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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 OSTI2023

A quantum trajectory picture of single photon absorption and energy transport in photosystem II

Here, we use quantum trajectory theory to study the dynamics of the first step in photosynthesis for a single photon interacting with photosystem II (PSII). By considering individual trajectories we are able to look beyond the ensemble average dynamics to compute the PSII system evolution conditioned upon individual photon counting measurements. Measurements of the transmitted photon beam strongly affects the system state, since detection of an outgoing photon confirms that the PSII must be in the electronic ground state, while a null measurement implies it is in an excited electronic state. We show that under ideal conditions, observing the null result transforms a state with a low excited state population to a state with nearly all population contained in the excited states. We study the PSII dynamics conditioned on such photon counting for both a pure excitonic model of PSII and a more realistic model with exciton-phonon coupling to a dissipative phononic environment. In the absence of such coupling, we show that the measured fluorescence rates show oscillations constituting a photon-counting witness of excitonic coherence. Excitonic coupling to the phonon environment has a strong effect on the observed rates of fluorescence, damping the oscillations. Addition of non-radiative decay and incoherent transitions to radical pair states in the reaction center to the phononic model allows extraction of a quantum efficiency of 92.5% from the long-time evolution, consistent with bulk experimental measurements.

Coherent states↗
DOE OSTI2020

Characterization of Partially Observed Epidemics - Application to COVID-19

This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Tensor Decompositions for Count Data that Leverage Stochastic and Deterministic Optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the global maximum likelihood estimator from local minima. Simultaneously, a recent trend in theoretical computer science and numerical linear algebra leverages randomization to solve very large, hard problems. The typical approach is to use randomization for a fast approximation and determinism for refinement to yield effective algorithms with theoretical guarantees. Two popular algorithms for Poisson CPD reflect that emergent dichotomy: CP Alternating Poisson Regression is a deterministic algorithm and Generalized Canonical Polyadic decomposition makes use of stochastic algorithms in several variants. This work extends recent work to develop two new methods that leverage randomized and deterministic algorithms for improved accuracy and performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The Next Generation Virgo Cluster Survey. XXVIII. Characterization of the Galactic White Dwarf Population

We use three different techniques to identify hundreds of white dwarf (WD) candidates in the Next Generation Virgo Cluster Survey (NGVS) based on photometry from the NGVS and GUViCS, and proper motions derived from the NGVS and the Sloan Digital Sky Survey (SDSS). Photometric distances for these candidates are calculated using theoretical color–absolute magnitude relations, while effective temperatures are measured by fitting their spectral energy distributions. Disk and halo WD candidates are separated using a tangential velocity cut of 200 km s{sup −1} in a reduced proper motion diagram, which leads to a sample of six halo WD candidates. Cooling ages, calculated for an assumed WD mass of 0.6 M {sub ⊙}, range between 60 Myr and 6 Gyr, although these estimates depend sensitively on the adopted mass. Luminosity functions for the disk and halo subsamples are constructed and compared to previous results from the SDSS and SuperCOSMOS survey. We compute a number density of (2.81 ± 0.52) × 10{sup −3} pc{sup −3} for the disk WD population—consistent with previous measurements. We find (7.85 ± 4.55) × 10{sup −6} pc{sup −3} for the halo, or 0.3% of the disk. Observed stellar counts are also compared to predictions made by the TRILEGAL and Besançon stellar population synthesis models. The comparison suggests that the TRILEGAL model overpredicts the total number of WDs. The WD counts predicted by the Besançon model agree with the observations, although a discrepancy arises when comparing the predicted and observed halo WD populations; the difference is likely due to the WD masses in the adopted model halo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Analysis of overlapping count data

Counts of a specific characteristic were obtained within regions defined on an object that was manufactured in a proprietary setting. The count regions were altered during production and resulted in misaligned or overlapping count data. A closed-formula maximum likelihood estimator (MLE) of the new region means is derived using all of the available count data and an independent Poisson model. The MLE is shown to be preferable to estimators constructed using generalized linear models for the overlapping data setting. This closed-form estimator extends to over-dispersed overlapping count data as the quasi-MLE and also performs well with correlated overlapping count data. Standard errors for the estimator are approximated and are validated with a simulation study. Additionally, the methods are extended to overlapping multinomial data. Illustrative examples of the methods are provided throughout the paper and are reproducible with the supplemental R code. Additionally, proofs of the paper’s results are also included in the supplemental material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

