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NASA NTRS2017

Effects of Variable Eccentricity on the Climate of an Earth-Like World

The Kepler era of exoplanetary discovery has presented the Astronomical community with a cornucopia of planetary systems very different from the one which we inhabit. It has long been known that Jupiter plays a major role in the orbital parameters of Mars and its climate, but there is also a long-standing belief that Jupiter would play a similar role for Earth if not for its large moon. Using a three dimensional general circulation model (3-D GCM) with a fully-coupled ocean we simulate what would happen to the climate of an Earth-like world if Mars did not exist, but a Jupiter-like planet was much closer to Earths orbit. We investigate two scenarios that involve evolution of the Earth-like planets orbital eccentricity from 0 to 0.066 on a time scale of 4500 years, and from 0 to 0.283 over 6500 years. We discover that during most of the 6500 year scenario the planet would experience a moist greenhouse effect when near periastron. This could have implications for the ability of such a world to retain an ocean on time scales of 109 years. More Earth-like planets in multi-planet systems will be discovered as we continue to survey the skies and the results herein show that the proximity of large gas giant planets may play an important role in the habitabilty of these worlds. These are the first such 3-D GCM simulations using a fully-coupled ocean with a planetary orbit that evolves over time due to the presence of a giant planet.

Stability↗

NASA NTRS2016

Enceladus Life Finder: the Search for Life in a Habitable Moon

Enceladus is one of the most intriguing bodies in the solar system. In addition to having one of the brightest and youngest surfaces, this small Saturnian moon was recently discovered to have a plume erupting from its south polar terrain and a global subsurface ocean. The Cassini Mission discovered organics and nitrogen-bearing molecules in the plume, as well as salts and silicates that strongly suggest ocean water in contact with a rocky core. However, Cassini's instruments lack sufficient resolution and mass range to determine if these organics are of biotic origin. The Enceladus Life Finder (ELF) is a Discovery-class mission that would use two state-of-the-art mass spectrometers to target the gas and grains of the plume and search for evidence of life in this alien ocean.

Saturn↗

NASA NTRS2017

Interpretable Categorization of Heterogeneous Time Series Data

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Drones↗

NASA NTRS2017

Applications of Anomaly Detection and Precursor Identification in Airspace Operations

As we continue to advance the U.S. National Airspace into the next generation of air traffic, we face challenges in both increase in complexity, as well as, a significant growth in traffic volume. Addressing these challenges, while maintaining the same level of safety is an important application of data mining. Because of these significant shifts in airspace design and usage there is a need to identify current and emergent safety risks along with their potential precursors. In recent years NASA has made advancements in developing scalable methods to address this effort in the Big Data paradigm. Multiple kernel anomaly detection approaches have been employed on both surveillance radar data and flight operational quality assurance data to identify operationally significant safety risks. Additionally, events have been explored with a recently developed precursor identification tool to discover states that reveal an increased probability of a safety event. These tools can be used to discover emerging safety risks that may not be currently monitored, which allows for mitigation tactics to be employed and ultimately make the overall airspace safer. This talk will discuss an overview of these methods and a discussion of the findings.

anomaly detection↗

NASA NTRS2016

Processing and Managing the Kepler Mission's Treasure Trove of Stellar and Exoplanet Data

The Kepler telescope launched into orbit in March 2009, initiating NASAs first mission to discover Earth-size planets orbiting Sun-like stars. Kepler simultaneously collected data for 160,000 target stars at a time over its four-year mission, identifying over 4700 planet candidates, 2300 confirmed or validated planets, and over 2100 eclipsing binaries. While Kepler was designed to discover exoplanets, the long term, ultra- high photometric precision measurements it achieved made it a premier observational facility for stellar astrophysics, especially in the field of asteroseismology, and for variable stars, such as RR Lyraes. The Kepler Science Operations Center (SOC) was developed at NASA Ames Research Center to process the data acquired by Kepler from pixel-level calibrations all the way to identifying transiting planet signatures and subjecting them to a suite of diagnostic tests to establish or break confidence in their planetary nature. Detecting small, rocky planets transiting Sun-like stars presents a variety of daunting challenges, from achieving an unprecedented photometric precision of 20 parts per million (ppm) on 6.5-hour timescales, supporting the science operations, management, processing, and repeated reprocessing of the accumulating data stream. This paper describes how the design of the SOC meets these varied challenges, discusses the architecture of the SOC and how the SOC pipeline is operated and is run on the NAS Pleiades supercomputer, and summarizes the most important pipeline features addressing the multiple computational, image and signal processing challenges posed by Kepler.

high performance computing↗

NASA NTRS2017

Differential Drag Demonstration: A Post-Mission Experiment with the EO-1 Spacecraft

