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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Photometric redshifts and intrinsic alignments: Degeneracies and biases in the 3 × 2 pt analysis

We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic alignments (IA) and photometric redshift (photo-z) model misspecification error. We first examine the degeneracies between the parameters of the tidal alignment–tidal torquing (TATT) model for IA and of a photo-z model including a mean shift ( Δ z ¯ ) and variance ( σ z ) for each tomographic bin of lenses and sources, under a variety of underlying true IA behaviors. We identify strong degeneracies between: (1) the redshift scaling of the tidal alignment amplitude and the mean shift and variances of source bins, (2) the redshift scaling of the tidal torquing amplitude and the variance of the lowest- z source bin, and (3) the IA source density weighting and the mean shift and variance of several source bins. We then use this information to guide our exploration of the level of cosmological parameter bias which can be induced given incorrect modeling of IA, photo-z, or both. We find that marginalizing over all the parameters of TATT is generally sufficient to preclude cosmological parameter bias in the scenarios we consider. However, this does not necessarily mean that IA and photo-z parameters are themselves unbiased, nor does it mean that the best-fit model is a good fit to the data. We also find scenarios where the inferred parameters produce χ d . o . f . 2 values indicative of a good fit but cosmological parameter bias is significant, particularly when the IA source density weighting parameter is not marginalized over. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Physically interpretable approximations of many-body spectral functions

The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the vector fitting (VFIT) algorithm to fit a variety of spectral functions calculated from the Holstein model of electron-phonon interactions. We show that the resulting rational functions are highly efficient in their fitting of sharp features in the spectral functions, and could provide a means to infer physically relevant information from a spectral data set. The position of the peaks in the approximated spectral function are determined by the location of poles in the complex plane. Additionally, we developed a variant of VFIT that incorporates regularization to improve the quality of fits. With this procedure, we demonstrate it is possible to achieve accurate spectral function fits that vary smoothly as a function of physical conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗
DOE OSTI2025

Measurement of the inhomogeneity of the KATRIN tritium source electric potential by high-resolution spectroscopy of conversion electrons from $\mathbf {^{83m}}$Kr

Precision spectroscopy of the electron spectrum of the tritium β-decay near the kinematic endpoint is a direct method to determine the effective electron antineutrino mass. The KArlsruhe TRItium Neutrino (KATRIN) experiment aims to determine this quantity with a sensitivity of better than 0.3 eV (90% C.L.). An inhomogeneous electric potential in the tritium source of KATRIN can lead to distortions of the β-spectrum, which directly impact the neutrino-mass observable. This effect can be quantified through precision spectroscopy of the conversion-electrons of co-circulated metastable 83m Kr. Therefore, dedicated, several-weeks long measurement campaigns have been performed within the KATRIN data taking schedule. In this work, we infer the tritium source potential observables from these measurements, and present their implications for the neutrino-mass determination.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector

The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (2023-2024) conditions, the jet energy resolution improves by 10-20% for jets with transverse momentum between 30-100 GeV. Inference time is evaluated using simulated multijet events, with a median of $20\,\hbox {ms}$ per event on an Nvidia L4 GPU, compared to approximately $110\,\hbox {ms}$ for the standard CMS PF reconstruction.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2024

A framework for inferring and analyzing pharmacotherapy treatment patterns

Background: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. Methods: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. Results: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. Conclusions: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Designing an Optimal Sensor Network via Minimizing Information Loss

Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Comprehensive Comparison of Building Height Products at a Global Level

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

42 ENGINEERING↗
DOE OSTI2020

2D Retrieval Frameworks for Hot Jupiter Phase Curves

Spectroscopic phase curves provide unique access to the three-dimensional properties of transiting exoplanet atmospheres. However, a modeling framework must be developed to deliver accurate inferences of atmospheric properties for these complex data sets. Here, we develop an approach to retrieve temperature structures and molecular abundances from phase curve spectra at any orbital phase. In the context of a representative hot Jupiter with a large day–night temperature contrast, we examine the biases in typical one-dimensional (1D) retrievals as a function of orbital phase/geometry, compared to two-dimensional (2D) models that appropriately capture the disk-integrated phase geometry. We guide our intuition by applying our new framework on a simulated Hubble Space Telescope (HST)+Spitzer phase curve data set in which the “truth” is known, followed by an application to the spectroscopic phase curve of the canonical hot Jupiter, WASP-43b. We also demonstrate the retrieval framework on simulated James Webb Space Telescope (JWST) phase curve observations. We apply our new geometric framework to a joint fit of all spectroscopic phases, assuming longitudinal molecular abundance homogeneity, resulting in an a factor of 2 improvement in abundances precision when compared to individual phase constraints. With a 1D retrieval model on simulated HST+Spitzer data, we find strongly biased molecular abundances for CH{sub 4} and CO{sub 2} at most orbital phases. With 2D, the day and night profiles retrieved from WASP-43b remain consistent throughout the orbit. JWST retrievals show that a 2D model is strongly favored at all orbital phases. Based on our new 2D retrieval implementation, we provide recommendations on when 1D models are appropriate and when more complex phase geometries involving multiple TP profiles are required to obtain an unbiased view of tidally locked planetary atmospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

DESI Spectroscopy of HETDEX Emission-line Candidates. I. Line Discrimination Validation