WE-DE-207B-01: Optimization for Contrast-Enhanced Spectral Mammography Based On Photon-Counting Detectors

Purpose: To investigate the feasibility of optimizing the imaging parameters for contrast-enhanced spectral mammography based on Si strip photon-counting detectors. Methods: A computer simulation model using polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector was evaluated for contrast-enhanced spectral mammography. The simulation traces the emission of photons from the x-ray source, attenuation through the breast and subsequent absorption in the detector. The breast was modeled as a mixture of adipose and mammary gland tissues with a breast density of 30%. A 4 mm iodine signal with a concentration of 4 mg/ml was used to simulate the enhancement of a lesion. Quantum efficiency of the detector was calculated based on the effective attenuation length in the Si strips. The figure-of-merit (FOM), which was defined as the decomposed iodine signal-to-noise ratio (SNR) with respect to the square root of the mean glandular dose (MGD), was chosen to optimize the imaging parameters, in terms of beam energy, splitting energy, and pre-filtrations for breast of various thicknesses and densities. Results: The optimal imaging parameters, which lead to the highest FOM, were found at a beam energy of 45 kVp with a splitting energy at 34 keV for an averaged breast thickness of 4 cm with a standard 0.75 mm Al pre-filtration. The optimal tube voltage varied slightly from 46 to 44 kVp as the breast thickness increases from 2 to 8 cm. The optimal tube voltage decreased to 42 kVp when the Al pre-filtration was increased to 3 mm. Conclusion: This simulation study predicted the optimal imaging parameters for application of photon-counting spectral mammography to contrast-enhanced imaging. The simulation results laid the ground work for future phantom and clinical studies. Grant funding from Philips Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

Abstract Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e−/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Materials Science↗
DOE OSTI2012

CORRELATIONS IN THE (SUB)MILLIMETER BACKGROUND FROM ACT Multiplication-Sign BLAST

We present measurements of the auto- and cross-frequency correlation power spectra of the cosmic (sub)millimeter background at 250, 350, and 500 {mu}m (1200, 860, and 600 GHz) from observations made with the Balloon-borne Large Aperture Submillimeter Telescope (BLAST); and at 1380 and 2030 {mu}m (218 and 148 GHz) from observations made with the Atacama Cosmology Telescope (ACT). The overlapping observations cover 8.6 deg{sup 2} in an area relatively free of Galactic dust near the south ecliptic pole. The ACT bands are sensitive to radiation from the cosmic microwave background, to the Sunyaev-Zel'dovich effect from galaxy clusters, and to emission by radio and dusty star-forming galaxies (DSFGs), while the dominant contribution to the BLAST bands is from DSFGs. We confirm and extend the BLAST analysis of clustering with an independent pipeline and also detect correlations between the ACT and BLAST maps at over 25{sigma} significance, which we interpret as a detection of the DSFGs in the ACT maps. In addition to a Poisson component in the cross-frequency power spectra, we detect a clustered signal at 4{sigma}, and using a model for the DSFG evolution and number counts, we successfully fit all of our spectra with a linear clustering model and a bias that depends only on redshift and not on scale. Finally, the data are compared to, and generally agree with, phenomenological models for the DSFG population. This study demonstrates the constraining power of the cross-frequency correlation technique to constrain models for the DSFGs. Similar analyses with more data will impose tight constraints on future models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting

Many existing object counting methods rely on density map estimation (DME) of the discrete grid representation by decoding extracted image semantic features from designed convolutional neural networks (CNNs). Relying on discrete density maps not only leads to information loss dependent on the original image resolution, but also has a scalability issue when analyzing high-resolution images with cubically increasing memory complexity. Furthermore, none of the existing methods can offer reliable uncertainty quantification (UQ) for the derived count estimates. To overcome these limitations, we design UNcertainty-aware, hypernetwork-based Implicit neural representations for Counting (UNIC) to assign probabilities and the corresponding counting confidence over continuous spatial coordinates. We derive a sampling-based Bayesian counting loss function and develop the corresponding model training algorithm. UNIC outperforms existing methods on the Remote Sensing Object Counting (RSOC) dataset with reliable UQ and improved interpretability of the derived count estimates. Our code is available at https://github.com/SiyuanXu-tamu/UNIC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Excluded volume and ion-ion correlation effects on the ionic atmosphere around B-DNA: Theory, simulations, and experiments

