Complexity Heliophysics: A lived and living history of systems and complexity science in Heliophysics

In this piece we study complexity science in the context of Heliophysics, describing it not as a discipline, but as a paradigm. In the context of Heliophysics, complexity science is the study of a star, interplanetary environment, magnetosphere, upper and terrestrial atmospheres, and planetary surface as interacting subsystems. Complexity science studies entities in a system (e.g., electrons in an atom, planets in a solar system, individuals in a society) and their interactions, and is the nature of what emerges from these interactions. It is a paradigm that employs systems approaches and is inherently multi- and cross-scale. Heliophysics processes span at least 15 orders of magnitude in space and another 15 in time, and its reaches go well beyond our own solar system and Earth's space environment to touch planetary, exoplanetary, and astrophysical domains. It is an uncommon domain within which to explore complexity science. After first outlining the dimensions of complexity science, the review proceeds in three epochal parts: 1) A pivotal year in the Complexity Heliophysics paradigm: 1996; 2) The transitional years that established foundations of the paradigm (1996-2010); and 3) The emergent literature largely beyond 2010. This review article excavates the lived and living history of complexity science in Heliophysics. The intention is to provide inspiration, help researchers think more coherently about ideas of complexity science in Heliophysics, and guide future research. It will be instructive to Heliophysics researchers, but also to any reader interested in or hoping to advance the frontier of systems and complexity science

GSFC Heliophysics Science Division 2008 Science Highlights

This report is intended to record and communicate to our colleagues, stakeholders, and the public at large about heliophysics scientific and flight program achievements and milestones for 2008, for which NASA Goddard Space Flight Center's Heliophysics Science Division (HSD) made important contributions. HSD comprises approximately 261 scientists, technologists, and administrative personnel dedicated to the goal of advancing our knowledge and understanding of the Sun and the wide variety of domains that its variability influences. Our activities include Lead science investigations involving flight hardware, theory, and data analysis and modeling that will answer the strategic questions posed in the Heliophysics Roadmap; Lead the development of new solar and space physics mission concepts and support their implementation as Project Scientists; Provide access to measurements from the Heliophysics Great Observatory through our Science Information Systems, and Communicate science results to the public and inspire the next generation of scientists and explorers

2015 Science Mission Directorate Technology Highlights

The role of the Science Mission Directorate (SMD) is to enable NASA to achieve its science goals in the context of the Nation's science agenda. SMD's strategic decisions regarding future missions and scientific pursuits are guided by Agency goals, input from the science community including the recommendations set forth in the National Research Council (NRC) decadal surveys and a commitment to preserve a balanced program across the major science disciplines. Toward this end, each of the four SMD science divisions -- Heliophysics, Earth Science, Planetary Science, and Astrophysics -- develops fundamental science questions upon which to base future research and mission programs. Often the breakthrough science required to answer these questions requires significant technological innovation, e.g., instruments or platforms with capabilities beyond the current state of the art. SMD's targeted technology investments fill technology gaps, enabling NASA to build the challenging and complex missions that accomplish groundbreaking science

Machine Learning of Scientific Events: Classification, Detection, and Verification

Classification and segmentation of objects using machine learning algorithms have been widely used in a large variety of scientific domains in the past few decades. With the exponential growth in the number of ground-based, air-borne, and space-borne observatories, Heliophysics has been taking full advantage of such algorithms in many automated tasks, and obtained valuable knowledge by detecting solar events and analyzing the big-picture patterns. Despite the fact that in many cases, the strengths of the general-purpose algorithms seem to be transferable to problems of scientific domains where scientific events are of interest, in practice there are some critical issues which I address in this dissertation. First, I discuss the four main categories of such issues and then in the proceeding chapters I present real-world examples and the different approaches I take for tackling them. In Chapter II, I take a classical path for classification of three solar events; Active Regions, Coronal Holes, and Quiet Suns. I optimize a set of ten image parameters and improve the classification performance by up to 36%. In Chapter III, in contrast, I utilize an automated feature extraction algorithm, i.e., a deep neural network, for detection and segmentation of another solar event, namely solar Filaments. Using an off-the-shelf algorithm, I overcome several of the issues of the existing detection module, while facing an important challenge; lack of an appropriate evaluation metric for verification of the segmentations. In Chapter IV, I introduce a novel metric to provide a more accurate verification especially for salient objects with fine structures. This metric, called Multi-Scale Intersection over Union (MIoU), is a fusion of two concepts; fractal dimension from Geometry, and Intersection over Union (IoU) which is a popular metric for segmentation verification. Through several experiments I examine the advantages of using MIoU over IoU, and I conclude this chapter by a follow-through on the segmentation results of the previously implemented filament detection module

