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NASA NTRS2018

An Overview of Science Challenges Pertaining to our Understanding of Extreme Geomagnetically Induced Currents

Vulnerability of man-made infrastructure to Earth-directed space weather events is a serious concern for today's technology-dependent society. Space weather-driven geomagnetically induced currents (GICs) can disrupt operation of extended electrically conducting technological systems. The threat of adverse impacts on critical technological infrastructure, like power grids, oil and gas pipelines, and communication networks, has sparked renewed interest in extreme space weather. Because extreme space weather events have low occurrence rate but potentially high impact, this presents a major challenge for our understanding of extreme GIC activity. In this chapter, we discuss some of the key science challenges pertaining to our understanding of extreme events. In addition, we present an overview of GICs including highlights of severe impacts over the last 80 years and recent U.S. Federal actions relevant to this community.

Ngwira, Chigomezyo M.↗

NASA NTRS2018

The Gaia Catalogue Second Data Release and Its Implications to Optical Observations of Man-Made Earth Orbiting Objects

The Gaia catalogue second data release and its implications to optical observations of man-made Earth orbiting objects. Abstract and not the Final Paper is attached. The Gaia spacecraft was launched in December 2013 by the European Space Agency to produce a three-dimensional, dynamic map of objects within the Milky Way. Gaia's first year of data was released in September 2016. Common sources from the first data release have been combined with the Tycho-2 catalogue to provide a 5 parameter astrometric solution for approximately 2 million stars. The second Gaia data release is scheduled to come out in April 2018 and is expected to provide astrometry and photometry for more than 1 billion stars, a subset of which with a the full 6 parameter astrometric solution (adding radial velocity) and positional accuracy better than 0.002 arcsec (2 mas). In addition to precise astrometry, a unique opportunity exists with the Gaia catalogue in its production of accurate, broadband photometry using the Gaia G filter. In the past, clear filters have been used by various groups to maximize likelihood of detection of dim man-made objects but these data were very difficult to calibrate. With the second release of the Gaia catalogue, a ground based system utilizing the G band filter will have access to 1.5 billion all-sky calibration sources down to an accuracy of 0.02 magnitudes or better. In this talk, we will discuss the advantages and practicalities of implementing the Gaia filters and catalogue into data pipelines designed for optical observations of man-made objects.

Frith, James M.↗

NASA NTRS2018

Astro-H/Hitomi Data Analysis, Processing, and Archive

Astro-H is the x-ray/gamma-ray mission led by Japan with international participation, launched on February 17, 2016. Soon after launch, Astro-H was renamed Hitomi. The payload consists of four different instruments (SXS, SXI, HXI, and SGD) that operate simultaneously to cover the energy range from 0.3 keV up to 600 keV. On March 27, 2016, JAXA lost contact with the satellite and, on April 28, they announced the cessation of the efforts to restore mission operations. Hitomi collected about one months worth of data with its instruments. This paper presents the analysis software and the data processing pipeline created to calibrate and analyze the Hitomi science data, along with the plan for the archive. These activities have been a collaborative effort shared between scientists and software engineers working in several institutes in Japan and United States.

Angelini, Lorella↗

NASA NTRS2018

The Kepler Light Curves of AGN: A Detailed Analysis

We present a comprehensive analysis of 21 light curves of Type 1 active galactic nuclei (AGN) from the Kepler spacecraft. First, we describe the necessity and development of a customized pipeline for treating Kepler data of stochastically variable sources like AGN. We then present the light curves, power spectral density functions (PSDs), and flux histograms. The light curves display an astonishing variety of behaviors, many of which would not be detected in ground-based studies, including switching between distinct flux levels. Six objects exhibit PSD flattening at characteristic timescales that roughly correlate with black hole mass. These timescales are consistent with orbital timescales or free-fall accretion timescales. We check for correlations of variability and high-frequency PSD slope with accretion rate, black hole mass, redshift, and luminosity. We find that bolometric luminosity is anticorrelated with both variability and steepness of the PSD slope. We do not find evidence of the linear rms-flux relationships or lognormal flux distributions found in X-ray AGN light curves, indicating that reprocessing is not a significant contributor to optical variability at the 0.1%-10% level.

