Repeating this process for all points in the beam, and for multiple time periods during an observation window, creates a matrix of all inclination-altitude-RAAN combinations that are visible during a sensor run. This process can then be repeated for all observations within a year (for an annual survey), propagating all the RAAN values to a common epoch, typically the start of a calendar year, and combining the observations to assemble a full estimate of the RAAN coverage of the LEO environment. This RAAN coverage can then be analyzed for uniformity of sampling, within a certain inclination-altitude pair, or between larger regions of the space environment. This paper provides a general overview of the radar assets utilized by ODPO, typical analysis data products assuming circular orbits, and a discussion of the algorithms that feed between modeling and measurement operations. Following the description of the algorithm, estimates of coverage using HUSIR radar data collected from multiple years are developed and compared.

Chris L Ostrom↗

NASA NTRS1990

Dumb-bell galaxies in southern clusters: Catalog and preliminary statistical results

The dominant galaxy of a rich cluster is often an object whose formation and evolution is closely connected to the dynamics of the cluster itself. Hoessel (1980) and Schneider et al. (1983) estimate that 50 percent of the dominant galaxies are either of the dumb-bell type or have companions at projected distances less than 20 kpc, which is far in excess of the number expected from chance projection (see also Rood and Leir 1979). Presently there is no complete sample of these objects, with the exception of the listing of dumb-bell galaxies in BM type I and I-II clusters in the Abell statistical sample of Rood and Leir (1979). Recent dynamical studies of dumb-bell galaxies in clusters (Valentijn and Casertano, 1988) still suffer from inhomogeneity of the sample. The fact that it is a mixture of optically and radio selected objects may have introduced an unknown biases, for instance if the probability of radio emission is enhanced by the presence of close companions (Stocke, 1978, Heckman et al. 1985, Vettolani and Gregorini 1988) a bias could be present in their velocity distribution. However, this situation is bound to improve: a new sample of Abell clusters in the Southern Hemisphere has been constructed (Abell et al., 1988 hereafter ACO), which has several advantages over the original northern catalog. The plate material (IIIaJ plates) is of better quality and reaches fainter magnitudes. This makes it possible to classify the cluster types with a higher degree of accuracy, as well as to fainter magnitudes. The authors therefore decided to reconsider the whole problem constructing a new sample of dumb-bell galaxies homogeneously selected from the ACO survey. Details of the classification criteria are given.

Vettolani, G.↗

NASA NTRS2005

Precision Requirements for Space-based XCO2 Data

Precision requirements have been determined for the column-averaged CO2 dry air mole fraction (X(sub CO2)) data products to be delivered by the Orbiting Carbon Observatory (OCO). These requirements result from an assessment of the amplitude and spatial gradients in X(sub CO2), the relationship between X(sub CO2) precision and surface CO2 flux uncertainties calculated from inversions of the X(sub CO2) data, and the effects of X,,Z biases on CO2 flux inversions. Observing system simulation experiments and synthesis inversion modeling demonstrate that the OCO mission design and sampling strategy provide the means to achieve the X(sub CO2) precision requirements. The impact of X(sub CO2) biases on CO2 flux uncertainties depend on their spatial and temporal extent since CO2 sources and sinks are inferred from regional-scale X(sub CO2) gradients. Simulated OCO sampling of the TRACE-P CO2 fields shows the ability of X(sub CO2) data to constrain CO2 flux inversions over Asia and distinguish regional fluxes from India and China.

