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Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.
We report the results of a 8000 s observation of the supernova remnant W44 using the ROSAT Position Sensitive Proportional Counter (PSPC). The image shows the same centrally peaked morphology observed by the Einstein IPC and contrasts with the shell-like radio morphology. The eastern limb shows a lack of X-ray emission within the radio shell, probably due to the interaction between the Supernova Remnants (SNR) and a molecular cloud. No counterpart to the pulsar 1853 + 01 in W44 has been detected, with L(sub X) less than 1.3 x 10(exp 32) ergs/s in the 0.2 to 2.4 keV band. The spectral analysis of the central part of W44, combining EXOSAT ME and Einstein SSS data, shows that the shocked plasma has not reached ionization equilibrium. The best nonequilibrium fit to PSPC, ME, and SSS spectra gives Eta = 10(exp 51) ergs cm(exp -6), T(sub s) = 10(exp 7) K with T(sub e) = T(sub i), suggesting conditions are approaching ionization equilibrium. There is no evidence of enhanced abundances of Mg, Si, S, or Fe. The variation of temperature and column density was obtained region by region using the PSPC and Einstein IPC. The temperature is largely uniform over the remnant, but strong column density variations are found to be consistent with molecular clouds in the line of sight. An evaporation model with a two-phase interstellar medium structure of clumps and interclump gas (White & Long 1991) can explain the X-ray centrally peaked morphology of W44. The clumps remaining behind a SN shock provide a reservoir of material, and evaporat e to increase the density of X-ray emitting gas in the interior of a SNR. The uniform temperature distribution of W44 strongly supports the predictions of this model. In addition, mosaiced H alpha and (S II) images of W44, taken using the prime focus universal extragalactic instrument (PFUEI) camera on the Palomar 60 sec telescope, reveal the first discovery of optical filaments (both H alpha and (S II)) in the northwestern and southeastern portion of the remnant, within the X-ray emitting region. The optical filaments and the X-ray image showing locally brighter emission and clumps along the filaments suggest both are produced by the interaction between the supernova shock front and regions of enhanced ambient density.
Topics include and overview, science objectives, study objectives, coronagraph types, metrics, ACCESS observatory, laboratory validations, and summary. Individual slides examine ACCESS engineering approach, ACCESS gamut of coronagraph types, coronagraph metrics, ACCESS Discovery Space, coronagraph optical layout, wavefront control on the "level playing field", deformable mirror development for HCIT, laboratory testbed demonstrations, high contract imaging with the HCIT, laboratory coronagraph contrast and stability, model validation and performance predictions, HCIT coronagraph optical layout, Lyot coronagraph on the HCIT, pupil mapping (PIAA), shaped pupils, and vortex phase mask experiments on the HCIT.
Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP) output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.
The impacts of present and potential future climate change will be one of the most important scientific and societal challenges in the 21st century. Given observed changes in temperature, sea ice, and sea level, improving our understanding of the climate system is an international priority. This system is characterized by complex phenomena that are imperfectly observed and even more imperfectly simulated. But with an ever-growing supply of climate data from satellites and environmental sensors, the magnitude of data and climate model output is beginning to overwhelm the relatively simple tools currently used to analyze them. A computational approach will therefore be indispensable for these analysis challenges. This chapter introduces the fledgling research discipline climate informatics: collaborations between climate scientists and machine learning researchers in order to bridge this gap between data and understanding. We hope that the study of climate informatics will accelerate discovery in answering pressing questions in climate science.
The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.
Thirty-five years after the discovery of rotation-powered pulsars, we still do not understand their pulsed emission at any wavelength. In the last few years there have been some fundamental developments in acceleration and emission models. I will review both the basic physics of the models as well as the latest developments in understanding the high-energy emission of rotation-powered pulsars. Special and general relativistic effects play important roles in pulsar emission, from inertial frame-dragging near the stellar surface to aberration, time-of-flight and retardation of the magnetic field near the light cylinder. Understanding how these effects determine what we observe at different wavelengths is critical to unraveling the emission physics. Fortunately two new gamma-ray telescopes, AGILE and GLAST, with launches expected this year will detect many new gamma-ray pulsars and test the predictions of these models with unprecedented sensitivity and energy resolution for gamma-rays in the range of 30 MeV to 300 GeV.
