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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

The Discovery of a Second Luminous Low Mass X-Ray Binary System in the Globular Cluster M15

Using the Chandra X-ray Observatory we have discovered a second bright X-ray source in the globular cluster M15 that is 2.7" to the west of AC211, the previously known low mass X-ray binary (LMXB) in this system. Prior to the 0.5" imaging capability of Chandra this second source could not have been resolved from AC211. The luminosity and spectrum of this new source, which we call M15-X2, are consistent with it also being a LMXB system. This is the first time that two LMXBs have been seen to be simultaneously active in a globular cluster. The new source, M15-X2, is coincident with a 18th U magnitude very blue star. The discovery of a second LMXB in M15 clears up a long standing puzzle where the X-ray and optical properties of AC211 appear consistent with the central source being hidden behind an accretion disk corona, and yet also showed a luminous X-ray burst suggesting the neutron star is directly visible. This discovery suggests instead that the X-ray burst did not come from AC211, but rather from the newly discovered X-ray source. We discuss the implications of this discovery for X-ray observations of globular clusters in nearby galaxies.

White, Nicholas E.↗
NASA NTRS2000

STS-92 Meal - Suit up - Depart O&C - Launch Discovery On Orbit - Landing - Crew Egress

The video begins with the introduction of the crew of Space Shuttle Discovery on STS-92, at their customary pre-flight meal. The crew consists of Commander Brian Duffy, Pilot Pamela Melroy, and Mission Specialists Leroy Chiao, William McArthur, Peter "Jeff" Wisoff, Michael Lopez-Alegria, and Koichi Wakata. The introduction and suit-up of the astronauts, and their departure in the Astrovan are shown at a quick pace. The video shows in detail the seating of the crew and each astronaut's final preparations in the White Room prior to boarding. Views of Discovery's night launch include: SLF Convoy, Beach Tracker, VAB, Pad Perimeter, Tower-1, UCS-15, Press Site, UCS-23, OTV-61, OTV-70, OTV-71, and the In-Cabin Ascent Camera. While in orbit, the Discovery orbiter docks with the International Space Station (ISS). The docking is shown in a series of still images. The video includes clips from four extravehicular activities (EVAs). The crew members who performed the EVAs comment on them while speaking to Mission Control. During the EVAs, the Z1 Truss and an antenna are attached to the ISS. The crew members on the fourth EVA test jet packs. Views of landing include: TV-1, TV-2, TV-3, LRO-1, and HUD.

Source record↗
NASA NTRS2001

STS-103 Photo-op/Suit-up/Depart O&C/Launch Discovery On-Orbit/Landing/Crew Egress

The crew of the STS-103 space mission are shown as they prepare for launch and landing of the space shuttle discovery. The crew includes: Commander Curtis L. Brown, Pilot Scott J. Kelly, and Mission Specialists: Steven L. Smith, C. Michael Foale, John M. Grunsfeld, Claude Nicollier and Jean-francois Clervoy. Footage of these astronauts during suit-up, Ingress, Egress and Operations and Checkout (O&C) is presented. Live footage of the countdown and launch of the space shuttle discovery is shown from various vantage points such as the Tower 1, Vehicle Assembly Building (VAB), and pad perimeter. Once in orbit, a presentation of Steven Smith, John Grunsfield, Michael Foale and Claude Nicollier performing three spacewalks to repair the HST is shown. The landing of the space shuttle discovery at the Kenndy Space Center is shown.

Source record↗
NASA NTRS2005

STS-114: Discovery Impromptu Briefing

Dr. Griffin, NASA Administrator, is accompanied by members of The U.S. House of Representatives in this STS-114 Discovery Impromptu briefing. The U.S. House of Representatives present include: Sherwood Boehlert, House Science Committee Chairman, Senator Hutchinson, Sheila Jackson, 18th Congressional District Texas, Al Green, 9th Congressional District, Representative Jim Davis, Florida, and Gene Green, 29th District, Texas. Griffin talks about the problem that occurred with the external fuel tank sensor of the Space Shuttle Discovery and the effort NASA is pursuing to track the problem, and identify the root cause. He answers questions from the news media about the next steps for the Space Shuttle Discovery, time frame for the launch, and activities for the astronauts for the next few days.