Science Mission Directorate TechPort Records for 2019 STI-DAA Release

The role of the Science Mission Directorate (SMD) is to enable NASA to achieve its science goals in the context of the Nation's science agenda. SMD's strategic decisions regarding future missions and scientific pursuits are guided by Agency goals, input from the science community including the recommendations set forth in the National Research Council (NRC) decadal surveys and a commitment to preserve a balanced program across the major science disciplines. Toward this end, each of the four SMD science divisions -- Heliophysics, Earth Science, Planetary Science, and Astrophysics -- develops fundamental science questions upon which to base future research and mission programs

AI-ready data in space science and solar physics: problems, mitigation and action plan

In the domain of space science, numerous ground-based and space-borne data of various phenomena have been accumulating rapidly, making analysis and scientific interpretation challenging. However, recent trends in the application of artificial intelligence (AI) have been shown to be promising in the extraction of information or knowledge discovery from these extensive data sets. Coincidentally, preparing these data for use as inputs to the AI algorithms, referred to as AI-readiness, is one of the outstanding challenges in leveraging AI in space science. Preparation of AI-ready data includes, among other aspects: 1) collection (accessing and downloading) of appropriate data representing the various physical parameters associated with the phenomena under study from different repositories; 2) addressing data formats such as conversion from one format to another, data gaps, quality flags and labeling; 3) standardizing metadata and keywords in accordance with NASA archive requirements or other defined standards; 4) processing of raw data such as data normalization, detrending, and data modeling; and 5) documentation of technical aspects such as processing steps, operational assumptions, uncertainties, and instrument profiles. Making all existing data AI-ready within a decade is impractical and data from future missions and investigations exacerbates this. This reveals the urgency to set the standards and start implementing them now. This article presents our perspective on the AI-readiness of space science data and mitigation strategies including definition of AI-readiness for AI applications; prioritization of data sets, storage, and accessibility; and identifying the responsible entity (agencies, private sector, or funded individuals) to undertake the task

Sparse Coding for Event Tracking and Image Retrieval

Comparing regions of images is a fundamental task in both similarity based object tracking as well as retrieval of images from image datasets, where an exemplar image is used as the query. In this thesis, we focus on the task of creating a method of comparison for images produced by NASA’s Solar Dynamic Observatory mission. This mission has been in operation for several years and produces almost 700 Gigabytes of data per day from the Atmospheric Imaging Assembly instrument alone. This has created a massive repository of high-quality solar images to analyze and categorize. To this end, we are concerned with the creation of image region descriptors that are selective enough to differentiate between highly similar images yet compact enough to be compared in an efficient manner, while also being indexable with current indexing technology. We produce such descriptors by pooling sparse coding vectors produced by spanning learned basis dictionaries. Various pooled vectors are used to describe regions of images in event tracking, entire image descriptors for image comparison in content based image retrieval, and as region descriptors to be used in a content based image retrieval system on the SDO AIA image pipeline

Enabling exploration : the lunar outpost and beyond

The purpose of this workshop is to bring together academic, governmental, and private sector interests to discuss progress in lunar exploration, share ideas and information, and form collaborations. It will be an opportunity to integrate diverse interests in lunar exploration to reduce risk and cost of establishing a permanent presence on the Moon through novel and innovative ideas, technologies, and partnerships.Lunar and Planetary Institute, National Aeronautics and Space Administration, Lunar Exploration Analysis Groupconveners, Clive Neal, Stephen MackwellPARTIAL CONTENTS: Reducing the Risk, Requirements, and Cost of the Human Exploration Phase of the Constellation Program with Robotic Landers and Rovers / D.A. Kring -- Hydrogen: A Strategy for Assessing the Key Element for the Lunar Outpost / J. Plescia, P. Spudis, B. Bussey, R. Elphic, S. Nozette, and A. Phipps -- Aristarchus Plateau as an Outpost Location / B.L. Jolliff and J. Zhang -- Commercial Development of the Moon: The Great Lunar Depository / D.S. McKay -- Scientific and Resource Characterization of Lunar Regolith Using Dielectric Spectroscopy / D.E. Stillman and R.E. Grimm

Discovery of Spatiotemporal Event Sequences

Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms