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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 OSTI2022

Organic carbon content and turnover time in relation to pedogenic iron and aluminum in four temperate forest soils sampled in 2007

Soil organic carbon (SOC) can be stabilized via association with iron (Fe) and aluminum (Al) minerals. Fe and Al can be strong predictors of SOC storage and turnover in soils with relatively high extractable metals content and moderately acidic to circumneutral pH. Here we test whether pedogenic Fe and Al influence SOC content and turnover in soils with low Fe and Al content and acidic pH. In soils from four sites spanning three soil orders, we quantified the amount of Fe and Al in operationally-defined poorly crystalline and organically-complexed phases using selective chemical dissolution applied to the soil fraction containing mineral-associated carbon. We evaluated the correlations of Fe and Al concentrations, mean annual precipitation (MAP), mean annual temperature (MAT), and pH with SOC content and 14C-based turnover times. We found that poorly crystalline Fe and Al content predicted SOC turnover times (p < 0.0001) consistent with findings of previous studies, while organically-complexed Fe and Al content was a better predictor of SOC concentration (p < 0.0001). Greater site-level MAP (p < 0.0001) and colder site-level MAT (p < 0.0001) were correlated with longer SOC turnover times but were not correlated with SOC content. Our results suggest that poorly crystalline Fe and Al effectively slow the turnover of SOC in these acidic soils, even when their combined content in the soil is less than 2% by mass. However, in the strongly acidic Spodosol, organo-metal complexes tended to be less stable resulting in a more actively cycling mineral-associated SOC pool. We characterized mineralogy and carbon content in soils from four forested sites in the eastern deciduous forest zone that span three soil orders. Using selective chemical dissolution applied to the soil fraction containing mineral-associated carbon, we quantified the amount of Fe and Al in operationally-defined poorly crystalline and organically-complexed phases, and correlated these with previously published SOC content and 14C-based turnover times (McFarlane et al. 2013). This dataset comprises site characterization undertaken in 2007 (.png) and subsequent soil analyses (.csv containing C, Fe, Al, 14C-derived TurnoverTime) concluded in 2010.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Modeling Economic Interdependence in Deterrence Using a Serious Game

In order to understand the effect of economic interdependence on conflict and on deterrents to conflict, and to assess the viability of online games as experiments to perform research, an online serious game was used to gather data on economic, political, and military factors in the game setting. These data were operationalized in forms analogous to variables from the real-world Militarized Interstate Disputes (MIDs) dataset. A set of economic predictor variables was analyzed using linear mixed effects regression models in an attempt to discover relationships between the predictor variables and conflict outcomes. Differences between the online game results and results from the real world are discussed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Power Plant Retirements: Trends and Possible Drivers

This paper synthesizes available data on historical and planned power plant retirements. Specifically, we present data on historical generation capacity additions and retirements over time, and the types of plants recently retired and planned for retirement. We then present data on the age of plants that have recently retired or that have plans to retire. We also review the characteristics of plants that recently retired or plan to retire vs. those that continue to operate, focusing on plant size, age, heat rate, and SO 2 emissions. Finally, we show the level of recent thermal plant retirements on a regional basis and correlate those data with a subset of possible factors that may be contributing to retirement decisions. This basic data synthesis cannot be used to precisely estimate the relative magnitude of retirement drivers. Nor do we explore every possible driver for retirement decisions. Moreover, future retirement decisions may be influenced by different factors than those that have affected past decisions. Nonetheless, it is clear that recently retired plants are relatively old, and that plants with stated planned retirement dates are—on average—no younger. We observe that retired plants are smaller, older, less efficient, and more polluting than operating plants. Based on simple correlation graphics, the strongest predictors of regional retirement differences appear to include SO 2 emissions rates (for coal), planning reserve margins (for all thermal units), variations in load growth or contraction (for all thermal units), and the age of older thermal plans (for all thermal units). Additional apparent predictors of regional retirements include the ratio of coal to gas prices and delivered natural gas prices. Other factors appear to have played lesser roles, including the penetration variable renewable energy (VRE), recent non-VRE capacity additions, and whether the region hosts an ISO/RTO.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Teaching Game Theory to Kids and Limits of Prediction