As the first gravity wave (GW) climatology study using nadir-viewing infrared sounders, 50 Atmospheric Infrared Sounder (AIRS) radiance channels are selected to estimate GW variances at pressure levels between 2-100 hPa. The GW variance for each scan in the cross-track direction is derived from radiance perturbations in the scan, independently of adjacent scans along the orbit. Since the scanning swaths are perpendicular to the satellite orbits, which are inclined meridionally at most latitudes, the zonal component of GW propagation can be inferred by differencing the variances derived between the westmost and the eastmost viewing angles. Consistent with previous GW studies using various satellite instruments, monthly mean AIRS variance shows large enhancements over meridionally oriented mountain ranges as well as some islands at winter hemisphere high latitudes. Enhanced wave activities are also found above tropical deep convective regions. GWs prefer to propagate westward above mountain ranges, and eastward above deep convection. AIRS 90 field-of-views (FOVs), ranging from +48 deg. to −48 deg. off nadir, can detect large-amplitude GWs with a phase velocity propagating preferentially at steep angles (e.g., those from orographic and convective sources). The annual cycle dominates the GW variances and the preferred propagation directions for all latitudes. Indication of a weak two-year variation in the tropics is found, which is presumably related to the Quasi-biennial oscillation (QBO). AIRS geometry makes its out-tracks capable of detecting GWs with vertical wavelengths substantially shorter than the thickness of instrument weighting functions. The novel discovery of AIRS capability of observing shallow inertia GWs will expand the potential of satellite GW remote sensing and provide further constraints on the GW drag parameterization schemes in the general circulation models (GCMs).
After discovery, the first task of exoplanet science is characterization. However experience has shown that the limited spectral range and resolution of most directly imaged exoplanet data requires an iterative approach to spectral modeling. Simple, brown dwarf-like models, must first be tested to ascertain if they are both adequate to reproduce the available data and consistent with additional constraints, including the age of the system and available limits on the planet's mass and luminosity, if any. When agreement is lacking, progressively more complex solutions must be considered, including non-solar composition, partial cloudiness, and disequilibrium chemistry. Such additional complexity must be balanced against an understanding of the limitations of the atmospheric models themselves. For example while great strides have been made in improving the opacities of important molecules, particularly NH3 and CH4, at high temperatures, much more work is needed to understand the opacity of atomic Na and K. The highly pressure broadened fundamental band of Na and K in the optical stretches into the near-infrared, strongly influencing the spectral shape of Y and J spectral bands. Discerning gravity and atmospheric composition is difficult, if not impossible, without both good atomic opacities as well as an excellent understanding of the relevant atmospheric chemistry. I will present examples of the iterative process of directly imaged exoplanet characterization as applied to both known and potentially newly discovered exoplanets with a focus on constraints provided by GPI spectra. If a new GPI planet is lacking, as a case study I will discuss HR 8799 c and d will explain why some solutions, such as spatially inhomogeneous cloudiness, introduce their own additional layers of complexity. If spectra of new planets from GPI are available I will explain the modeling process in the context of understanding these new worlds.
NASA is committed to transforming our aviation system to best meet demands and opportunities of the future. With a vision of safe, efficient, flexible, and environmentally sustainable air transportation, the NASA Aeronautics Research Mission Directorate is conducting research and development to address future needs of the aviation community, the Nation, and the world. While our NASA Aeronautics vision and strategy reaches into the next 25 years and beyond, we recognize that our vision and strategy must be responsive to new discoveries and emerging markets. For this reason, we are empowering our research community to redefine the future of aviation by dreaming up convergent/transformative ideas and studying if those ideas are possible. By modeling the new NASA Aeronautics' Convergent Aeronautics Solutions (CAS) Project after the venture capital community, we created opportunities for teams of intrapreneurs to mature their ideas into concepts through rapid feasibility studies. The CAS Project expects teams to consider the complexities and potential benefits of multi-disciplinary solutions and to leverage technology advances from outside the field of aeronautics. We also expect teams to explore their concepts in a rapid, iterative manner that allows them to learn and adjust their research approach. Within a year or two, teams are responsible for reporting on the feasibility of their concept. The findings inform NASA Aeronautics strategic planning and further investment. This presentation will give an overview of CAS.
Dimethyl sulfide (DMS), emitted from the oceans, is the most abundant biological source of sulfur to the marine atmosphere. Atmospheric DMS is oxidized to condensable products that form secondary aerosols that affect Earth’s radiative balance by scattering solar radiation and serving as cloud condensation nuclei. We report the atmospheric discovery of a previously unquantified DMS oxidation product, hydroperoxymethyl thioformate (HPMTF, HOOCH2SCHO), identified through global-scale airborne observations that demonstrate it to be a major reservoir of marine sulfur. Observationally constrained model results show that more than 30% of oceanic DMS emitted to the atmosphere forms HPMTF. Coincident particle measurements suggest a strong link between HPMTF concentration and new particle formation and growth. Analyses of these observations show that HPMTF chemistry must be included in atmospheric models to improve representation of key linkages between the biogeochemistry of the ocean, marine aerosol formation and growth, and their combined effects on climate.
