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At least 37 records · Page 2

X-ray Observations of Cosmic Ray Acceleration

Since the discovery of cosmic rays, detection of their sources has remained elusive. A major breakthrough has come through the identification of synchrotron X-rays from the shocks of supernova remnants through imaging and spectroscopic observations by the most recent generation of X-ray observatories. This radiation is most likely produced by electrons accelerated to relativistic energy, and thus has offered the first, albeit indirect, observational evidence that diffusive shock acceleration in supernova remnants produces cosmic rays to TeV energies, possibly as high as the "knee" in the cosmic ray spectrum. X-ray observations have provided information about the maximum energy to which these shOCks accelerate electrons, as well as indirect evidence of proton acceleration. Shock morphologies measured in X-rays have indicated that a substantial fraction of the shock energy can be diverted into particle acceleration. This presentation will summarize what we have learned about cosmic ray acceleration from X-ray observations of supernova remnants over the past two decades.

Petre, Robert↗

Direct-imaging Discovery and Dynamical Mass of a Substellar Companion Orbiting an Accelerating Hyades Sun-like Star with SCExAO/CHARIS

We present the direct-imaging discovery of a substellar companion in orbit around a Sun-like star member of the Hyades open cluster. So far, no other substellar companions have been unambiguously confirmed via direct imaging around main-sequence stars in Hyades. The star HIP 21152 is an accelerating star as identified by the astrometry from the Gaia and Hipparcos satellites. We detected the companion, HIP 21152 B, in multiple epochs using the high-contrast imaging from SCExAO/CHARIS and Keck/NIRC2. We also obtained the stellar radialvelocity data from the Okayama 188 cm telescope. The CHARIS spectroscopy reveals that HIP 21152 B’s spectrum is consistent with the L/T transition, best fit by an early T dwarf. Our orbit modeling determines the semimajor axis and the dynamical mass of HIP 21152 B to be 17 +7.2 −3.8 and 27.8 +8.4 −5.4 M Jub , respectively. The mass ratio of HIP 21152 B relative to its host is ≈2%, near the planet/brown dwarf boundary suggested by recent surveys. Mass estimates inferred from luminosity-evolution models are slightly higher (33–42 MJup). With a dynamical mass and a well-constrained age due to the system’s Hyades membership, HIP 21152 B will become a critical benchmark in understanding the formation, evolution, and atmosphere of a substellar object as a function of mass and age. Our discovery is yet another key proof of concept for using precision astrometry to select direct-imaging targets.

Masayuki Kuzuhara↗

Earth's magnetosphere - Global problems in magnetospheric plasma physics

Magnetospheric physics is presently in a transition from the exploratory stage to one in which satellite missions and ground-based observations are planned with the specific object of achieving a global understanding and self-consistent quantitative description of the cause-and-effect relationship among the principal dynamic processes involved. Measurements turn to lower and lower energies and to higher ion mass species, in order to encompass the entire particle population, and to a broader range of the frequency spectrum of magnetic and electric field variations. In the present paper, the current status of our knowledge on magnetospheric plasma physics is reviewed, with particular reference of such fundamental advances as the discovery of layers of streaming plasma in the magnetosphere beneath its boundary surface, the identification of the terrestrial magnetosphere as a celestial source of kilometric radiation and relativistic particles, the identification of parallel electric field regions within the magnetosphere and their role in auroral particle acceleration, and the discovery of large fluxes of energetic heavy ions trapped in the magnetosphere.

Roederer, J. G.↗

Progress on the James Webb Space Telescope

I will describe the scientific program anticipated for the James Webb Space Telescope and the progress in its construction. When the JWST was conceived in 1995 it was expected to make its greatest contributions in the study of the first objects to form after the Big Bang, in the evolution of galaxies, and in the formation and evolution of stars and planetary systems. Since then, the age-distance-redshift relation has become clear with the precise measurement of the Hubble constant, the discovery of the accelerating universe, and the remarkable agreement of CMBR calculations with direct measurements of the large-scale structure. So what is left and what has changed? Galaxy formation and growth is still mysterious, star formation is still hidden, the dark matter and dark energy are still unobservable, and the tools at hand may or may not help enough. But the JWST, as a general-purpose observatory, will be available for imaginative use, and is just what Simon White's polemic seems to request. As an example, the JWST should be quite capable of observing transiting exoplanets with remarkable precision, even though there was no requirement to do so, and its coronagraphs will be very good even without a monolithic primary mirror. The JWST mission has now been officially approved by NASA and is in the Federal budget. It is planned for launch in 2014. Flight instruments will begin to arrive at Goddard in mid-2010, and the first flight mirror segments have already passed their first cryogenic tests. The flight detectors have been selected and have remarkable performance; for example, the near IR detectors have dark currents of the order of 10 electrons per pixel per hour.