I have once been asked to read a lecture to a group of 6th graders on Game Theory. After agreeing to it, I realized that explaining the game theory basics to 6th graders my be difficult, given that terms such as Nash equilibrium, minimax, maximin, optimization may not resonate in a 6th grade classroom. Instead I've introduced game theory using the rock-paper-scissors (RPS) game. Turns out kids are excellent gametheoreticians. In RPS, they understood both the benefits of randomizing their own strategy and of predicting their opponent's moves. They offered a number of heuristics both for the prediction and opening move. These heuristics included optimizing against past opponent moves, such as not playing rock if the opponent just played scissors, and playing a specific opening hand, such as "paper". Visualizing the effects of such strategic choices on-the-fly would be interesting and educational. This brief essay attempts demonstrating and visualizing the value of a few different strategic options in RPS. Specifically, we would like to illustrate the following: 1) what is the value of being unpredictable?; and 2) what is the value of being able to predict your opponent? In regard to prediction of human players, the question 2) has been reflected in Jon McLoone's entry in Wolfram Blog from January 20, 2014[1]. McLoone created a predictive algorithm for playing against human opponents, that learns to beat human opponents reliably after approximately 30 - 40 games. I use McLoone's implementation to represent a predictive and random strategies. The rest of this documents 1) investigates performance of this predictive strategy against a random strategy (which is optimal in RPS) and in 2) attempts to turn this predictive power against the predictive strategy by allowing the opponent the full knowledge of the predictor's strategy (but not the choices made using the strategy). This exposes a weakness in predictions made without taking risks into account by illustrating that predictive strategy may make the predictor predictable as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗
DOE OSTI2020

Multiclass classification experiments

We seek a multiclass classifier that can satisfy the following requirements. 1. Predict among at least three classes. 2. Handle a moderately large number of correlated predictors. 3. Handle mixed categorical and continuous predictors. 4. Handle missing values (in some way). 5. Be trained with about n = 1,000 responses, and 6. Quantify prediction uncertainty. Based on 5. above, it is assumed that accuracy of prediction is paramount (relative to, say, runtime). All methods considered in this document take on the order of minutes (most take seconds) on a 2.8 GHz Quad-Core Intel Core i7 processor with 16GB RAM. Secondly, quantification of uncertainty is assumed to be a secondary task, since, with this small data size, resampling methods (i.e. bootstrapping) are possible. Further studies should quantify the feasibility and accuracy of resampling methods, and/or other methods, for quantifying uncertainty. R was used for all software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep Learning Prediction of Protein Complex Structures

Proteins interact to form protein complex to carry out biological functions such as catalytic chemical reaction. Therefore, it is important to develop computational methods to predict protein-protein interaction and the structures of protein complexes to study and enhance protein function. In this project, we successfully developed several deep learning methods to predict inter-protein contacts and the reinforcement learning and optimization methods to reconstruct protein complex structures from predicted inter-chain contacts. The methods were integrated with the MULTICOM protein complex structure prediction system and applied to predict the complex structures of biomass production-related proteins of green algae. During the two and a half years of research and development, all the specific milestones of the project were achieved successfully. 16 publications/manuscripts were produced. 10 software tools were developed. A patent application was submitted. Our MULTICOM predictors leveraging some tools developed in this project were ranked among the top predictors in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Product Innovation to Increase Low-to-Moderate-Income Customers' Adoption of Community Solar PV