power spectral density functions(PSDs)↗

NASA NTRS2014

First All-Sky Search for Continuous Gravitational Waves from Unknown Sources in Binary Systems

We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect algorithm, the search was carried out on data from the sixth LIGO science run and the second and third Virgo science runs. The search covers a range of frequencies from 20 Hz to 520 Hz, a range of orbital periods from 2 to ∼2,254 h and a frequency- and period-dependent range of frequency modulation depths from 0.277 to 100 mHz. This corresponds to a range of projected semimajor axes of the orbit from ∼0.6 × 10(exp −3) ls to ∼6,500 ls assuming the orbit of the binary is circular. While no plausible candidate gravitational wave events survive the pipeline, upper limits are set on the analyzed data. The most sensitive 95% confidence upper limit obtained on gravitational wave strain is 2.3 × 10(exp −24) at 217 Hz, assuming the source waves are circularly polarized. Although this search has been optimized for circular binary orbits, the upper limits obtained remain valid for orbital eccentricities as large as 0.9. In addition, upper limits are placed on continuous gravitational wave emission from the low-mass x-ray binary Scorpius X-1 between 20 Hz and 57.25 Hz.

systems↗

NASA NTRS2018

GeneLab: "Omics" Data System for Space Biology Research

Determining the biological impact of spaceflight through novel approaches is essential to reduce the health risks to astronauts for long-term space missions. The current established health risks due to spaceflight are only reflecting known symptomatic and physiologic responses and do not reflect early onset of other potential diseases. There are many unknown variables which still need to be identified to fully understand the health impacts due to the environmental factors in space. One method to uncover potential novel biological mechanisms responsible for health risks in astronauts is by utilizing NASA's GeneLab Data Systems (genelab.nasa.gov). GeneLab is public repository that hosts multiple omics datasets generated from space biology experiments that include experiments flown in space, simulated cosmic radiation experiments, and simulated microgravity experiments. This presentation will provide an overview of GeneLab and examples of analysis that are being done with GeneLab datasets. These example will include novel data and work being generated with various scientists around the world involved with GeneLab's Analysis Working Groups (AWG) that are assisting with the development of pipelines and advancing GeneLab to the next phase, a publication from GeneLab discovering novel Carbon Dioxide impact due to rodent habitats, another publication from GeneLab discovering a potential master regulator responsible for health risk associated due to spaceflight, and potential cardiovascular risk from space radiation.

microgravity↗

NASA NTRS2018

Evidence for an Optical Low-Frequency Quasi-Periodic Oscillation in the Kepler Light Curve of an Active Galaxy

We report evidence for a quasi-periodic oscillation (QPO) in the optical light curve of KIC 9650712, a narrow-line Seyfert 1 galaxy in the original Kepler field. After the development and application of a pipeline for Kepler data specific to active galactic nuclei (AGNs), one of our sample of 21 AGNs selected by infrared photometry and X-ray flux demonstrates a peak in the power spectrum at log ν = −6.58 Hz, corresponding to a temporal period of t = 44 days. We note that although the power spectrum is well fit by a model consisting of a Lorentzian and a single power law, alternative continuum models cannot be ruled out. From optical spectroscopy, we measure the black hole mass of this AGN as log (M(sub BH)/solar mass) = 8.17. We find that this frequency lies along a correlation between low-frequency QPOs and black hole mass from stellar and intermediate mass black holes to AGNs, similar to the known correlation in high-frequency QPOs.

quasi-periodic oscillation (QPO)↗

NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗

NASA NTRS2018

GeneLab: "Omics" Data Systems for Spaceflight and Simulated Spaceflight Environment