The ionic atmosphere around a nucleic acid regulates its stability in aqueous salt solutions. One major source of complexity in biological activities involving nucleic acids arises from the strong influence of the surrounding ions and water molecules on their structural and thermodynamic properties. Here, we implement a classical density functional theory for cylindrical polyelectrolytes embedded in aqueous electrolytes containing explicit (neutral hard sphere) water molecules at experimental solvent concentrations. Our approach allows us to include ion correlations as well as solvent and ion excluded volume effects for studying the structural and thermodynamic properties of highly charged cylindrical polyelectrolytes. Several models of size and charge asymmetric mixtures of aqueous electrolytes at physiological concentrations are studied. Our results are in good agreement with Monte Carlo simulations. Our numerical calculations display significant differences in the ion density profiles for the different aqueous electrolyte models studied. However, similar results regarding the excess number of ions adsorbed to the B-DNA molecule are predicted by our theoretical approach for different aqueous electrolyte models. These findings suggest that ion counting experimental data should not be used alone to validate the performance of aqueous DNA-electrolyte models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Waste Characterization Methods Based on Gamma-ray Imaging Spectrometers - 19454

Characterization of LLW, ILW, U, NORM and TENORM is critical to the nuclear industry's bottom line. Accurate measurements enable the disposal of waste at the lowest possible cost. In the past the most accurate technology available involved using HPGe detectors and extensive system modeling to estimate the total source term given what the user knows about the geometry of the source and any shielding between the source and the detectors. Now there are a variety of imaging detectors that are available commercially, including CZT and HPGe detectors that achieve energy resolution ≤1% FWHM at 662 keV. These devices couple the traditional high resolution spectroscopic analysis with next generation gamma-ray imaging information to more accurately characterize the radiation source term. Uncertainties in the source location will result in significant error in the characterization of an object's radioactivity. It is helpful if the object can be spun, such as a barrel, so that any asymmetry in the source distribution is averaged out from the detector's point of view. Even in the case of a spinning barrel it is important to determine if the radiation is concentrated near the center of the barrel or distributed near the edge. The closer one measures to a barrel, which usually dictates the time required to acquire data, the more these spatial variations in the source distribution will affect the calculated activity of each isotope. This paper will focus on showing the use of CZT imaging spectrometers to characterize complicated radiation source terms. The radiation images are used to determine where the source term should be defined in a radiation transport model that calculates the expected number of photopeak counts for each isotope detected. This is used to calculate the activity of the source. The transport model is used to determine the attenuation of the radiation source term through distance and shielding material, but the detection efficiency is based solely on radiation measurement data. These measurements are taken with a robotic source positioning device that allows for detailed directional efficiency calibration. It is a fully automated system where a detector is loaded into the device and all of the efficiency curves are captured as a function of direction by creating new measurements for each location the robot positions the source. These measurements are tagged with the true source location and the efficiency curve is calculated for each measurement. This is used to create a response function matrix as a function of energy and incident direction which is used to convert the direction dependent flux on the detector head determined from the simulation model to the expected number of counts per unit activity of each isotope of interest. The results of these source term reconstructions in both the spatial and spectral domain will be shown. The impact of improper specification of the source term will be shown for a few standard waste characterization scenarios to demonstrate the importance of proper source location determination. Finally, the underlying calibration method and some sample efficiency curves as a function of direction will be shown to demonstrate the importance of calibrating out directional response functions in imaging devices (which often have a strong forward sensitivity bias). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Total Measurement Uncertainty in Neutron Coincidence Multiplicity Analysis