This study aims to comprehensively analyze community solar project preferences for consumers and suppliers by conducting three distinct analyses. First we analyze the predictors of community solar contract adoption to understand how individual priorities affect the probability of adoption. Using an original data set of survey responses from potential community solar customers, we analyzed the predictors of contract adoption by employing a weighted logit model. We find that individuals who were previously familiar with community solar projects were significantly more likely to adopt than those who were not familiar. Secondly, a survey of community solar developers and financiers identified industry perceived barriers to community solar access and inclusion. Thirdly, we gathered payment performance information from community solar initiatives to measure how financial risks are perceived and how they interact with customer demographics. Our study is beneficial to the public by providing insights into the drivers and barriers of community solar adoption and sheds light on the importance of understanding individual priorities in designing effective community solar policies. The community solar industry has changed significantly since the beginning of this project. The industry continues to grow at a rapid rate, with an additional 7 gigawatts expected to come online between 2022 and 2027. With federal pressure to meet climate goals, as the harms of climate change continue to impact everybody, legislators are looking to community solar as a method to achieving their states energy policy goals. These new policies that push for low-to-moderate inclusion, coupled with the increase in community solar capacity illustrate a new era for the community solar industry. A number of policies have arisen in the last few months that push for more inclusive practices including the groundbreaking Solar for All program run by the EPA. The research created a “best practice” contract that can then be used, in conjunction with the manuscript and validated study, to pitch the industry on a more inclusive community solar product.

14 SOLAR ENERGY↗
DOE OSTI2025

wa-hls4ml and lui-gnn: A benchmark and GNN-based surrogate model for hls4ml resource and latency estimation

As machine learning (ML) increasingly serves as a tool for addressing real-time challenges in scientific applications, the development of advanced tooling has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as model synthesis, are now becoming limiting factors in the rapid iteration of designs. To reduce these emerging constraints, multiple efforts are being launched toward designing an ML-based surrogate model that estimates resource usage of synthesized accelerator architectures. This model would reduce the design iteration time, especially when designing within a set of given hardware constraints. This approach shows considerable potential, but as it stands, the effort is early and would benefit from coordination and standardization to assist future work as it emerges. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of more than 100,000 fully connected neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. In addition to the resource utilization and latency data provided, the dataset includes generated artifacts and log files for many of the synthesized neural networks, in order to support future research in ML-based code generation. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, as well as the average performance across a subset of the dataset. We measure the performance of a given predictor model through multiple metrics, including $R^2$ score and SMAPE on regression tasks, as well as inference time to further characterize the estimator under test. Additionally, we introduce the latency/utilization inference graph neural network (lui-gnn), a surrogate model that uses a graph neural network to represent input architectures in the form of a directed graph. This graph representation allows for a diverse set of model architectures to all be effectively handled by a surrogate model. We present the architecture and performance of the model, as evaluated by the new proposed benchmark, including SMAPE, $R^2$ score, and inference times, and find that lui-gnn generally predicts latency and utilization for the 75\% quantile within several percent of the synthesized resources on the synthetic test dataset, indicating that this approach of estimating resource and latency via a surrogate models has promise and warrants further research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

respiration↗
DOE OSTI2022

Sex comparisons in physiological and cognitive performance during hypoxic challenge

Within the tactical aviation community, human performance research lags in considering potential psychophysiological differences between male and female aviators due to little inclusion of females during the design and development of aircraft systems. A poor understanding of how male and female aviators differ with respect to human performance results in unknown potential sex differences on aeromedically relevant environmental stressors, perchance leading to suboptimal performance, safety, and health guidelines. For example, previous hypoxia studies have excluded female participants or lacked a sizeable sample to examine sex comparisons. As such, progress toward sensor development and improving hypoxia familiarization training are stunted due to limited knowledge of how individual differences, including sex, may or may not underlie hypoxia symptoms and performance impairment. Investigating sex differences bridges the gap between aerospace medicine and operational health, and addressing hypoxia is one of many facets yet to be studied. In the current study, we retrospectively examined N = 6 hypoxia studies with male-female participant samples (total, N = 189; male, n = 118; female, n = 71). We explored sex as a predictor of physiological response, sensory deficits, the severity of cognitive performance declines, and symptom manifestation via linear and binary logistic regression models. We found that the female sex predicted lower peripheral oxygen saturation and the likelihood of headache reporting in response to hypoxic challenge, yet explained little variance when combined with age and body mass index. The sensory and cognitive performance models did not converge, suggesting high intra-individual variability. Together, sex, age, and body mass index were not the most robust predictors in responses to hypoxic challenge; we cannot infer this for sensory deficits and cognitive performance within an experimentally induced hypoxic environment. The findings have implications for improving hypoxia familiarization training, monitoring sensor development, and emergency response and recovery protocols in case of a hypoxia occurrence suitable for all aircrew. We recommend continuing to elucidate the impact of sex and intrapersonal differences in hypoxia and other aeromedically relevant stressors in tactical aviation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Hybrid Solid Oxide Fuel Cell/Gas Turbine Model Development for Electric Aviation