Source record↗
NASA NTRS2005

The Challenges of Interoperable Data Discovery

The Global Change Master Directory (GCMD) assists the oceanographic community in data discovery and access through its online metadata directory. The directory also offers data holders a means to post and search their oceanographic data through the GCMD portals, i.e. online customized subset metadata directories. The Gulf of Maine Ocean Data Partnership (GoMODP) has expressed interest in using the GCMD portals to increase the visibility of their data holding throughout the Gulf of Maine region and beyond. The purpose of the Gulf of Maine Ocean Data Partnership (GoMODP) is to "promote and coordinate the sharing, linking, electronic dissemination, and use of data on the Gulf of Maine region". The participants have decided that a "coordinated effort is needed to enable users throughout the Gulf of Maine region and beyond to discover and put to use the vast and growing quantities of data in their respective databases". GoMODP members have invited the GCMD to discuss further collaborations in view of this effort. This presentation. will focus on the GCMD GoMODP Portal - demonstrating its content and use for data discovery, and will discuss the challenges of interoperable data discovery. interoperability among metadata standards and vocabularies will be discussed. A short overview of the lessons learned at the Marine Metadata Interoperability (MMI) metadata workshop held in Boulder, Colorado on August 9-11, 2005 will be given.

Meaux, Melanie F.↗
NASA NTRS2006

STS-121: Discovery L-1 Countdown Status Briefing

Bruce Buckingham, NASA Public Affairs, introduces Jeff Spaulding, NASA Test Director; Debbie Hahn, STS-121 Payload Manager; and Kathy Winters, Shuttle Weather Officer. Spaulding gives his opening statement on this one day prior to the launching of the Space Shuttle Discovery. He discusses the following topics: 1) Launch of the Space Shuttle Discovery; 2) Weather; 3) Load over of onboard reactants; 4) Hold time for liquid hydrogen; 5) Stowage of Mid-deck completion; 6) Check-out of onboard and ground network systems; 7) Launch windows; 8) Mission duration; 9) Extravehicular (EVA) plans; 10) Space Shuttle landing day; and 11) Scrub turn-around plans. Hahn presents and discusses a short video of the STS-121 payload flow. Kathy Winters gives her weather forecast for launch. She then presents a slide presentation on the following weather conditions for the Space Shuttle Discovery: 1) STS-121 Tanking Forecast; 2) Launch Forecast; 3) SRB Recovery; 4) CONUS Launch; 5) TAL Launch; 6) 24 Hour Delay; 7) CONUS 24 Hour; 8) TAL 24 Hour; 9) 48 Hour Launch; 10) CONUS 48 Hour; and 11) TAL 48 Hour. The briefing ends with a question and answer period from the media.

Source record↗
NASA NTRS2013

Discovery: Under the Microscope at Kennedy Space Center

The National Aeronautics & Space Administration (NASA) is known for discovery, exploration, and advancement of knowledge. Since the days of Leeuwenhoek, microscopy has been at the forefront of discovery and knowledge. No truer is that statement than today at Kennedy Space Center (KSC), where microscopy plays a major role in contamination identification and is an integral part of failure analysis. Space exploration involves flight hardware undergoing rigorous "visually clean" inspections at every step of processing. The unknown contaminants that are discovered on these inspections can directly impact the mission by decreasing performance of sensors and scientific detectors on spacecraft and satellites, acting as micrometeorites, damaging critical sealing surfaces, and causing hazards to the crew of manned missions. This talk will discuss how microscopy has played a major role in all aspects of space port operations at KSC. Case studies will highlight years of analysis at the Materials Science Division including facility and payload contamination for the Navigation Signal Timing and Ranging Global Positioning Satellites (NA VST AR GPS) missions, quality control monitoring of monomethyl hydrazine fuel procurement for launch vehicle operations, Shuttle Solids Rocket Booster (SRB) foam processing failure analysis, and Space Shuttle Main Engine Cut-off (ECO) flight sensor anomaly analysis. What I hope to share with my fellow microscopists is some of the excitement of microscopy and how its discoveries has led to hardware processing, that has helped enable the successful launch of vehicles and space flight missions here at Kennedy Space Center.

Howard, Philip M.↗
NASA NTRS2016

Discovery of a Makemakean Moon

We describe the discovery of a satellite in orbit about the dwarf planet (136472) Makemake. This satellite, provisionally designated S/2015 (136472) 1, was detected in imaging data collected with the Hubble Space Telescope's Wide Field Camera 3 on UTC 2015 April 27 at 7.80 +/- 0.04 mag fainter than Makemake and at a separation of 0farcs57. It likely evaded detection in previous satellite searches due to a nearly edge-on orbital configuration, placing it deep within the glare of Makemake during a substantial fraction of its orbital period. This configuration would place Makemake and its satellite near a mutual event season. Insufficient orbital motion was detected to make a detailed characterization of its orbital properties, prohibiting a measurement of the system mass with the discovery data alone. Preliminary analysis indicates that if the orbit is circular, its orbital period must be longer than 12.4 days and must have a semimajor axis > or approx. = 21,000 km. We find that the properties of Makemake's moon suggest that the majority of the dark material detected in the system by thermal observations may not reside on the surface of Makemake, but may instead be attributable to S/2015 (136472) 1 having a uniform dark surface. This dark moon hypothesis can be directly tested with future James Webb Space Telescope observations. We discuss the implications of this discovery for the spin state, figure, and thermal properties of Makemake and the apparent ubiquity of trans-Neptunian dwarf planet satellites.