Differential drag is a technique for altering the semi-major axis, velocity, and along-track position of a spacecraft in low Earth orbit. It involves varying the spacecrafts cross-sectional area relative to its velocity direction by temporarily changing attitude and solar array angles, thus varying the amount of atmospheric drag on the spacecraft. The technique has recently been proposed and used by at least three satellite systems for initial separation of constellation spacecraft after launch, stationkeeping during the mission, and potentially for conjunction avoidance. Similarly, differential drag has been proposed as a control strategy for rendezvous, removing the need for active propulsion. In theory, some operational missions that lack propulsion capability could use this approach for conjunction avoidance, though options are typically constrained for spacecraft that are already in orbit. Shortly before the spacecraft was decommissioned, an experiment was performed using NASAs EO-1 spacecraft in order to demonstrate differential drag on an operational spacecraft in orbit, and discover some of the effects differential drag might manifest. EO-1 was not designed to maintain off-nominal orientations for long periods, and as a result the team experienced unanticipated challenges during the experiment. This paper will discuss operations limitations identified before the experiment, as well as those discovered during the experiment. The effective displacement that resulted from increasing the drag area for 39 hours will be compared to predictions as well as the expected position if the spacecraft maintained nominal operations. A hypothetical scenario will also be examined, studying the relative risks of maintaining an operational spacecraft bus in order to maintain the near-maximum drag area orientation and hasten reentry.

differential drag↗

NASA NTRS2017

The Kepler Data Processing Handbook: A Field Guide to Prospecting for Habitable Worlds

The Kepler telescope hurtled into orbit in March 2009, initiating NASA's first mission to discover Earth-size planets orbiting Sun-like stars. Kepler simultaneously collected data for approximately 165,000 target stars at a time over its four-year mission, identifying over 4700 planet candidates, over 2300 confirmed or validated planets, and over 2100 eclipsing binaries. While Kepler was designed to discover exoplanets, the long-term, ultrahigh photometric precision measurements it achieved made it a premier observational facility for stellar astrophysics, especially in the field of asteroseismology, and for variable stars, such as RR Lyrae. The Kepler Science Operations Center (SOC) was developed at NASA Ames Research Center to process the data acquired by Kepler from pixel-level calibrations all the way to identifying transiting planet signatures and subjecting them to a suite of diagnostic tests to establish or break confidence in their planetary nature. Detecting small, rocky planets transiting Sun-like stars presents a variety of daunting challenges, including achieving an unprecedented photometric precision of ~20 ppm on 6.5-hour timescales, and supporting the science operations, management, processing, and repeated reprocessing of the accumulating data stream. A newly revised and expanded version of the Kepler Data Processing Handbook (KDPH) has been released to support the legacy archival products. The KDPH details the theory, design and performance of the algorithms supporting each data processing step. This paper presents an overview of the KDPH and features illustrations of several key algorithms in the Kepler Science Data Processing Pipeline. Kepler was selected as the 10th mission of the Discovery Program. Funding for this mission is provided by NASA, Science Mission Directorate.

high performance computing↗

NASA NTRS2017

Differential Drag Demonstration: A Post-Mission Experiment with the EO-1 Spacecraft

Differential drag is a technique for altering the semimajor axis, velocity, and along-track position of a spacecraft in low Earth orbit. It involves varying the spacecraft's cross-sectional area relative to its velocity direction by temporarily changing attitude and solar array angles, thus varying the amount of atmospheric drag on the spacecraft. The technique has recently been proposed and used by at least three satellite systems for initial separation of constellation spacecraft after launch, stationkeeping during the mission, and potentially for conjunction avoidance. Similarly, differential drag has been proposed as a control strategy for rendezvous, removing the need for active propulsion. In theory, some operational missions that lack propulsion capability could use this approach for conjunction avoidance, though options are typically constrained for spacecraft that are already in orbit. Shortly before the spacecraft was decommissioned, an experiment was performed using NASA's EO-1 spacecraft in order to demonstrate differential drag on an operational spacecraft in orbit, and discover some of the effects differential drag might manifest. EO-1 was not designed to maintain off-nominal orientations for long periods, and as a result the team experienced unanticipated challenges during the experiment. This paper will discuss operations limitations identified before the experiment, as well as those discovered during the experiment. The effective displacement that resulted from increasing the drag area for 39 hours will be compared to predictions as well as the expected position if the spacecraft maintained nominal operations. A hypothetical scenario will also be examined, studying the relative risks of maintaining an operational spacecraft bus in order to maintain the near-maximum drag area orientation and hasten reentry.

orbital lifetim↗

NASA NTRS2014

External Dependencies-Driven Architecture Discovery and Analysis of Implemented Systems

A method for architecture discovery and analysis of implemented systems (AIS) is disclosed. The premise of the method is that architecture decisions are inspired and influenced by the external entities that the software system makes use of. Examples of such external entities are COTS components, frameworks, and ultimately even the programming language itself and its libraries. Traces of these architecture decisions can thus be found in the implemented software and is manifested in the way software systems use such external entities. While this fact is often ignored in contemporary reverse engineering methods, the AIS method actively leverages and makes use of the dependencies to external entities as a starting point for the architecture discovery. The AIS method is demonstrated using the NASA's Space Network Access System (SNAS). The results show that, with abundant evidence, the method offers reusable and repeatable guidelines for discovering the architecture and locating potential risks (e.g. low testability, decreased performance) that are hidden deep in the implementation. The analysis is conducted by using external dependencies to identify, classify and review a minimal set of key source code files. Given the benefits of analyzing external dependencies as a way to discover architectures, it is argued that external dependencies deserve to be treated as first-class citizens during reverse engineering. The current structure of a knowledge base of external entities and analysis questions with strategies for getting answers is also discussed.

Ganesan, Dharmalingam↗

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

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

spaceflight↗

NASA NTRS2016

Retrieval of Daily Maps of PM2.5 Aerosol in the Problematic California Valleys: Bright, Speckled Reflectances, Thin AOT, but High Pollution

The San Joaquin Valley suffers from severe episodes of respirable aerosol (PM2.5) in wintertime.We provide maps of aerosol episodes using daily snapshots of PM2.5 and its changing features despite numerous difficulties inherent to sampling the region. Linear relationships relating aerosol optical thickness (AOT) to PM2.5 give an explained variance of approximately 3.The GEO-CAPE mission has as a goal the provision of relevant measures of respirable aerosol to the community,but has not formulated a science goal beyond the limited goal of retrieval of AOT, bringing the usefulness of GEO-CAPE into doubt.Our special focus was on the DISCOVER-AQ period, Jan-Feb 2013, which had many supporting measurements.Both high pollution and retrieval difficulties tend to occur in many Mediterranean agricultural regions like the San Joauin. One difficulty is the relatively bright surfaces with considerable exposed soil. NASAs MAIAC and MODIS Deep Blue retrieval techniques are shown to have considerable skill even at low aerosol optical thickness (AOT) values, as evaluated by concurrent AERONET sunphotometer measurements.More significantly, these AOT values can correspond to high daytime PM2.5 since aerosol mixed layer depth is thin and variable, 200m 600 m. The thin layers derive from typical subsidence of dry air between more stormy periods. This situation provides an advantage: water vapor column is also almost completely limited to a similar mixed layer depth, and can thus serve as a measure of aerosol dilution.Using the MAIAC Water Vapor Column:In order to make the maps below, we used the MAIAC data but subtracted partial water-vapor columns estimated from MERRA Reanalysis Data availabe from the GSFC GMAO using kriging. We did not use the mixed-layer estimates from MERRA, since such analyses were found problematic during our forecasting exercises for DISCOVER-AQ. Observations from the aircraft soundings suggested that this overlying moisture was mostly due to larger scale flows, not ML venting.However, the specific humidity at the surface and a nearly well-mixed ML was analyzed by kriging from the surface network (MesoWest, University of Utah). These were thought to be truer, uninfluenced by physical process modeling that combines with data observations. (TBD: How different are they?) Procedure: Subtract overlying partial water columns from MAIAC column water and divide this by a surface value of water vapor. (MAIAC column is expressed in cm of water, i.e., water vapor at surface conditions.This method appears to bring out useful details in the distribution of submicron particles in the very problematic Wintertime San Joaquin Valley, and allow analysis of pollution episodes throughout the valley, rather than long-term averages.

san joaquin valley↗

NASA NTRS2019

The Broad Absorption Line Tidal Disruption Event iPTF15af: Optical and Ultraviolet Evolution

We present multiwavelength observations of the tidal disruption event (TDE) iPTF15af, discovered by the intermediate Palomar Transient Factory survey at redshift z = 0.07897. The optical and ultraviolet (UV) light curves of the transient show a slow decay over 5 months, in agreement with previous optically discovered TDEs. It also has a comparable blackbody peak luminosity of L(sub peak) approx. = 1.5 x 10(exp 44) erg s(exp -1). The inferred temperature from the optical and UV data shows a value of (3–5) × 10(exp 4) K. The transient is not detected in X-rays up to L(sub X) < 3 x 10(exp 42) erg s(exp -1) within the first 5 months after discovery. The optical spectra exhibit two distinct broad emission lines in the He ii region, and at later times also Hα emission. Additionally, emission from [N iii] and [O iii] is detected, likely produced by the Bowen fluorescence effect. UV spectra reveal broad emission and absorption lines associated with high-ionization states of N v, C iv, Si iv, and possibly P v. These features, analogous to those of broad absorption line quasars (BAL QSOs), require an absorber with column densities N(sub H) > 10(exp 23) cm(exp -2). This optically thick gas would also explain the nondetection in soft X-rays. The profile of the absorption lines with the highest column density material at the largest velocity is opposite that of BAL QSOs. We suggest that radiation pressure generated by the TDE flare at early times could have provided the initial acceleration mechanism for this gas. Spectral UV line monitoring of future TDEs could test this proposal.