A thermodynamic model was developed and validated to analyze a high-performance solid oxide fuel cell and gas turbine (SOFC-GT) hybrid power system for electric aviation. This study used a process simulation software package (ProMax) to study the role of SOFC design and operation on the feasibility and performance of the hybrid system. Standard modules, including compressor, turbine, heat exchanger, reforming reactor, and combustor were used from the ProMax tool suite while a custom module was created to simulate the SOFC stack. The model used an SOFC test data set as an input. Additional SOFC stack performance effects, such as pressure, temperature, and utilization of air and fuel, were added from open source data. System performance predictors were SOFC specific power, fuel-to-electricity conversion efficiency, and hybrid system efficiency. Using these input data and predictors, a static thermodynamic performance model was created that can be modified for different system configurations and operating conditions. Prior to creating the final aircraft performance model, initial demonstration models were developed to validate output results. We used the NASA SOFC model as a benchmark, which was created with their Numerical Propulsion System Simulator (NPSS) software framework. Our output results matched within 1% of both the NASA model and open source SOFC performance data. With confidence gained in the accuracy of this model, a 1-MW SOFC-GT hybrid power system was constructed for an aircraft propulsion concept. Overall hybrid system efficiencies of > 75% FTE were observed during standard 36,000 feet cruise flight conditions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗
DOE OSTI2025

Assessing Heterogeneity of Surface Water Temperature Following Stream Restoration and a High-Intensity Fire from Thermal Imagery

Thermal heterogeneity of rivers is essential to support freshwater biodiversity. Salmon behaviorally thermoregulate by moving from patches of warm water to cold water. When implementing river restoration projects, it is essential to monitor changes in temperature and thermal heterogeneity through time to assess the impacts to a river’s thermal regime. Lightweight sensors that record both thermal infrared (TIR) and multispectral data carried via unoccupied aircraft systems (UASs) present an opportunity to monitor temperature variations at high spatial (<0.5 m) and temporal resolution, facilitating the detection of the small patches of varying temperatures salmon require. Here, we present methods to classify and filter visible wetted area, including a novel procedure to measure canopy cover, and extract and correct radiant surface water temperature to evaluate changes in the variability of stream temperature pre- and post-restoration followed by a high-intensity fire in a section of the river corridor of the South Fork McKenzie River, Oregon. We used a simple linear model to correct the TIR data by imaging a water bath where the temperature increased from 9.5 to 33.4 °C. The resulting model reduced the mean absolute error from 1.62 to 0.35 °C. We applied this correction to TIR-measured temperatures of wetted cells classified using NDWI imagery acquired in the field. We found warmer conditions (+2.6 °C) after restoration (p < 0.001) and median absolute deviation for pre-restoration (0.30) to be less than both that of post-restoration (0.85) and post-fire (0.79) orthomosaics. In addition, there was statistically significant evidence to support the hypothesis of shifts in temperature distributions pre- and post-restoration (KS test 2009 vs. 2019, p < 0.001, D = 0.99; KS test 2019 vs. 2021, p < 0.001, D = 0.10). Moreover, we used a Generalized Additive Model (GAM) that included spatial and environmental predictors (i.e., canopy cover calculated from multispectral NDVI and photogrammetrically derived digital elevation model) to model TIR temperature from a transect along the main river channel. This model explained 89% of the deviance, and the predictor variables showed statistical significance. Collectively, our study underscored the potential of a multispectral/TIR sensor to assess thermal heterogeneity in large and complex river systems.