The Hobby–Eberly Dark Energy Experiment (HETDEX) is an untargeted spectroscopic galaxy survey that uses Lyα-emitting galaxies (LAEs) as tracers of 1.9 < z < 3.5 large-scale structure. Most detections consist of a single emission line, whose identity is inferred via a Bayesian analysis of ancillary data. To determine the accuracy of these line identifications, HETDEX detections were observed with the Dark Energy Spectroscopic Instrument (DESI). In two DESI pointings, high-confidence spectroscopic redshifts are obtained for 1157 sources, including 982 LAEs. The DESI spectra are used to evaluate the accuracy of the HETDEX object classifications and tune the methodology to achieve the HETDEX science requirement of ≲2% contamination of the LAE sample by low-redshift emission-line galaxies, while still assigning 96% of the true Lyα emission sample with the correct spectroscopic redshift. We compare emission-line measurements between the two experiments assuming a simple Gaussian line fitting model. Fitted values for the central wavelength of the emission line, the measured line flux, and line widths are consistent between the surveys within uncertainties. Derived spectroscopic redshifts, from the two classification pipelines, when both agree as an LAE classification, are consistent to within $\langle$Δz/(1 + z)$\rangle$ = 6.9 × 10 −5 with an rms scatter of 3.3 × 10 −4 . Data are available at https://data.desi.lbl.gov/desi/public/dr1/vac/dr1/hetdex.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Measuring Black Hole Light Echoes with Very Long Baseline Interferometry

Light passing near a black hole can follow multiple paths from an emission source to an observer due to strong gravitational lensing. Photons following different paths take different amounts of time to reach the observer, which produces an echo signature in the image. The characteristic echo delay is determined primarily by the mass of the black hole, but it is also influenced by the black hole spin and inclination to the observer. In the Kerr geometry, echo images are demagnified, rotated, and sheared copies of the direct image and lie within a restricted region of the image. Echo images have exponentially suppressed flux, and temporal correlations within the flow make it challenging to directly detect light echoes from the total light curve. In this Letter, we propose a novel method to search for light echoes by correlating the total light curve with the interferometric signal at high spatial frequencies, which is a proxy for indirect emission. We explore the viability of our method using numerical general relativistic magnetohydrodynamic simulations of a near-face-on accretion system scaled to M87-like parameters. We demonstrate that our method can be used to directly infer the echo delay period in simulated data. An echo detection would be clear evidence that we have captured photons that have circled the black hole, and a high-fidelity echo measurement would provide an independent measure of fundamental black hole parameters. Our results suggest that detecting echoes may be achievable through interferometric observations with a modest space-based very long baseline interferometry mission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties Over the NSA Site

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This data set provides more than 1800 samples of cloud-base ice precipitation properties over Utqiagvik, North Slope of Alaska, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this data set include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this data set. The retrieval algorithm and analysis of this data set are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Magnetospheric plasma motion during a sudden commencement.

A sudden commencement occurred at 2348 UT on Feb. 15, 1967, when the ATS-1 satellite was about 2 hr past local noon at a geocentric distance of 6.6 earth radii. Plasma was observed by the Suprathermal Ion Detector first to flow in the antisolar direction, as expected, but then to flow westward, for about 2 min, at about 50 km/sec. Analysis of ground magnetograms suggests that the surprising westward flow, which must have involved an electric field of about 10 mV/m at 6.6 earth radii, resulted from the ionosphere's reaction to the sudden commencement. Beginning about 2 min before the start of the westward flow at ATS-1, ground magnetometers near the foot of the ATS-1 field line typically recorded magnetic-field deflections of about 70 gamma, to the northeast. Taking the ground observations, assuming a height-integrated Hall conductivity of 1 mho, and a standoff distance of 7.2 earth radii inferred from Explorer 33 solar-wind data, a magnetospheric electric field is derived which agrees in magnitude and direction with that required to produce the observed flow at ATS-1.

Lin, C.-A.↗
NASA NTRS1974

Particulate sizes from polarization measurements

An experiment with a ground-based polarimeter is described. It is shown that the optical properties of particles in the atmosphere can be inferred on the basis of the measured data of the radiation field. The results of the investigation demonstrate that the turbid atmosphere can be continually monitored with the aid of remote measurements of polarization and intensity.

Kuriyan, J. G.↗
NASA NTRS1975

The correlation of Skylab L-band brightness temperatures with antecedent precipitation

The S194 L-band radiometer flown on the Skylab mission measured terrestrial radiation at the microwave wavelength of 21.4 cm. The terrain emissivity at this wavelength is strongly dependent on the soil moisture content, which can be inferred from antecedent precipitation. For the Skylab data acquisition pass from the Oklahoma panhandle to southeastern Texas on 11 June 1973, the S194 brightness temperatures are highly correlated with antecedent precipitation from the preceding eleven day period, but very little correlation was apparent for the preceding five day period. The correlation coefficient between the averaged antecedent precipitation index values and the corresponding S194 brightness temperatures between 230 K and 270 K, the region of apparent response to soil moisture in the data, was -0.97. The equation of the linear least squares line is given.

Mcfarland, M. J.↗
NASA NTRS1975

The correlation of Skylab L-band brightness temperatures with antecedent precipitation

The S194 L-band radiometer flown on the Skylab mission measured terrestrial radiation at the microwave wavelength of 21.4 cm. The terrain emissivity at this wavelength is strongly dependent on the soil moisture content, which can be inferred from antecedent precipitation. For the Skylab data acquisition pass from the Oklahoma panhandle to southeastern Texas on 11 June 1973, the S194 brightness temperatures are highly correlated with antecedent precipitation from the preceding eleven day period, but very little correlation was apparent for the preceding five day period. The correlation coefficient between the averaged antecedent precipitation index values and the corresponding S194 brightness temperatures between 230 K and 270 K, the region of apparent response to soil moisture in the data, was -0.97. The equation of the linear least squares line fitted to the data was: API (cm) = 31.99 -0.114 TB (K), where API is the antecedent precipitation index and TB is the S194 brightness temperature.

Mcfarland, M. J.↗