Miller, C. E.↗

DOE OSTI2020

Impact of divertor material on neutral recycling and discharge fueling in DIII-D

We report that experiments with the lower divertor of DIII-D during the Metal Rings Campaign (MRC) show that the fraction F of atomic D in the total recycling flux is material-dependent and varies through the ELM cycle, which may affect divertor fueling. Between ELMs, F C ~ 10% and FW ~ 40%, consistent with expectations if all atomic recycling is due to reflections. During ELMs, F C increases to 50% and F W to 60%. In contrast, the total D recycling coefficient including atoms and molecules R stays close to unity near the strike point where the surface is saturated with D. During ELMs, R can deviate from unity, increasing during high energy ELM-ion deposition (net D release) and decreasing at the end of the ELM which leads to ability of the target to trap the ELM-deposited D. The increase of R > 1 in response to an increase in ion impact energy Ei has been studied with small divertor target samples using Divertor Materials Evaluation System (DiMES). An electrostatic bias was applied to DiMES to change E i by 90 eV. On all studied materials including C, Mo, uncoated and W-coated TZM (>99% Mo, Ti, and Zr alloy), W, and W fuzz, an increase of E i transiently increased the D yield (and R) by ~10%. On C there was also an increase in the molecular D 2 yield, probably due to ion-induced D 2 desorption. Despite the measured increase in F on W compared to C, attached H-mode shots with OSP on W during MRC did not demonstrate a higher pedestal density. About 8% increase in the edge density could be seen only in attached L-mode scenarios. The difference can be explained by higher D trapping in the divertor and lower divertor fueling efficiency in H- versus L-mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗

DOE OSTI2024

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Electron-induced chemical transformation of vapor-phase synthesized hybrid resist materials for EUV and beyond EUV lithography

Here, in this study, we utilize an in situ Fourier transform infrared (FTIR) spectroscopy chamber equipped with an electron flood gun to comprehensively investigate the electron-induced chemical transformations in advanced inorganic–organic hybrid resist thin films. The characteristics of the electron gun were assessed, showing a detectable current decrease with increasing Faraday cup bias voltage, confirming that the electron energies impacting the sample were within the expected range. This unique FTIR system was applied to study two hybrid resist thin-film systems, Al–HQ and Zn–HQ, both exhibiting saturation growth characteristics at 150 °C. Results revealed that Al–HQ demonstrated superior electron sensitivity compared to Zn–HQ at energies ≤ 100 eV. Chemical changes observed were consistent with previous findings, underscoring the potential of this methodology to elucidate exposure mechanisms. This research contributes to the fundamental understanding necessary for the development of advanced resist materials for the next-generation nanolithography applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Cosmic Reionization On Computers: The Galaxy-Halo Connection between $5 \leq z \leq10$

We explore the connection between the stellar component of galaxies and their host halos during the epoch of reionization (5 ≤ z ≤ 10) using the CROC (Cosmic Reionization on Computers) simulations. We compare simulated galaxies with observations and find that CROC underpredicts the abundance of luminous galaxies when compared to observed UV luminosity functions, and analogously the most massive galaxies when compared to observed stellar mass functions. We can trace the deficit of star formation to high redshifts, where the slope of the star formation rate to stellar mass relation is consistent with observations, but the normalization is systematically low. This results in a star formation rate density and stellar mass density that are systematically offset from observations. However, the less luminous or lower stellar mass objects have luminosities and stellar masses that agree fairly well with observational data. We explore the stellar-to-halo mass ratio (SHMR), a key quantity that is difficult to measure at high redshifts and that models do not consistently predict. In CROC, the SHMR decreases with redshift, a trend opposite to some abundance-matching studies. These discrepancies uncover where future effort should be focused in order to improve the fidelity of modeling cosmic reionization. We also compare the CROC galaxy bias with observational measurements using Lyman-break galaxy samples, finding reasonable consistency.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History

In order to study the Milky Way, RR Lyrae (RRL) variable stars identified by Gaia, ASAS-SN, and ZTF sky survey projects have been analyzed as tracers in this work. Photometric and spectroscopic information of 3417 RRLs including proper motions, radial velocity, and metallcity are obtained from observational data of Gaia, LAMOST, GALAH, APOGEE, and RAVE. Precise distances of RRLs with typical uncertainties less than 3% are derived by using a recent comprehensive period–luminosity–metallicity relation. Our results from kinematical and chemical analysis provide important clues for the assembly history of the Milky Way, especially for the Gaia–Sausage ancient merger. The kinematical and chemical trends found in this work are consistent with those of recent simulations that indicated that the Gaia–Sausage merger had a dual origin in the Galactic thick disk and halo. As recent similar works have found, the halo RRL sample in this work contains a subset of radially biased orbits besides a more isotropic component. This higher orbital anisotropy component amounts to β ≃ 0.8, and it contributes between 42% and 83% of the halo RRLs at 4 < R( kpc) < 20.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1963