Barker, Matthew I. (ORCID:0000000252864930)↗
DOE OSTI2015

Spatial and Temporal Correlates of Greenhouse Gas Diffusion from a Hydropower Reservoir in the Southern United States

Emissions of CO 2 and CH 4 from freshwater reservoirs constitute a globally significant source of atmospheric greenhouse gases (GHGs), but knowledge gaps remain with regard to spatiotemporal drivers of emissions. We document the spatial and seasonal variation in surface diffusion of CO 2 and CH 4 from Douglas Lake, a hydropower reservoir in Tennessee, USA. Monthly estimates across 13 reservoir sites from January to November 2010 indicated that surface diffusions ranged from 236 to 18,806 mg m -2 day -1 for CO 2 and 0 to 0.95 mg m -2 day -1 for CH 4 . Next, we developed statistical models using spatial and physicochemical variables to predict surface diffusions of CO 2 and CH 4 . Models explained 22.7 and 20.9% of the variation in CO 2 and CH4 diffusions, respectively, and identified pH, temperature, dissolved oxygen, and Julian day as the most informative important predictors. These findings provide baseline estimates of GHG emissions from a reservoir in eastern temperate North America a region for which estimates of reservoir GHGs emissions are limited. Our statistical models effectively characterized non-linear and threshold relationships between physicochemical predictors and GHG emissions. Further refinement of such models will aid in predicting current GHG emissions in unsampled reservoirs and forecasting future GHG emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

The H I content of galaxies in groups and clusters as measured by ALFALFA

We present the H i content of galaxies in nearby groups and clusters as measured by the 70% complete Arecibo Legacy Fast-ALFA (ALFALFA) survey, including constraints from ALFALFA detection limits. Our sample includes 22 systems at distances between 70 and 160 Mpc over the mass range 12.5<log M/M{sub ⊙}<15.0, for a total of 1986 late-type galaxies. We find that late-type galaxies in the centers of groups lack H i at fixed stellar mass relative to the regions surrounding them. Larger groups show evidence of a stronger dependence of H i properties on environment, despite a similar dependence of color on environment at fixed stellar mass. We compare several environment variables to determine which is the best predictor of galaxy properties; group-centric distance r and r/R{sub 200} are similarly effective predictors, while local density is slightly more effective and group size and halo mass are slightly less effective. While both central and satellite galaxies in the blue cloud exhibit a significant dependence of H i content on local density, only centrals show a strong dependence on stellar mass, and only satellites show a strong dependence on halo mass. Finally, we see evidence that H i is deficient for blue cloud galaxies in denser environments even when both stellar mass and color are fixed. This is consistent with a picture where H i is removed or destroyed, followed by reddening within the blue cloud. Our results support the existence of pre-processing in isolated groups, along with an additional rapid mechanism for gas removal within larger groups and clusters, perhaps ram-pressure stripping.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evidence for the Late Arrival of Hot Jupiters in Systems with High Host-star Obliquities

It has been shown that hot Jupiters systems with massive, hot stellar primaries exhibit a wide range of stellar obliquities. On the other hand, hot Jupiter systems with low-mass, cool primaries often have stellar obliquities close to zero. Efficient tidal interactions between hot Jupiters and the convective envelopes present in lower-mass main-sequence stars have been a popular explanation for these observations. If this explanation is accurate, then aligned systems should be older than misaligned systems. Likewise, the convective envelope mass of a hot Jupiter's host star should be an effective predictor of its obliquity. We derive homogeneous stellar parameters—including convective envelope masses—for hot Jupiter host stars with high-quality sky-projected obliquity inferences. Using a thin-disk stellar population's Galactic velocity dispersion as a relative age proxy, we find that hot Jupiter host stars with larger-than-median obliquities are older than hot Jupiter host stars with smaller-than-median obliquities. The relative age difference between the two populations is larger for hot Jupiter host stars with smaller-than-median fractional convective envelope masses and is significant at the 3.6σ level. We identify stellar mass, not convective envelope mass, as the best predictor of stellar obliquity in hot Jupiter systems. The best explanation for these observations is that many hot Jupiters in misaligned systems arrived in the close proximity of their host stars long after their parent protoplanetary disks dissipated. The dependence of observed age offset on convective envelope mass suggests that tidal realignment contributes to the population of aligned hot Jupiters orbiting stars with convective envelopes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