Blagorodnova, N.↗

NASA NTRS2014

Automated Discovery of Flight Track Anomalies

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high- volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

Matthews, Bryan↗

NASA NTRS2014

Saturn's Rings

The rings are changing before our eyes; structure varies on all timescales and unexpected things have been discovered. Many questions have been answered, but some answers remain elusive. Here we highlight the major ring science progress over the mission to date, and describe new observations planned for Cassini’s final three years. Ring Composition and particle sizes: The rings are nearly all water ice with no other ices – so why are they reddish? The C Ring and Cassini Division are “dirtier” than the more massive B and A Rings, as shown by near- IR and, recently, microwave observations. Particle sizes, from stellar and radio occultation's, vary from place to place. Ring structure, micro and macro: numerous spiral density waves and ubiquitous “self-gravity wakes” reveal processes which fostered planet formation in the solar system and elsewhere. However, big puzzles remain regarding the main ring divisions, the C Ring plateau structures, and the B Ring irregular structure. Moonlets, inside and out, seen and unseen: Two gaps contain sizeable moonlets, but more gaps seem to contain none; even smaller embedded “propeller” objects wander, systematically or randomly, through the A ring. Rubble pile ring moons just outside the rings may escaped from the rings, and the recently discovered “Peggy” may be trying this as we watch. Impact bombardment of the rings: Comet fragments set the rings to rippling on century-timescales, and boulders crash through hourly; meanwhile, the constant hail of in falling Kuiper belt material has a lower mass flux than previously thought. Origin and Age of the Rings: The ring mass and bombardment play key roles. The ring mass is well known everywhere but in the B Ring (where most of it is). New models suggest how tidal breakup of evolving moons may have formed massive ancient rings, of which the current ring is just a shadow. During its last three years, the Cassini tour profile will allow entirely new observations: direct measurement of the still-unknown ring mass; direct in-situ sampling of ring particle composition (targeting the iron- or carbon based red non-icy component); and radar backscattering observations.

Cassini↗

NASA NTRS2019

Starshade Rendezvous Probe

The Starshade Rendezvous Probe Mission (https://smd-prod.s3.amazonaws.com/science-red/s3fs-public/atoms/files/Starshade2.pdf ) [1] will be the first space-based, high-contrast imaging mission with the potential to detect and characterize Earth-like planets in the habitable zone (HZ) around sunlike stars while at the same time exploring entire planetary systems about our nearest neighbors. Over the last two decades, astronomers have discovered and cataloged thousands of planets around other stars. Nevertheless, we have yet to find a planetary system like our own or to characterize discovered small planets to determine if they are similar to Earth. The next step in exploration is to image full planetary systems, including their HZs, and to obtain planetary spectra with enough sensitivity to determine if a planet is Earth-like. A space-based direct imaging mission to ultimately find and characterize other Earth-like planets is a long-term priority for space astrophysics [2, 3].

Seager, Sara↗

NASA NTRS2019

Optical Communications Feasibility Study for Science Mission Located at Sun-Earth Lagrange Point L2

NASA's Exploration and Space Communications projects division recently completed an investigative study, researching the feasibility of integrating optical communications capabilities onto a science-based mission orbiting at Lagrange Point 2 (L2). Optical communications has been demonstrated and integrated into many low-Earth-orbiting missions, however, using the technology as far as L2 needed to be thoroughly researched and proven similarly as efficient as the mission's planned radio frequency (RF) system, but preferably more efficient that the equivalent RF systems. The investigation team was charged with assessing the feasibility of including optical communications on the mission without interfering with its primary science objectives. This came to be known as the "Do No Harm" approach to accommodation. As science and detection technologies become more advanced, data rates and communications requirements continue to evolve. Missions with complex science instruments have a need for more optimized communications capabilities. Optical communications technologies provide NASA and industry missions with increased data rates and quicker response times, allowing scientists to access more data than ever before. Missions utilizing optical communications will have a lighter and more efficient method of transmitting data to users on Earth. The team investigating this opportunity discovered that including an optical communications payload on board the mission to provide a bi-directional link between the spacecraft and Earth-based ground stations is feasible and reaches data rates that are comparable to, and even exceed, the mission's RF system. In the course of their investigation, the team also discovered significant navigation and ranging benefits provided by the optical communications payload (OCP).

LCRD↗

NASA NTRS2019

Deep Learning Method for Detecting Precursors to Adverse Events

With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.

Matthews, Bryan L.↗