The satellite ferrimagnetic power limiter

To limit the 4080-mc local oscillator signal power input to the beat oscillator modulator of the Telstar satellite communications repeater, a subsidiary absorption limiter was used which consisted of an optically polished sphere of single-crystal yttrium iron garnet (YIG), placed in a resonant transmission cavity between the amplified 4080-mc output of the traveling-wave tube and the BO modulator input. The limiter holds the output power nearly constant above a given input threshold; below this threshold the YIG is linear and introduces only a small loss. The threshold is determined, for a given sample at a given frequency, by the external magnetic bias field. Temperature compensation over the desired range was obtained by orienting the crystal with the dc magnetic field along a [100] or “hard” axis. The total weight of the limiter package, including the bias magnet and cavity, is 13 ounces.

YTTRIUM-IRON GARNET /YIG/ CRYSTAL↗

NASA NTRS1989

Estimations of ABL fluxes and other turbulence parameters from Doppler lidar data

Techniques for extraction boundary layer parameters from measurements of a short-pulse CO2 Doppler lidar are described. The measurements are those collected during the First International Satellites Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). By continuously operating the lidar for about an hour, stable statistics of the radial velocities can be extracted. Assuming that the turbulence is horizontally homogeneous, the mean wind, its standard deviations, and the momentum fluxes were estimated. Spectral analysis of the radial velocities is also performed from which, by examining the amplitude of the power spectrum at the inertial range, the kinetic energy dissipation was deduced. Finally, using the statistical form of the Navier-Stokes equations, the surface heat flux is derived as the residual balance between the vertical gradient of the third moment of the vertical velocity and the kinetic energy dissipation. Combining many measurements would normally reduce the error provided that, it is unbiased and uncorrelated. The nature of some of the algorithms however, is such that, biased and correlated errors may be generated even though the raw measurements are not. Data processing procedures were developed that eliminate bias and minimize error correlation. Once bias and error correlations are accounted for, the large sample size is shown to reduce the errors substantially. The principal features of the derived turbulence statistics for two case studied are presented.

Gal-Chen, Tzvi↗

NASA NTRS1990

Potential, velocity, and density fields from sparse and noisy redshift-distance samples - Method

A method for recovering the three-dimensional potential, velocity, and density fields from large-scale redshift-distance samples is described. Galaxies are taken as tracers of the velocity field, not of the mass. The density field and the initial conditions are calculated using an iterative procedure that applies the no-vorticity assumption at an initial time and uses the Zel'dovich approximation to relate initial and final positions of particles on a grid. The method is tested using a cosmological N-body simulation 'observed' at the positions of real galaxies in a redshift-distance sample, taking into account their distance measurement errors. Malmquist bias and other systematic and statistical errors are extensively explored using both analytical techniques and Monte Carlo simulations.

Dekel, Avishai↗

NASA NTRS2006

Initial Results of the Geosynchronous Synthetic Thinned Array Radiometer (GeoSTAR)

An error budget is presented to meet 1 Kelvin radiometric accuracy in a geostationary atmospheric sounder with 50 km spatial resolution on the earth. The gain and phase errors are weighted by the magnitude of visibility versus antenna separation, and requirements range between approx.0.5% and 0.3 degrees of amplitude and phase, respectively, for the closest spacings at the center of the array, and about 5% and 3 degrees for the majority of the array. The latter requirement is met by our design without any special testing or stabilizations by reference signals. The former is met using an internal noise diode reference and by measuring the detailed antenna patterns on the antenna range. Biases and other additive errors in the raw visibility samples must be below about 2 mK on average, and this requirement is met by a phase shifting scheme applied to the local oscillator distribution. An outline of the data processing is presented, along with the first images from this system.