spaceflight↗

NASA NTRS2018

Land Surface Reflectances from Geostationary Sensors

GEONEX is a processing pipeline that produces a suite of satellite land surface products using data streams from the latest geostationary (GEO) sensors including the GOES016/ABI and the Himawari-8/AHI. The suite, created collaboratively by scientists from NASA and NOAA, includes top-of-atmosphere (TOA) reflectances, land surface reflectances (LSRs), vegetation indices, LAI/fPAR, and other downstream products. As a key component of the GEONEX product processing, we have adapted the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce LSRs from the TOA data. Because the algorithm depends on building "stacks" of images, we first run internal geo-registration checks to ensure geo-spatial accuracy and consistency of the input (L1B) data before transferring them from the geostationary projection into a tile system in geographic grids. Scan-time is inferred from metadata and applied to calculate the sun-sensor angles for each grid cell. The MAIAC algorithm is run to detect clouds/shadows, estimate aerosol optical thickness (AOT), perform atmospheric corrections, and generate LSRs. We have processed 18-months (from 2016/04 onward) of AHI data over East Asia and Oceania at a 10-minute time step and 10-months (from 2018/01 onward) of ABI data over North and South Americas at a 15-minute time step. As a verification measure, we compare the GEONEX (AHI/ABI) surface reflectances with the standard MODIS products (MOD09GA) and the MODIS MAIAC products over pixels that have similar sun-view geometries. The results indicate general linear relationships between GEONEX and corresponding MODIS LSRs. In particular, the RMSEs between GEONEX and MOD09 data are comparable to those between MOD09 and MODIS MAIAC products, suggesting that the uncertainties of GEONEX LSRs fall into an acceptable range. However, direct comparisons of LSRs over pixels with different sun-view angles are not as straightforward and require more modeling efforts to correct the directional effects. Evaluation of such angular influences on the downstream products (e.g., vegetation indices) is also under investigation.

Geostationary satellite; Remote Sensing; Atmospher↗

NASA NTRS2019

Time Series Analysis in the Search for Other Worlds Through Transit Photometry

The Kepler Mission launched in June 2009 to commence NASA's first mission to search for potentially habitable, Earth-size planets orbiting Sun-like stars. Kepler discovered explanets via the transit method: searching for minute (100 ppm) drops in brightness lasting 1 - 13 hours corresponding to occasions where the planet crosses the face of its host star from Kepler's point of view. The exquisite precision required to carry out the Kepler mission (20 ppm in 6.5 hours) pushed astronomical time series analysis to the limits, and motivated the development of novel algorithmic approaches. Transit signatures of rocky planets are often dwarfed by the intrinsic stellar variability, which is not white noise, and often is non-stationary, and by instrumental systematic effects, which can include transients and electronic artifacts. Surmounting this challenging regime of weak, temporally compact, periodic signals in observation noise with strong systematics and other sources of variability motivated the development of 1) an overcomplete, non-decimated, wavelet-based matched filter to jointly estimate the properties of the non-stationary, non-white observation noise process, and 2) a multi-scale, maximum a posteriori (msMAP) approach to identifying and removing instrumental systematic effects. After over nine years of observations, the Kepler spacecraft finally ran out of fuel in November 2018, ending its data collection activities. Over 2300 planets were discovered by Kepler in its primary mission, and over 355 have been discovered by K2, the repurposed mission that followed Kepler's primary mission after the loss of a second reaction wheel in May 2013. We have ported the Kepler science pipeline for the Transiting Exoplanet Survey Satellite (TESS) Mission, which began science observations in July 2019, and report initial results and performance of the modified science pipeline.The Kepler and TESS Missions are supported by NASA's Science Mission Directorate.

transit surveys↗

NASA NTRS2016

Time Series Explorer: Bayesian Blocks with Generalized Profiles and in Higher Dimensions

The Time Series Explorer (TSE) is a project aimed at provided new and advanced time series analysis algorithms in two forms: a tool kit and an automated pipeline applying selected tools in machine learning settings. I will present a sketch of TSE with emphasis on time-domain modeling in general and recent improvements of the Bayesian Block (BB) algorithm in particular. This includes generalizing the shape of the elementary blocks from the current constant-rate model to general shapes, such as two-sided exponentials. Related topics will include extension of BB to higher dimensions and a novel way to detect and characterize short time-scale bursts in time-tagged event data. The Fermi Gamma Ray Space Telescope light curve for the Crab Nebula will be used as an example for all of the algorithms discussed. This work is in collaboration with Tom Loredo.