Neutron multiplicity analysis is a widely used technique in international safeguards and is most commonly applied to the assay of product and scrap materials in the production of mixed oxide fuel. The typical neutron multiplicity analysis applies only a crude, almost cursory, uncertainty analysis limited to propagation of statistical errors in the derived singles, doubles, and triples count rates through the point source model. While these analyses do not account for uncertainties in basic detector parameters (e.g., efficiency or gate utilization factors), nuclear data, or covariances between any of these values, measurement uncertainties are often quoted in the range of 1–2% of the resulting mass values. In this work, we examined the impact of the nuclear data and instrumentation uncertainties on the multiplicity assay result for various counting regimes using the point-model equations as the basis for discussion. Potential biases introduced by the current nuclear data set on the measurement and the need for a more complete error treatment when we progress beyond empirical calibrations based on highly representative items will be discussed.

Mcelroy Jr, Robert↗
DOE OSTI2024

Crowd-based spatial risk assessment of urban flooding: Results from a municipal flood hotline in Detroit, MI

Climate change is increasing the frequency and intensity of extreme precipitation events, raising the risk of urban flood disasters. This study uses a crowd-sourced municipal call database to characterize the spatial distribution of flood risk in Detroit, MI. Call data including dates and addresses were obtained from the City of Detroit Department of Public Works for 2021. Calls were mapped and aggregated to census tract counts and merged with neighborhood-level data. Associations of predictors with flood calls were tested using spatial regression models. Flooding calls were located throughout the city but were concentrated in specific areas. Multivariate models of census tract level call counts indicated that increased poverty and Black, immigrant, and older residents were positively associated with flood calls, while increased elevation was associated with protective effects. Longer distances from waste water interceptors were associated with higher risk for calls. Crowd-sourced flood hotline call data can be used for effective spatial flood risk assessment. Though flooding occurs throughout the city of Detroit, infrastructural, neighborhood, and household factors influence flooding extent. Limitations included the self-reported nature of calls. Future modeling efforts might include input from local stakeholders to improve spatial risk assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

PRIMA-X - Performance Retargeting of Instrumentation, Measurement, and Analysis Technologies for Exascale Computing

The PRIMA-X (Performance Retargeting of Instrumentation, Measurement, and Analysis Technologies for Exascale Computing) project is the successor of the DOE PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing) project, which addressed the challenge of creating a core measurement infrastructure that would serve as a common platform for both integrating leading parallel performance systems (notably TAU and Scalasca) and developing next-generation scalable performance tools. The PRIMA-X project shifts the focus away from refactorization of robust performance tools towards a re-targeting of the parallel performance measurement and analysis architecture for extreme scales. The massive concurrency, asynchronous execution dynamics, hardware heterogeneity, and multi-objective prerequisites (performance, power, resilience) that identify exascale systems introduce fundamental constraints on the ability to carry forward existing performance methodologies. In particular, there must be a deemphasis of per-thread observation techniques to significantly reduce the otherwise unsustainable flood of redundant performance data. Instead, it will be necessary to assimilate multi-level resource observations into macroscopic performance views, from which resilient performance metrics can be attributed to the computational features of the application. This requires a scalable framework for node-level and system-wide monitoring and runtime analyses of dynamic performance information. Also, the interest in optimizing parallelism parameters with respect to performance and energy drives the integration of tool capabilities in the exascale environment further. Initially, PRIMA-X was a collaborative project between the University of Oregon (lead institution) and the German Research School for Simulation Sciences (GRS). Because Prof. Wolf, the PI at GRS, accepted a position as full professor at Technische Universitaet Darmstadt (TU Darmstadt) starting February 1st, 2015, the work of GRS was continued at TU Darmstadt. This report covers the work at TU Darmstadt after the transition. The first main accomplishment of TU Darmstadt is the development of different techniques to aggregate performance data on the level of threads. TU Darmstadt evaluated different schemes designed at GRS during the first phase of this project and integrated them into Score-P, a widely used performance measurement framework for HPC applications. The second main accomplishment is a substantial increase of Score-P’s scalability, achieved by improving its internal representation of the underlying system. Third, we extended our performance-modeling tool Extra-P to automatically create models with more than one input parameter (e.g., process count and input size), which was previously impossible. These models allow the performance behavior of an application to be extrapolated to larger machines. Specific applications of the new multi-parameter capability realized in this project include modeling the isoefficiency function of task-based applications and a novel co-design methodology. Further extensions of Extra-P comprise online modeling functionality, automatic refinement of the model search space, and segmented performance modeling. Further contributions include new locality metrics for both shared- and distributed-memory applications, a new way of measuring the impact of inter-application inference on the execution time of an application, and finally an automatic method that selects appropriate OpenMP constructs for the parallelization of sequential code and classifies the affected variables based on their sharing semantics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Outbreak analysis from case count and survey data