Mather, John C.↗

POEMMA: Probe of Extreme Multi-Messenger Astrophysics

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) mission is being designed to establish charged-particle astronomy with ultra-high energy cosmic rays (UHECRs) and to observe cosmogenic tau neutrinos (CTNs). The study of UHECRs and CTNs from space will yield orders-of-magnitude increase in statistics of observed UHECRs at the highest energies, and the observation of the cosmogenic flux of neutrinos for a range of UHECR models. These observations should solve the long-standing puzzle of the origin of the highest energy particles ever observed, providing a new window onto the most energetic environments and events in the Universe, while studying particle interactions well beyond accelerator energies. The discovery of CTNs will help solve the puzzle of the origin of UHECRs and begin a new field of Astroparticle Physics with the study of neutrino properties at ultra-high energies.

Olinto, Angela V.↗

Mega-Drivers to Inform NASA Space Technology Strategic Planning

The National Aeronautics and Space Administration (NASA) Space Technology Mission Directorate (STMD) has been developing a new Strategic Framework to guide investment prioritization and communication of STMD strategic goals to stakeholders. STMD’s analysis of global trends identified four overarching drivers which are anticipated to shape the needs of civilian space research for years to come. These Mega-Drivers form the foundation of the Strategic Framework. The Increasing Access Mega-Driver reflects the increase in the availability of launch options, more capable propulsion systems, access to planetary surfaces, and the introduction of new platforms to enable exploration, science, and commercial activities. Accelerating Pace of Discovery reflects the exploration of more remote and challenging destinations, drives increased demand for improved abilities to communicate and process large datasets. The Democratization of Space reflects the broadening participation in the space industry, from governments to private investors to citizens. Growing Utilization of Space reflects space market diversification and growth. This paper will further describe the observable trends that inform each of these Mega Drivers, as well as the interrelationships between them within STMD’s new Strategic Framework.

Grande, Melanie L.↗

Mega-Drivers to Inform NASA Space Technology Strategic Planning

The National Aeronautics and Space Administration (NASA) Space Technology Mission Directorate (STMD) has been developing a new Strategic Framework to guide investment prioritization and communication of STMD strategic goals to stakeholders. STMD’s analysis of global trends identified four overarching drivers which are anticipated to shape the needs of civilian space research for years to come. These Mega-Drivers form the foundation of the Strategic Framework. The Increasing Access Mega-Driver reflects the increase in the availability of launch options, more capable propulsion systems, access to planetary surfaces, and the introduction of new platforms to enable exploration, science, and commercial activities. Accelerating Pace of Discovery reflects the exploration of more remote and challenging destinations, drives increased demand for improved abilities to communicate and process large datasets. The Democratization of Space reflects the broadening participation in the space industry, from governments to private investors to citizens. Growing Utilization of Space reflects space market diversification and growth. This paper will further describe the observable trends that inform each of these Mega Drivers, as well as the interrelationships between them within STMD’s new Strategic Framework.

Melanie L Grande↗

SCExAO CHARIS Direct Imaging Discovery of a 20 au Separation, Low-mass Ratio Brown Dwarf Companion to an Accelerating Sun-like Star

We present the direct imaging discovery of a substellar companion to the nearby Sun-like star, HD 33632 Aa, at a projected separation of ∼20 au, obtained with SCExAO/CHARIS integral field spectroscopy complemented by Keck/NIRC2 thermal infrared imaging. The companion, HD 33632 Ab, induces a 10.5σ astrometric acceleration on the star as detected with the Gaia and Hipparcos satellites. SCExAO/CHARIS JHK (1.1–2.4 μm) spectra and Keck/NIRC2 L(sub p) (3.78 μm) photometry are best matched by a field L/T transition object: an older, higher-gravity, and less dusty counterpart to HR 8799 cde. Combining our astrometry with Gaia/Hipparcos data and archival Lick Observatory radial velocities, we measure a dynamical mass of 46.4 ± 8 MJ and an eccentricity of e < 0.46 at 95% confidence. HD 33632 Ab’s mass and mass ratio (4.0% ± 0.7%) are comparable to the low-mass brown dwarf GJ 758 B and intermediate between the more massive brown dwarf HD 19467 B and the (near-)planet-mass companions to HR 2562 and GJ 504. Using Gaia to select for direct imaging observations with the newest extreme adaptive optics systems can reveal substellar or even planet-mass companions on solar system–like scales at an increased frequency compared to blind surveys

exoplanets↗

Giotto data analysis: Electron plasma and heavy ion composition measurements at Comet Halley