Bayesian Block (BB) algorithm↗

NASA NTRS2016

Unmanned Aircraft System Traffic Management (UTM) Concept of Operations

Many applications of small Unmanned Aircraft System (sUAS) have been envisioned. These include surveillance of key assets such as pipelines, rail, or electric wires, deliveries, search and rescue, traffic monitoring, videography, and precision agriculture. These operations are likely to occur in the same airspace in presence of many static and dynamic constraints such as airports, and high wind areas. Therefore, small UAS, typically 55 pounds and below, operations need to be managed to ensure safety and efficiency of operations is maintained. This paper will describe the Concept of Operations (ConOps) for NASA's UAS Traffic Management (UTM) research initiative. The UTM ConOps is focused on safely enabling large-scale small UAS (sUAS) operations in low altitude airspace. The UTM construct supports large-scale visual line of sight and beyond visual line of sight operations. It is based on two primary mantras: (1) flexibility where possible and structure where necessary (2) a risk-based approach where geographical needs and use case indicate the airspace performance requirements. Preliminary stakeholder feedback and initial UTM tests conducted by NASA show promise of UTM to enable large-scale low altitude UAS operations safely.

autonomy↗

NASA NTRS2017

Improved Reference Sampling and Subtraction: A Technique for Reducing the Read Noise of Near-Infrared Detector Systems

Near-infrared array detectors, like the James Webb Space Telescope (JWST) NIRSpec's Teledyne's H2RGs, often provide reference pixels and a reference output. These are used to remove correlated noise. Improved reference sampling and subtraction (IRS(exp 2)) is a statistical technique for using this reference information optimally in a leastsquares sense. Compared with the traditional H2RG readout, IRS(exp 2) uses a different clocking pattern to interleave many more reference pixels into the data than is otherwise possible. Compared with standard reference correction techniques, IRS(exp 2) subtracts the reference pixels and reference output using a statistically optimized set of frequency dependent weights. The benefits include somewhat lower noise variance and much less obvious correlated noise. NIRSpec's IRS(exp 2) images are cosmetically clean, with less 1/f banding than in traditional data from the same system. This article describes the IRS(exp 2) clocking pattern and presents the equations needed to use IRS(exp 2) in systems other than NIRSpec. For NIRSpec, applying these equations is already an option in the calibration pipeline. As an aid to instrument builders, we provide our prototype IRS(exp 2) calibration software and sample JWST NIRSpec data. The same techniques are applicable to other detector systems, including those based on Teledyne's H4RG arrays. The H4RG's interleaved reference pixel readout mode is effectively one IRS(exp 2) pattern.

Methods-statistical↗

NASA NTRS2014

Are We Alone? Kepler on a Quest

Two presentations, a technical and a non-technical with information about the Kepler Mission. Includes graphics on the telescope, how it works, the planets it has found. The technical includes couple of slides on the software pipeline architecture that helps detect the planets using Kepler Data. None of the Kepler graphics are newly created. Except the About Me slide everything else has been borrowed from not one but several different presentations, ones that I deemed helpful to me while doing my talks.

Kepler↗

NASA NTRS2018

Biocybernetic Adaptation Strategies: Machine Awareness of Human Engagement for Improved Operational Performance

Human operators interacting with machines or computers continually adapt to the needs of the system ideally resulting in optimal performance. In some cases, however, deteriorated performance is an outcome. Adaptation to the situation is a strength expected of the human operator which is often accomplished by the human through self-regulation of mental state. Adaptation is at the core of the human operator's activity, and research has demonstrated that the implementation of a feedback loop can enhance this natural skill to improve training and human/machine interaction. Biocybernetic adaptation involves a “loop upon a loop,” which may be visualized as a superimposed loop which senses a physiological signal and influences the operator’s task at some point. Biocybernetic adaptation in, for example, physiologically adaptive automation employs the “steering” sense of “cybernetic,” and serves a transitory adaptive purpose – to better serve the human operator by more fully representing their responses to the sys- tem. The adaptation process usually makes use of an assessment of transient cog- nitive state to steer a functional aspect of a system that is external to the operator’s physiology from which the state assessment is derived. Therefore, the objective of this paper is to detail the structure of biocybernetic systems regarding the level of engagement of interest for adaptive systems, their processing pipeline, and the adaptation strategies employed for training purposes, in an effort to pave the way towards machine awareness of human state for self-regulation and improved operational performance.