Understanding biological impacts from spaceflight hazards and the subsequent development of countermeasures are a high priority to enable humanity to venture back to the Moon, and then to Mars and beyond. Experiments have been conducted with model organisms flown to space and analogous investigations terrestrially, to identify biological mechanistic impacts from spaceflight hazards and to develop mitigation countermeasures, thus contributing towards basic and applied science goals. However, sending organisms into space is a costly endeavor. To maximize scientific return, all biospecimens not required by spaceflight-relevant Principal Investigators are harvested, preserved, and archived in the NASA Biological Institutional Scientific Collection (NBISC). Biospecimens are collected and preserved according to well-established standard operating procedures to maintain scientific quality and are available on-request by the international scientific community. NBISC currently stores over 32,000 biospecimens from Shuttle, International Space Station, and ground-based space analog investigations. Tissue sharing has resulted in at least 33 publications since 2011 and 51 requests since 2016. Many requests for NBISC biospecimens come from first-time investigators who subsequently submit grants as their point-of-entry into the field of spaceflight biology and health. The NBISC biorepository is part of the NASA ‘Open Science for Life in Space’ collaborative group of projects, which includes NASA Genelab, the Space Biology Program’s Biospecimen Sharing Program, Physical Sciences Informatics, and the Ames Life Sciences Data Archive. NBISC biospecimens have been awarded to NASA Genelab, who then generated various open access science ‘omics datasets through the GeneLab Sample Processing laboratory, with resulting data widely used for biological study. Other NBISC biospecimen awards have led to studies on fecal microbiome analysis, DNA damage analysis using single-cell DNA sequencing, enzymatic-pathway identification involved in spaceflight muscle atrophy, and characterization of ocular morphological changes. Of note, NBISC is expanded to include a new Space Microbial Culture Collection (SMCC) for the collection, identification, documentation, long-term preservation, and distribution of space-related microbial isolates.
This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)
With the Voyager spacecrafts' discovery of low-frequency radio emissions from the depths of the outer heliosphere has come the realization that the boundaries between our heliosphere and the local interstellar medium have been detected. A model is presented here that can account for the observed radio emissions, based upon a termination shock modified by the dynamical effect of galactic and anomalous cosmic rays. Frequency and time domain properties of both continuum and transient radio events are explained, and new estimates for the distance to the termination shock (approximately 60-70 astronomical units) and the heliopause (less than or approximately 90 AU) are given.
This paper presents an introduction to the astrophysics of cosmic rays and diffuse gamma-rays and discusses some of the puzzles that have emerged recently due to more precise data and improved propagation models: the excesses in Galactic diffuse gamma-ray emission, secondary antiprotons and positrons, and the flatter than expected gradient of cosmic rays in the Galaxy. These also involve the dark matter, a challenge to modern physics, through its indirect searches in cosmic rays. Though the final solutions are yet to be found, I discuss some ideas and results obtained mostly with the numerical propagation model GALPROP. A fleet of spacecraft and balloon experiments targeting these specific issues is set to lift off in a few years, imparting a feeling of optimism that a new era of exciting discoveries is just around the corner. A complete and comprehensive discussion of all the recent results is not attempted here due to the space limitations.
The Galactic low-mass X-ray binary AT2019wey (ATLAS19bcxp, SRGAJ043520.9+552226, SRGEJ043523.3+552234, ZTF19acwrvzk) was discovered as a new optical transient in Dec 2019, and independently as an X-ray transient in Mar 2020. In this paper, we present comprehensive NICER, NuSTAR, Chandra, Swift, and MAXI observations of AT2019wey from ~1 year prior to the discovery to the end of September 2020. AT2019wey appeared as a ~ 1 mCrab source and stayed at this flux density for several months, displaying a hard X-ray spectrum that can be modeled as a power-law with photon index Γ ~ 1.8. In June 2020 it started to brighten and reached ~20 mCrab in ~2 months. The inclination of this system can be constrained to I ≲ 30° by modelling the reflection spectrum. Starting from late-August (~ 59082 MJD), AT2019wey entered into the hard-intermediate state (HIMS), and underwent a few week-long timescale outbursts, where the brightening in soft X-rays is correlated with the enhancement of a thermal component. Low-frequency quasi-periodic oscillation (QPO) was observed in the HIMS. We detect no pulsation and in timing analysis of the NICER and NuSTAR data. The X-ray states and power spectra of AT2019wey are discussed against the landscape of low-mass X-ray binaries.
The primary role of the Technical Integration Office is to provide technical solutions and services to different branches at KSC (Kennedy Space Center) and NASA program customers. The Technical Integration Office helps support KSC's operational needs by providing services such as digital connectivity, data center services, modelling and simulation tools, and communication video services. To learn the necessary technology and processes for my internship, I am working on two projects: learning C# (C Sharp programming language) with SQL and developing requirements for a PX (Communication and Public Engagement) inventory management system. To learn how to efficiently program with C#, my mentor assigned me to complete a sports informatics application that would let users discover facts and rules about various sports. The sports informatics application comes with search capabilities, report generating features, rule lists that users can modify, and diagrams for various sport strategies. To further build upon this project, I also developed a sport simulation game with the application. Once I begin more SQL-based projects, I will have the opportunity to learn how to manage databases and link SQL servers with C# programs. To develop requirements for the inventory management system, I have met with PX representatives and toured their storage facilities to see how they organize and store their items and equipment. I will also be meeting with representatives from the budget office to find out what information must be in a system budget report. The main components the system must have are customer request management, a search feature for items and equipment, report generation capabilities, and automated system warnings when item quantities reach or go below administrator-specified threshold levels. I have drafted questions and shall statements that will ultimately become part of the inventory management system requirements document.