Parker, Alex H.↗
NASA NTRS2018

How Do You Go From a Concept Idea to a NASA Selected Mission? Formulating the Psyche Discovery Mission with JPL's Concurrent Engineering Teams

JPL’s Office of Formulation provides continuity of support and access to domain subject matter experts, as Principal Investigators mature their mission concepts from “cocktail napkin” ideas to Preliminary Design Reviews [1]. Using NASA’s Psyche mission as a case study, we describe JPL’s concurrent engineering A-Team and Team X support to the Psyche competed concept study team in the areas of 1) Initial Feasibility, 2) Trade Space Exploration, 3) Spacecraft Point Design and Cost Estimate, 4) Science, Technical, Management, and Cost Review, and 5) Strategy and Communication Development. NASA’s Psyche Discovery-class mission started as a grassroots idea from Principal Investigator L.T. ElkinsTanton. Is there a compelling Discovery mission to visit the interior of a body for the first time, by sending a mission to an iron metal asteroid? In less than five years the Psyche concept was selected as a mission under NASA’s Discovery Program. While Psyche had a dedicated concept development team [2], they utilized JPL’s concurrent engineering teams, methods, analysis tools, and subject matter experts throughout their mission concept formulation lifecycle

Ziemer, John↗
NASA NTRSDate not supplied

Populating a Graph Database to Run a Usage-Based Discovery Tool

Most dataset discovery tools for Earth Observation data rely on descriptions and other metadata of the datasets, using keyword searches or attribute filtering to determine relevance. However, these descriptions often do not include the potential uses of the data. Thus, a user working on floods will rarely see few if any rainfall datasets show up in such a search. The Usage Based Discovery tool, on the other hand, offers usage instances to the user, either research articles or applications, along with the datasets that those usage instances used. This allows a user, particularly one new to the world of Earth Observation data, to investigate which datasets are used in similar cases. The information that powers Usage-Based Discovery is a graph database of relationships of usage to dataset and usage to topic, allowing the user to narrow their search for similar cases. In order to scale out to a graph database rich enough to provide a satisfactory user experience, we combine manual and automated processes to populate the graph. The initial content of the graph has been seeded primarily via human-aided data curation methods, using sites like Google Scholar. To scale up this effort, we’ve employed crowdsourcing. It is easy for anyone to contribute to our graph using their Open Researcher and Contributor Identifier for authorization. We’re now experimenting with Machine Learning and Natural Language Processing to help automate population of the graph, starting with the classification of research articles by topic. Finding adequate training data in the absence of a comprehensive and open research article API continues to be a significant challenge.

Vincent Inverso↗
NASA NTRSDate not supplied

Integrating Multi-agency Data Products in a Cloud-based Platform for Streamlined Discovery, Visualization, and Use

Earth science data users almost always have an interest in utilizing geospatial data from multiple agencies. As computing capability and cloud-based infrastructures accelerate the pace at which scientific research can be done, there is a growing need to enable search, discovery, and use of multi-agency geospatial observations relevant for a common use case - without undergoing the search and discovery process in a less efficient, disparate path with each agency. NASA’s Earth Observing System Data and Information System (EOSDIS) and NOAA’s National Environmental Satellite, Data and Information Service (NESDIS) both support a wide range of Earth science disciplines’ research, operations, and applications activities. Presently, however, there are few examples of data discovery frameworks supporting an inquiry of both NASA’s and NOAA’s extensive archives of Earth observations that are equally suitable for a particular science scenario, regardless of the agency that “owns” the data. NASA and NOAA are collaborating on a data expedition platform for exploring fire weather using data products from both agencies. Users will be able to search, discover, and visualize NASA and NOAA products in one interface. Each agency will curate metadata for its respective datasets, providing for a rich search experience. The collaboration will pilot a shared search interface into these metadata datastores. Data products will be stored in the cloud in cloud-optimized format(s). These formats will allow for optimized data access and visualization to support the “data expedition”. Avenues for further development and application of this cloud-based, multi-agency data provisioning platform will also be discussed.