This investigation involved the analysis of electron plasma and heavy ion composition measurements made by the COPERNIC (COmplete Positive ion, Electron, and Ram Negative Ion measurements near Comet Halley) plasma experiment during the close fly-by of Halley by the European Space Agency's Giotto spacecraft. The experiment provided measurements of the full 3-dimensional distribution of 10 eV-30 keV electrons, and mass analysis of cold cometary ions from 10-210 amu. The analysis of the COPERNIC data has yielded some remarkable results, including: The discovery of negatively charged ions in the inner coma; the discovery of far heavier (mass is greater than 50 amu) ions than predicted, dominated by complex molecular ions made up of C, H, O, and N; the discovery of an adiabatic heating effect on electrons from the compression of the solar wind plasma; the identification of several organic and sulfur bearing ions; and the discovery of a new 'mystery region' where electrons are accelerated to high energies. These discoveries were in addition to the detailed analysis of 'expected' features at Comet Halley. Although this grant has expired, analysis continues on the data at a low (unfunded) level, and it is expected that more significant results will be obtained. A bibliography of the papers resulting from this research is attached, and a copy of each paper is included.

Lin, Robert P.↗

Using the NASA GeneLab Data System to Study the Metagenomes of Spaceships and Their Occupants

With humans pushing to live further off Earth for longer periods of time, it is increasingly important to understand the changes that occur in biological systems during spaceflight whether these be astronauts, their microbial commensals, or their plant-based life support systems. In a three-part presentation, we discuss GeneLab and recent discoveries regarding the microbiota of spacecrafts and space-flown animals. Part 1: GeneLab: Open Science for Life in Space, Jonathan Galazka, NASA Ames Research Center To accelerate the pace of discovery from precious spaceflight biological experiments, NASA as develop the GeneLab data system (genelab.nasa.gov), which allows unfettered access to omics data from spaceflight and spaceflight relevant experiments. GeneLab houses metagenomic datasets from spacecraft and relevant spacecraft models. Users can download this data and associated metadata to make new discoveries about how microbial communities may change and adapt to spaceflight.

Galazka, Jonathan M.↗

Shock acceleration of nuclei and electrons in the heliosphere beyond 24 AU

Radially propagating shocks of solar flare origin are analyzed using plasma and charged particle measurements on Pioneer 10 to show that protons and helium nuclei are continuously accelerated to energies of up to roughly 70 MeV per nucleon. The discovery that electrons are simultaneously accelerated to relativistic energies places new constraints on models for acceleration by these quasi-perpendicular shocks in the outer heliosphere.

Pyle, K. R.↗

Rapidly Assessing Changes in Bone Mineral Balance Using Natural Stable Calcium Isotopes

We demonstrate that variations in the Ca isotope ratios in urine rapidly and quantitatively reflect changes in bone mineral balance. This variation occurs because bone formation depletes soft tissue of light Ca isotopes, while bone resorption releases that isotopically light Ca back into soft tissue. In a study of 12 individuals confined to bed rest, a condition known to induce bone resorption, we show that Ca isotope ratios shift in a direction consistent with net bone loss after just 7 days, long before detectible changes in bone density occur. Consistent with this interpretation, the Ca isotope variations track changes observed in N-teleopeptide, a bone resorption biomarker, while bone-specific alkaline phosphatase, a bone formation biomarker, is unchanged. Ca isotopes can in principle be used to quantify net changes in bone mass. Ca isotopes indicate an average loss of 0.62 +/- 0.16 % in bone mass over the course of this 30-day study. The Ca isotope technique should accelerate the pace of discovery of new treatments for bone disease and provide novel insights into the dynamics of bone metabolism.

Morgan, J. L. L.↗

Beyond Ecosystem Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model Integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.

Istem Fer↗

Open Science for Life in Space

Understanding how biology changes in response spaceflight and how these changes affect crew and craft is important to the design of safe, robust and effective space missions. Advances in analytical technologies allow large volumes of new data to be generated from space biological experiments, and a growing library of legacy data can be re-analyzed in the context of a greater understanding of biological systems. This enables powerful insights into how biology changes during spaceflight. However, for this to be fully realized additional tools, programs, and communities must be developed. Now a developing suite of interconnected open science resources (GeneLab, the Ames Life Sciences Data Archive, the NASA Biological Institutional Science Collection, the Biospecimen Sharing Program) can be leveraged by dedicated Analysis Working Groups to accelerate the pace of discovery in the space biological sciences.

Open Science↗

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↗

Accelerating Large Data Analysis By Exploiting Regularities

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Moran, Patrick J.↗