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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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NASA NTRSDate not supplied

VEG-04 PICK-AND-EAT CROP PRODUCTION AND HUMAN RESEARCH ON THE INTERNATIONAL SPACE STATION

Growing fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the astronaut dietary experience as we move toward longer-duration missions. Tending plants may also serve as a countermeasure for crew psychological stresses associated with spaceflight. However, requirements to support consistent growth of a variety of nutritious crops under spaceflight environmental conditions remain unclear. This study explores the potential to grow crops for consumption on the International Space Station (ISS) using the Veggie vegetable-production system. VEG-04A and B were two flight tests conducted in 2019 with the leafy green crop Mizuna mustard. Mizuna was grown in two Veggie chambers simultaneously, with the chambers set to different red-to-blue light formulations; one Veggie was programmed as “red-rich” and the second as “blue-rich.” Light quality is known to impact plant growth, nutrition, microbiology, and organoleptic characteristics on Earth, and the Veggie flight tests examined how these impacts might differ in microgravity. VEG-04A was a 35-day growth test with a single harvest. VEG-04B, a 58-day test with harvests at four, six, and eight weeks from the same plants, assessed sustained productivity. Challenges with the watering program occurred early during VEG-04A, and several plants failed to survive in both the flight and ground control operations. Thus, prior to VEG-04B, an extra test was conducted to tailor water timing and volumes. This test allowed a fine tuning of methods for VEG-04B, and generally plant survival was better in that experiment. At each harvest, the astronauts froze half of the edible plant tissue to return to Earth and weighed the remaining half using the ISS Mass Measurement Device (MMD). Weighed samples were then cleaned with produce-sanitizing wipes, and consenting crew members participated in organoleptic evaluation of the fresh produce. Organoleptic evaluations were conducted on the Mizuna crops grown in both light treatments, and data from these tests are compared to ground data collected at JSC. The remaining sanitized produce was available for crew consumption as desired. Frozen flight samples were returned for microbial and chemical analyses to assess food safety and nutritional quality. Microbial assessments included culturing and identifying aerobic bacteria, yeasts, and molds, and screening for specific human pathogens. Chemical nutrient analyses included assessing elements, antioxidants, and phenolics in plant tissues. Crew members involved in plant operations completed behavioral health surveys pre-flight, in-flight associated with plant growth and harvest activities, and post-flight. Surveys captured the amount of time spent on different plant-related operations, enjoyment of the different activities, engagement with Veggie, experience interacting with Veggie, and sensory stimulation associated with growing plants in Veggie. Within each study, plant growth did not differ across light treatment or location (flight versus ground). In general, more biomass was produced in most treatments during the longer study, but growth of this crop declined over time with the repeated harvests. On average, bacterial and fungal counts were significantly lower on ground control samples than flight samples, and microbial counts increased with repeated harvests. Light treatment did not influence any elements in tissues tested; however, the growth duration did impact levels of several elements. Organoleptic scores were generally higher in flight, and ground tasters considered samples more bitter. Amount of interaction and responses to Veggie varied widely by individual. Enjoyable tasks had higher impact than non-enjoyable tasks and interacting with Veggie was generally viewed as positive. These tests on ISS are helping to mitigate the risk of an inadequate food supply for long-duration missions by adding fresh vegetables and key nutrients to the crew diet, and indicating which plant care activities are providing behavioral health benefits for the crew. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call. Videos due Jan. 25th.

Veggie↗
NASA NTRSDate not supplied

Pick-and-Eat Space Crop Production Flight Testing on the International Space Station

Growing fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the astronaut dietary experience as we move toward longer-duration missions. However, requirements to support consistent growth of a variety of high-quality crops under spaceflight environmental conditions remain unclear. This study explores the potential to grow crops for consumption on the International Space Station (ISS) using Veggie, NASA’s vegetable production chamber system. VEG-04A and VEG-04B were two flight tests with ground components conducted with the leafy green crop mizuna mustard. In each location, mizuna was grown in two Veggie units simultaneously, with the chambers set to different red-to-blue-to-green light formulations. Preflight verification testing with various lighting treatments was conducted to down-select two treatments that contributed to the best desirable growth and sensory qualities in mizuna mustard. For the flight tests, one Veggie was programmed as “red-rich” with an average of 270 μmol m-2 s-1 of 630 nm red light, 30 μmol m-2 s-1 of 455 nm blue light, and 30 μmol m-2 s-1 of 530 nm green light. The second Veggie was “blue-rich” with an average of 150 µmol m-2 s-1 of 630 nm red light, 150 µmol m-2 s-1 of 455 nm blue light, and 30 µmol m-2 s-1 of 530 nm green light. Light quality is known to impact plant growth, nutrition, microbiology, and sensory characteristics on Earth, and the Veggie flight tests examined how these impacts might differ in microgravity. VEG-04A, a 35-day growth test with a single harvest, was initiated in June and harvested in July 2019. VEG-04B, a 56-day test with three harvests from the same plants, assessed sustained productivity. Preflight testing for VEG-04B was conducted after the VEG-04A flight test to improve the operations, approaches, and watering requirements, which resulted in better crop establishment in the VEG-04B flight test. VEG-04B was initiated in October 2019 with harvests at four, six, and eight weeks after initiation. At all of the harvests, the astronauts froze half of the edible plant tissue to return to Earth and weighed the remaining half using the Mass Measurement Device (MMD). Weighed samples were then cleaned with produce-sanitizing wipes, and consenting crew members participated in sensory evaluations of the fresh produce. The remaining sanitized produce was available for crew consumption as desired. Frozen flight samples were returned to Earth for chemical and microbial analyses to assess nutritional quality and food safety. Flight-grown mizuna was generally more acceptable to the crew and had higher nutrient levels, although mizuna grown in the ground control performed better in terms of higher yield and lower microbial load. This presentation will focus on results from the nutritional and sensory analyses, including how nutrients identified as key for supplementing the crew diet varied across lighting treatments, harvest approaches, and spaceflight versus ground conditions. It is our hope that these tests on the ISS will help mitigate the risk of an inadequate food supply for long-duration missions by adding fresh vegetables to the crew diet. This study was supported by NASA’s Human Research and Space Biology Programs through the HERO NNJ13ZSA002N-ILSRA grant solicitation.

Jess Bunchek↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the magnitude of 105, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but training only on simulated data results in poor performance on real data. Domain adaptation can bridge the gap between simulated and real datasets. We adopt domain adaptation on the estimation of Einstein radius in simulated galaxy-scale gravitational lensing images. We evaluate two domain adaptation techniques - domain adversarial neural networks (DANN) and maximum mean discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain with emulation of DES survey conditions. We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain datasets. Our results show the potential of using domain adaptation to perform analysis on future survey data with a deep neural network trained on simulated data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Managing Risk for the James Webb Space Telescope Deployment Mechanisms: Enabling First Light

The $10 billion James Webb Space Telescope (JWST) is perhaps the most ambitious astronomy mission in NASA history and is the largest and most powerful space science telescope optimized for infrared detection. It is operating in an L2 Orbit about a million miles away from the earth. This will complement and extend the discoveries of the Hubble Space Telescope. It was so big that it had to fold origami – style to fit in the rocket and open in space.Webb’s 5-layer tennis court sized sunshield assembly(SSA) protects the telescope from the Sun’s thermal radiation enabling instruments to operate about 230degree C below 0. It relayed the first amazing picture of galaxies to the world on 12 July 2022. Webb’s unprecedented infrared sensitivity will peer back in time over 13.5 billion years to see the first galaxies born after the Big Bang.

JWST↗