Synthetic Thinned Aperture Radiometer (STAR)↗

NASA NTRS2011

Post-Fragmentation Whole Genome Amplification-Based Method

This innovation is derived from a proprietary amplification scheme that is based upon random fragmentation of the genome into a series of short, overlapping templates. The resulting shorter DNA strands (<400 bp) constitute a library of DNA fragments with defined 3 and 5 termini. Specific primers to these termini are then used to isothermally amplify this library into potentially unlimited quantities that can be used immediately for multiple downstream applications including gel eletrophoresis, quantitative polymerase chain reaction (QPCR), comparative genomic hybridization microarray, SNP analysis, and sequencing. The standard reaction can be performed with minimal hands-on time, and can produce amplified DNA in as little as three hours. Post-fragmentation whole genome amplification-based technology provides a robust and accurate method of amplifying femtogram levels of starting material into microgram yields with no detectable allele bias. The amplified DNA also facilitates the preservation of samples (spacecraft samples) by amplifying scarce amounts of template DNA into microgram concentrations in just a few hours. Based on further optimization of this technology, this could be a feasible technology to use in sample preservation for potential future sample return missions. The research and technology development described here can be pivotal in dealing with backward/forward biological contamination from planetary missions. Such efforts rely heavily on an increasing understanding of the burden and diversity of microorganisms present on spacecraft surfaces throughout assembly and testing. The development and implementation of these technologies could significantly improve the comprehensiveness and resolving power of spacecraft-associated microbial population censuses, and are important to the continued evolution and advancement of planetary protection capabilities. Current molecular procedures for assaying spacecraft-associated microbial burden and diversity have inherent sample loss issues at practically every step, particularly nucleic acid extraction. In engineering a molecular means of amplifying nucleic acids directly from single cells in their native state within the sample matrix, this innovation has circumvented entirely the need for DNA extraction regimes in the sample processing scheme.

Benardini, James↗

NASA NTRS2014

A Range Correction for Icesat and Its Potential Impact on Ice-sheet Mass Balance Studies

We report on a previously undocumented range error in NASA's Ice, Cloud and land Elevation Satellite (ICESat) that degrades elevation precision and introduces a small but significant elevation trend over the ICESat mission period. This range error (the Gaussian-Centroid or 'G-C'offset) varies on a shot-to-shot basis and exhibits increasing scatter when laser transmit energies fall below 20 mJ. Although the G-C offset is uncorrelated over periods less than1 day, it evolves over the life of each of ICESat's three lasers in a series of ramps and jumps that give rise to spurious elevation trends of −0.92 to −1.90 cm yr(exp −1), depending on the time period considered. Using ICESat data over the Ross and Filchner-Ronne ice shelves we show that (1) the G-C offset introduces significant biases in ice-shelf mass balance estimates, and (2) the mass balance bias can vary between regions because of different temporal samplings of ICESat.We can reproduce the effect of the G-C offset over these two ice shelves by fitting trends to sample-weighted mean G-C offsets for each campaign, suggesting that it may not be necessary to fully repeat earlier ICESat studies to determine the impact of the G-C offset on ice-sheet mass balance estimates.

mass balance↗

NASA NTRS2020

Precision Assessment of the HPLC Phytoplankton Pigment Dataset Analyzed by NASA to Quantify Global Variability in Support of Ocean Color Remote Sensing

The ability to generate chlorophyll a (Chl a) assessments from ocean color orbital sensors, such as VIIRS and MODIS, that satisfy the requirements to be climate-quality data record (CDR) quality is contingent in part on the quality of the in situ ground or sea truth observations that serve as datasets for vicarious calibration and algorithm validation activities. NASA has a mandate to collect, analyze, and distribute in situ data of the highest possible quality with documented uncertainties and in keeping with established performance metrics. Using a dataset of over 18,000 HPLC phytoplankton pigment samples representing water collected in all major ocean basins analyzed a central laboratory (Field Support Group (FSG) of the Ocean Ecology Laboratory (OEL) at NASA Goddard Space Flight Center (GSFC)), we performed an assessment of the global precision among sample replicates of Chl a as well as major accessory pigments. We investigated the impacts of filtration volume, water basin, collection technique, pigment concentration, and different filtration volumes for replicate filters on replicate filter precision, as well as investigating any pigment-specific differences. Our results quantify sample variability with the goal of understanding any systemic biases or biogeographic influences.

Thomas, Crystal S.↗

NASA NTRS2024

Unveiling the Transferability of PLSR Models for Leaf Trait Estimation: Lessons from a Comprehensive Analysis with a Novel Global Dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least-squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47,393 records for >700 species and eight PFTs at 101 globally-distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55% and 7.0-33.12%) compared to nonspatial random cross-validation (NRCV). The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs and time due to biased validation schemes and provide guidance for future field sampling strategies and remote sensing applications.

Leaf traits↗