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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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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

ULTRADEEP IRAC IMAGING OVER THE HUDF AND GOODS-SOUTH: SURVEY DESIGN AND IMAGING DATA RELEASE

The IRAC ultradeep field and IRAC Legacy over GOODS programs are two ultradeep imaging surveys at 3.6 and 4.5 μm with the Spitzer Infrared Array Camera (IRAC). The primary aim is to directly detect the infrared light of reionization epoch galaxies at z > 7 and to constrain their stellar populations. The observations cover the Hubble Ultra Deep Field (HUDF), including the two HUDF parallel fields, and the CANDELS/GOODS-South, and are combined with archival data from all previous deep programs into one ultradeep data set. The resulting imaging reaches unprecedented coverage in IRAC 3.6 and 4.5 μm ranging from >50 hr over 150 arcmin{sup 2}, >100 hr over 60 sq arcmin{sup 2}, to ∼200 hr over 5–10 arcmin{sup 2}. This paper presents the survey description, data reduction, and public release of reduced mosaics on the same astrometric system as the CANDELS/GOODS-South Wide Field Camera 3 (WFC3) data. To facilitate prior-based WFC3+IRAC photometry, we introduce a new method to create high signal-to-noise PSFs from the IRAC data and reconstruct the complex spatial variation due to survey geometry. The PSF maps are included in the release, as are registered maps of subsets of the data to enable reliability and variability studies. Simulations show that the noise in the ultradeep IRAC images decreases approximately as the square root of integration time over the range 20–200 hr, well below the classical confusion limit, reaching 1σ point-source sensitivities as faint as 15 nJy (28.5 AB) at 3.6 μm and 18 nJy (28.3 AB) at 4.5 μm. The value of such ultradeep IRAC data is illustrated by direct detections of z = 7–8 galaxies as faint as H{sub AB} = 28.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Longitudinal Solid P Longitudinal Solid Polarized Target for CL get for CLAS12 and Study of Spin AS12 and Study of Spin Structure of Nucleons

A suite of experiments measuring target-spin observables in electron-nucleon scattering (dubbed Run Group C) was conducted at Jefferson Lab's Hall B in Newport News, VA with a new polarized nuclear target known as 'APOLLO' (Ammonia POLarized LOngitudinally). This innovative target is engineered to seamlessly integrate with the advanced 12GeV CEBAF (Continuous Electron Beam Accelerator Facility) accelerator and the Hall B CLAS12 (12 GeV CEBAF Large Acceptance Spectrometer) detector array. The 'APOLLO' target harnesses the power of Dynamic Nuclear Polarization (DNP) to achieve longitudinal polarization of solid ammonia, thereby creating a net polarization in both protons (NH3) and deuterons (ND3). These samples are subjected to a 5 Tesla magnetic field produced by the CLAS12 spectrometer central solenoid, cooled to 1 K using helium evaporation, and subsequently exposed to microwave radiation at a frequency of 140 GHz. This project was made possible through a collaborative partnership between the Jefferson Lab Target Group, Old Dominion University, University of Virginia, Christopher Newport University, and the CLAS Collaboration. Integrating the APOLLO target into CLAS12 presented unique challenges because of its specific spatial and dimensional constraints. This dissertation will highlight the innovative solutions developed to address these challenges, including the creation of a new target material transport system, the incorporation of superconducting magnetic correction coils, and the development of a customized Nuclear Magnetic Resonance (NMR) system. In addition to a detailed description of the development and operation of APOLLO, and an overview of RGC, this document also describes the global data fitting of proton asymmetries in the Deep Inelastic scattering (DIS) and the Resonance regions, and it offers insights into the comprehensive analysis of target polarization data.

Pandey, Pushpa↗
NASA NTRS1978

DSN command system Mark III-78

The Deep Space Network command Mark III-78 data processing system includes a capability for a store-and-forward handling method. The functions of (1) storing the command files at a Deep Space station; (2) attaching the files to a queue; and (3) radiating the commands to the spacecraft are straightforward. However, the total data processing capability is a result of assuming worst case, failure-recovery, or nonnominal operating conditions. Optional data processing functions include: file erase, clearing the queue, suspend radiation, command abort, resume command radiation, and close window time override.

Stinnett, W. G.↗
DOE OSTI2017

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Hohman, Frederick M.↗
DOE OSTI2025

Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.