cloud-based technology↗
NASA NTRS2021

Discovery and Confirmation of the Shortest Gamma-Ray Burst from a Collapsar

Gamma-ray bursts (GRBs) are among the brightest and most energetic events in the Universe. The duration and hardness distribution of GRBs has two clusters, now understood to reflect (at least) two different progenitors. Short-hard GRBs (SGRBs; T(sub 90) < 2 s) arise from compact binary mergers, and long-soft GRBs (LGRBs; T(sub 90) > 2 s) have been attributed to the collapse of peculiar massive stars (collapsars). The discovery of SN 1998bw/GRB 980425 marked the first association of an LGRB with a collapsar, and AT 2017gfo/GRB 170817A/GW170817 marked the first association of an SGRB with a binary neutron star merger, which also produced a gravitational wave. Here, we present the discovery of ZTF20abwysqy (AT2020scz), a fast-fading optical transient in the Fermi satellite and the Interplanetary Network localization regions of GRB 200826A; X-ray and radio emission further confirm that this is the afterglow. Follow-up imaging (at rest-frame 16.5 days) reveals excess emission above the afterglow that cannot be explained as an underlying kilonova, but which is consistent with being the supernova. Although the GRB duration is short (rest-frame T90 of 0.65 s), our panchromatic follow-up data confirm a collapsar origin. GRB 200826A is the shortest LGRB found with an associated collapsar; it appears to sit on the brink between a successful and a failed collapsar. Our discovery is consistent with the hypothesis that most collapsars fail to produce ultra-relativistic jets.

High-energy astrophysics↗
NASA NTRS2020

Trident: The Path to Triton on a Discovery Budget

This paper describes Trident, a proposed Discovery mission to Neptune’s moon Triton, 30 AU from the Sun. Triton formed in the Kuiper Belt but was captured by Neptune into a highly-inclined retrograde orbit, where tidal forces thawed its interior, forming an ocean that likely persists to the present day. Recent outer solar system missions like Cassini and New Horizons have yielded completely new models for processes on ocean worlds, active worlds, and KBOs. Triton isn’t just a key to solar system science, it’s a whole keyring: a singular captured KBO and evolved ocean world, with active plumes, an energetic ionosphere, and a young unique surface. The NASA OPAG Roadmap to Ocean Worlds identifies Triton as the highest priority candidate ocean world [1], ripe for investigation. The Trident mission concept is an excellent case study in “design to cost”: we show how exploration of Triton under Discovery is made possible by radioisotope power combined with a rare, extremely efficient Jupiter gravity assist, enabling a simple, lowmass spacecraft design on a ballistic trajectory. The Triton encounter sequence probes for an ocean, measures the ionosphere, and views nearly the whole of Triton as it traverses a single orbit around Neptune, mapping the >60% of the surface that is as yet unseen. The Triton encounter concludes with fullframe imaging illuminated by “Neptune-shine” when Trident is in Neptune eclipse, for direct comparison with Voyager 2’s observations nearly 50 years prior. Trident carries a mature complement of instruments: a Magnetometer, IR Spectrometer & Narrow Angle Camera, Wide-Angle Camera, Plasma Spectrometer, and telecom hardware-enabled Radio Science. The flight system design integrates heritage components from Ball with JPL leadership and expertise in key specialty areas to provide a Voyager-like, robust spacecraft commensurate with a Discovery cost and risk tolerance. Cost avoidance features include: use of existing instrument designs, small blowdown monopropellant propulsion system, a contributed X-band telecommunications system that also performs radio science, and a simple power system. Additionally, the passive thermal design accommodates the large solar dynamic range from Venus to Neptune by using the HGA for shade when close to the sun, and MMRTG excess heat, modulated by louvers.

Frazier, William↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗
DOE OSTI2024

Target–decoy false discovery rate estimation using Crema

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗
DOE OSTI2021

AI-based language models powering drug discovery and development

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations

Data-driven discovery of differential equations has been an emerging research topic. We propose a novel algorithm subsampling-based threshold sparse Bayesian regression (SubTSBR) to tackle high noise and outliers. The subsampling technique is used for improving the accuracy of the Bayesian learning algorithm. It has two parameters: subsampling size and the number of subsamples. When the subsampling size increases with fixed total sample size, the accuracy of our algorithm goes up and then down. When the number of subsamples increases, the accuracy of our algorithm keeps going up. We demonstrate how to use our algorithm step by step and compare our algorithm with threshold sparse Bayesian regression (TSBR) for the discovery of differential equations. We show that our algorithm produces better results. We also discuss the merits of discovering differential equations from data and demonstrate how to discover models with random initial and boundary condition as well as models with bifurcations. The numerical examples are: (1) predator-prey model with noise, (2) shallow water equations with outliers, (3) heat diffusion with random initial and boundary condition, and (4) fish-harvesting problem with bifurcations.

97 MATHEMATICS AND COMPUTING↗