Stephens, Chad↗

NASA NTRS2019

Quantitative Comparison of Proprietary and Open-Source Georeferencing Tools for Use with Astronaut Photography

The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. Astronauts on the International Space Station (ISS) respond to requests from CEO to acquire imagery of scientific and education targets, to include high profile targets in response to activations from the International Charter for Space & Major Disasters (also known as the International Disaster Charter, or IDC) and NASA’s Disasters Program. CEO facilitates the acquisition of astronaut photography in response to IDC events and delivers georeferenced data products to the United States Geological Survey (USGS) for distribution to the disaster community. Using GeoRef, an internal web-based tool developed in collaboration with NASA’s Ames Research Center, CEO generates data packages of georeferenced imagery, uncertainty images for assessing control and tie point accuracy, and metadata documenting raw and processed data. Operational experience with the Georef software identified vulnerabilities to internal code and server errors that can significantly increase time of data production. As such, CEO developed a backup procedure in case the GeoRef software experiences front-end or back-end errors. A system using OSGEO’s open-source QGIS software combined with a semi-automated pipeline using the object-oriented Python language and the Geospatial Abstract Library for generating metadata is quantitatively compared to GeoRef’s data package for quality and productivity. Root Mean Square Error (RMSE) provides a standard measurement of data quality as it relates to ground error. Assessing RMSE measurements generated from georeferenced astronaut photographs acquired with different obliquity and focal length offers a comprehensive accuracy assessment of the software’s transformation algorithms. This assessment will indicate the software's ability to produce data products with the least ground-error or highest data quality regarding ground accuracy. In addition, a comparison of the software’s efficiency in generating a data package that includes georeferenced images, metadata, and uncertainty images for measuring tie/ground point error was performed. Initial results, based on the comparison of three nadir-facing astronaut photographs acquired with a 95mm focal length, reveal the QGIS-based system's average RMSE is 2.36 (pixels) suggesting its georectification system produces data products that meet and perhaps improve upon Georef solution's average RMSE of 32.99 (pixels). However, the QGIS system was unable to reproduce two unique Georef data products, uncertainty images for measuring tie and control point errors and a translated unwrapped image. In addition, the Georef software is designed to accept handheld camera pose information from a hardware component (Geosens) scheduled for deployment on the ISS in late 2018; this information is intended to provide increased accuracy and auto-registration capability for astronaut photographs. Future work is expected to determine the QGIS-based georectification system’s potential as an open-source alternative (and operational backup) to Georef for georeferencing the full range of resolutions and viewing angles unique to handheld digital camera imagery in support of ISS disaster response activities.

Jagge, Amy M.↗

NASA NTRS2019

Exploring Beyond Earth's Atmosphere with Human-Machine Teams

NASA's highly successful Kepler Mission has revolutionized our understanding of the Galaxy. We now know that planets, even Earth-size planets in the habitable zone, are common. With the end of the Kepler Mission we now look to the future with the Transiting Exoplanet Survey Satellite (TESS) which will discover thousands of exoplanets in orbit around the brightest stars in the sky. In a two-year survey, TESS will perform an all-sky search of more than 200,000 stars for temporary drops in brightness caused by planetary transits. With Kepler and TESS, humanity is finally at the verge of studying the masses, sizes, densities, orbits, and atmospheres of a large cohort of small planets, including a sample of rocky worlds in the habitable zones of their host stars which may prove to host life. The massive data sets generated by Kepler and TESS must be meticulously combed for the weakest planetary signals every month. While a daunting and error-prone task for humans, this is an exciting opportunity for the breakthroughs recently seen in machine learning. Specifically, traditional methods for identifying planet transits require extensive data processing pipelines followed by extensive human vetting. This manual process risks loss of information due to the data processing and to inconsistency and biases due to individual human vetters. The latest advancements in machine learning will allow an objective classifier to minimize the losses of information and greatly lessen the burden on the human vetters, in addition to providing assessment of quality and score to each planet candidate, freeing the humans to concentrate on border cases and other more interesting investigations.

Smith, Jeffrey C.↗