This code enables fitting a model of an infectious disease outbreak, customized for when available data is a combination of case counts and symptom survey responses. This is just a small bit of code being used only for a publication; we need to release it to fulfill a journal's requirement that our work be reproducible.

Goldberg, Emma↗
DOE OSTI2022

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Jones, Sara↗
DOE OSTI2013

HerMES: COSMIC INFRARED BACKGROUND ANISOTROPIES AND THE CLUSTERING OF DUSTY STAR-FORMING GALAXIES

We present measurements of the auto- and cross-frequency power spectra of the cosmic infrared background (CIB) at 250, 350, and 500 {mu}m (1200, 860, and 600 GHz) from observations totaling {approx}70 deg{sup 2} made with the SPIRE instrument aboard the Herschel Space Observatory. We measure a fractional anisotropy {delta}I/I = 14% {+-} 4%, detecting signatures arising from the clustering of dusty star-forming galaxies in both the linear (2-halo) and nonlinear (1-halo) regimes; and that the transition from the 2- to 1-halo terms, below which power originates predominantly from multiple galaxies within dark matter halos, occurs at k{sub {theta}} {approx} 0.10-0.12 arcmin{sup -1} (l {approx} 2160-2380), from 250 to 500 {mu}m. New to this paper is clear evidence of a dependence of the Poisson and 1-halo power on the flux-cut level of masked sources-suggesting that some fraction of the more luminous sources occupy more massive halos as satellites, or are possibly close pairs. We measure the cross-correlation power spectra between bands, finding that bands which are farthest apart are the least correlated, as well as hints of a reduction in the correlation between bands when resolved sources are more aggressively masked. In the second part of the paper, we attempt to interpret the measurements in the framework of the halo model. With the aim of fitting simultaneously with one model the power spectra, number counts, and absolute CIB level in all bands, we find that this is achievable by invoking a luminosity-mass relationship, such that the luminosity-to-mass ratio peaks at a particular halo mass scale and declines toward lower and higher mass halos. Our best-fit model finds that the halo mass which is most efficient at hosting star formation in the redshift range of peak star-forming activity, z {approx} 1-3, is log(M{sub peak}/M{sub Sun }) {approx} 12.1 {+-} 0.5, and that the minimum halo mass to host infrared galaxies is log(M{sub min}/M{sub Sun }) {approx} 10.1 {+-} 0.6.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A time-varying vulnerability index for COVID-19 in New Mexico, USA using generalized propensity scores

The coronavirus disease (COVID-19) pandemic has highlighted systemic inequities in the United States and resulted in a larger burden of negative social outcomes for marginalized communities. New Mexico, a state in the southwestern US, has a unique population with a large racial minority population and a high rate of poverty that may make communities more vulnerable to negative social outcomes from COVID-19. To identify which communities may be at the highest relative risk, we created a county-level vulnerability index. After the first COVID-19 case was reported in New Mexico on March 11, 2020, we fit a generalized propensity score model that incorporates sociodemographic factors to predict county-level viral exposure and thus, the generic risk to negative social outcomes such as unemployment or mental health impacts. We used four static sociodemographic covariates important for the state of New Mexico—population, poverty, household size, and minority population—and weekly cumulative case counts to iteratively run our model each week and normalize the exposure score to create a time-varying vulnerability index. We found the relative vulnerability between counties varied in the first eight weeks from the initial COVID-19 case before stabilizing. This framework for creating a location-specific vulnerability index in response to an ongoing disaster may be used as a quick, deployable metric to inform health policy decisions such as allocating state resources to the county level.

59 BASIC BIOLOGICAL SCIENCES↗