AI↗
DOE OSTI2024

A Science Gateway for the Repeatable Analysis of Machine Learning Predicted Gravity Anomalies

In recent years, deep learning has become an increasingly popular alternative for modeling in geoscience applications due to its scalability and efficiency. However, the interpretability, compute, data volume, and hyperparameter tuning requirements of deep learning models make development and monitoring difficult. Furthermore, model explainability and communicating results obtained by these models to users or domain experts is a challenge, as domain experts in geoscience also need to have a deep understanding of how those models function in order to support their scientific works. Here, we describe a science gateway and machine learning pipeline for predicting gravity anomalies from geophysical data. The gateway, built on open-source technologies, provides a holistic view of the pipeline through interactive visualizations aimed at enabling efficient exploratory data analysis. The repeatability, reproducibility, and monitoring capabilities of this overall system allow us to iterate and analyze at scale. Using this pipeline and gateway, we can repeatedly produce accurate high-resolution gravity anomaly datasets. By describing the underlying technologies, implementation, and results, here we provide a foundation for the broader adoption of science gateways into cross-cutting geoscience and machine learning research projects as a means to improve the scientific discovery and collaboration in the geophysics and computational sciences community.

58 GEOSCIENCES↗
DOE OSTI2025

Contrastive learning for robust representations of neutrino data

In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalize effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. Published by the American Physical Society 2025

Wilkinson, Alex (ORCID:0000000253404506)↗
NASA NTRS2004

Deep Space Ka-band Link Management and the MRO Demonstration: Long-term Weather Statistics Versus Forecasting

During the last 40 years, deep space radio communication systems have experienced a move toward shorter wavelengths. In the 1960s a transition from L- to S-band occurred which was followed by a transition from S- to X-band in the 1970s. Both these transitions provided deep space links with wider bandwidths and improved radio metrics capability. Now, in the 2000s, a new change is taking place, namely a move to the Ka-band region of the radio frequency spectrum. Ka-band will soon replace X-band as the frequency of choice for deep space communications providing ample spectrum for the high data rate requirements of future missions. The low-noise receivers of deep space networks have a great need for link management techniques that can mitigate weather effects. In this paper, three approaches for managing Ka-band Earth-space links are investigated. The first approach uses aggregate annual statistics, the second one uses monthly statistics, and the third is based on the short-term forecasting of the local weather. An example of weather forecasting for Ka-band link performance prediction is presented. Furthermore, spacecraft commanding schemes suitable for Ka-band link management are investigated. Theses schemes will be demonstrated using NASA's Mars Reconnaissance Orbiter (MRO) spacecraft in the 2007 to 2008 time period, and the demonstration findings will be reported in a future publication.

Mars Reconnaissance Orbiter (MRO)↗
NASA NTRS2012

Entry, Descent, and Landing Communications for the 2011 Mars Science Laboratory

The Mars Science Laboratory (MSL), established as the most advanced rover to land on the surface of Mars to date, launched on November 26th, 2011 and arrived to the Martian Gale Crater during the night of August 5th, 2012 (PDT). MSL will investigate whether the landing region was ever suitable to support carbon-based life, and examine rocks, soil, and the atmosphere with a sophisticated suite of tools. This paper addresses the flight system requirement by which the vehicle transmitted indications of the following events using both X-band tones and UHF telemetry to allow identification of probable root causes should a mission anomaly have occurred: Heat-Rejection System (HRS) venting, completion of the cruise stage separation, turn to entry attitude, atmospheric deceleration, bank angle reversal commanded, parachute deployment, heatshield separation, radar ground acquisition, powered descent initiation, rover separation from the descent stage, and rover release. During Entry, Descent, and Landing (EDL), the flight system transmitted a UHF telemetry stream adequate to determine the state of the spacecraft (including the presence of faults) at 8 kbps initiating from cruise stage separation through at least one minute after positive indication of rover release on the surface of Mars. The flight system also transmitted X-band semaphore tones from Entry to Landing plus one minute although since MSL was occulted, as predicted, by Mars as seen from the Earth, Direct-To-Earth (DTE) communications were interrupted at approximately is approx. 5 min after Entry ( approximately 130 prior to Landing). The primary data return paths were through the Deep Space Network (DSN) for DTE and the existing Mars network of orbiting assets for UHF, which included the Mars Reconnaissance Orbiter (MRO), Mars Odyssey (ODY), and Mars Express (MEX) elements. These orbiters recorded the telemetry data stream and returned it back to Earth via the DSN. The paper also discusses the total power received during EDL and the robustness of the telecom design strategy used to ensure EDL communications coverage.

relay operations↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, and fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs welcome any ASPB members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, to foster collaborations with equity. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within the Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate with community members and set new standards for space-relevant data and metadata. The AWGs welcome any ASGSR members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are encouraged to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