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NASA NTRS2017

Kepler Planet Detection Metrics: Robovetter Completeness and Effectiveness for Data Release 25

In general, the Kepler pipeline identifies a list of Threshold Crossing Events (TCEs), which are periodic flux decrements meeting certain criteria (Jenkins, 2017). These TCEs are reviewed and those that appear consistent with astrophysically transiting or eclipsing systems are classified as Kepler Objects of Interest (KOIs). Further review is given to KOIs, which are then dispositioned as Planet Candidates (PCs) or False Positive (FPs). FPs are further denoted by four major flags that indicate if the signal is Not Transit-Like (NTL), due to a Stellar Eclipse (SS; previously referred to as Significant Secondary), and/or due to contamination from a source other than the target as evidenced by a Centroid Offset (CO) oran Ephemeris Match (EM) with another object. This entire TCE review process is known as dispositioning or vetting.In the first five Kepler mission planet candidate catalogs (Borucki et al., 2011a,b; Batalha et al., 2013; Burke et al., 2014; Rowe et al., 2015), TCEs were manually examined on an individual basis and dispositioned using various plots and quantitative diagnostic tests (see e.g., Coughlin, 2017). In the sixth catalog, Mullally et al. (2015a) employed partial automation via simple parameter cuts to automatically disposition a large fraction of TCEs as not transit-like. Mullally et al. (2015a) also used an automated technique known as the centroid Robovetter (Mullally, 2017) to automatically identify some FP KOIs due to centroid offsets - a telltale signature of light contamination from another target. The remaining targets were manually dispositioned. In the seventh catalog, Coughlin et al. (2016) automated theentire dispositioning process using what is collectively known simply as the Robovetter.In the eighth and final mission catalog, Thompson et al. (2017) use a revised Robovetter to automate the dispositioning of all TCEs with an emphasis on creating a catalog suitable for accurately determining planet occurrence rates. In order to calculate accurate occurrence rates, the completeness and effectiveness of the Robovetter must be characterized. We define these terms as applied to the Robovetter, following Thompson et al. (2017), as:1. Completeness: The fraction of transiting planets detected by the pipeline that are classified as planet candidates by the Robovetter.2. Effectiveness: The fraction of false positives detected by the pipeline that are classified as false positives by the Robovetter.The remainder of this document describes products that can be used to quantitatively assess Robovetter completeness and effectiveness for an arbitrary set of Kepler stars.

Robovetter↗

NASA NTRS2018

Geonex: A NASA-NOAA Collaboration for Producing Land Surface Products from Geostationary Sensors Using Cloud Computing

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement.Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. We estimate the cost of deploying GEONEX to be $400 - 750 a month for processing data (every 30 minutes) and producing products over the conterminous US. For products such as Fire, latency can be as little as 10 minutes from the time of data acquisition.

Geostationary↗

NASA NTRS2010

Kepler Mission's Focal Plane Characterization Models Implementation

The Kepler Mission photometer is an unusually complex array of CCDs. A large number of time-varying instrumental and systemic effects must be modeled and removed from the Kepler pixel data to produce light curves of sufficiently high quality for the mission to be successful in its planet-finding objective. After the launch of the spacecraft, many of these effects are difficult to remeasure frequently, and various interpolations over a small number of sample measurements must be used to determine the correct value of a given effect at different points in time. A library of software modules, called Focal Plane Characterization (FC) Models, is the element of the Kepler Science Data Pipeline (hereafter "pipeline") that handles this. FC, or products generated by FC, are used by nearly every element of the SOC processing chain. FC includes Java components: database persistence classes, operations classes, model classes, and data importers; and MATLAB code: model classes, interpolation methods, and wrapper functions. These classes, their interactions, and the database tables they represent, are discussed. This paper describes how these data and the FC software work together to provide the pipeline with the correct values to remove non-photometric effects caused by the photometer and its electronics from the Kepler light curves. The interpolation mathematics is reviewed, as well as the special case of the sky-to-pixel,pixel-to-sky coordinate transformation code, which incorporates a compound model that is unique in the SOC software.

mission↗

NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗

NASA NTRS2019

Kepler Data Validation II–Transit Model Fitting and Multiple-Planet Search

This paper discusses the transit model-fitting and multiple-planet search algorithms and performance of the Kepler Science Data Processing Pipeline, developed by the Kepler Science Operations Center (SOC). Threshold crossing events (TCEs), which are transit candidate events, are generated by the Transiting Planet Search (TPS) component of the pipeline and subsequently processed in the data validation (DV) component. The transit model is used in DV to fit TCEs to characterize planetary candidates and to derive parameters that are used in various diagnostic tests to classify them. After the signature associated with the TCE is removed from the light curve of the target star, the residual light curve goes through TPS again to search for additional TCEs. The iterative process of transit model fitting and multiple-planet search continues until no TCE is generated from the residual light curve or an upper limit is reached. The transit model-fitting and multiple-planet search performance of the final release (9.3, 2016January) of the pipeline is demonstrated with the results of the processing of four years (17 quarters) of flight data from the primary Kepler Mission. The transit model-fitting results are accessible from the NASA Exoplanet Archive. The final version of the SOC codebase is available through GitHub.

Threshold crossing events (TCEs↗

NASA NTRS2018

Transcriptomics and Proteomics Discussion

This presentation will cover the the basic pipelines for transcriptomics and proteomics that the GeneLab Analysis Working Groups (AWGs) have so far determined to be optimal. Basic transcriptomic pipelines will first be presented from primary analysis to higher-order systems analysis. Examples of how the data has been analyzed will be presented. Proteomics pipelines will also be presented compiled from various AWG members. Discussion will be generated from the AWG members to reach a consensus for each omic type.

Transcriptomics↗

NASA NTRS2019

An Improved Far-Field Small Unmanned Aerial System Optical Detection Algorithm

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation for autonomous small Unmanned Aerial System (sUAS) operations. This work presents an aircraft detect and track pipeline that fuses image differencing and morphological filtering detections for inputs to a Kalman-based object tracking pipeline. The pipeline is evaluated using two types of flight encounters: 1) motorcopter sUAS vs. fixed-wing sUAS 2) motorcopter sUAS vs general aviation plane.

Chester Dolph↗

NASA NTRS2020

Assessment of holographic microscopy for quantifying marine particle size and concentration

Holographic microscopy has emerged as a tool for in situ imaging of microscopic organisms and other particles in the marine environment: appealing because of the relatively larger sampling volume and simpler optical configuration compared to other imaging systems. However, its quantitative capabilities have so far remained uncertain, in part because hologram reconstruction and image recognition have required manual operation. Here, we assess the quantitative skill of our automated hologram processing pipeline (CCV Pipeline), to evaluate the size and concentration measurements of environmental and cultured assemblages of marine plankton particles, and microspheres. Over 1 million particles, ranging from 10 to 200 μm in equivalent spherical diameter, imaged by the 4‐Deep HoloSea digital inline holographic microscope (DIHM) are analyzed. These measurements were collected in parallel with a FlowCam (FC), Imaging FlowCytobot (IFCB), and manual microscope identification. Once corrections for particle location and nonuniform illumination were developed and applied, the DIHM showed an underestimate in ESD of about 3% to 10%, but successfully reproduced the size spectral slope from environmental samples, and the size distribution of cultures (Dunaliella tertiolecta, Heterosigma akashiwo, and Prorocentrum micans) and microspheres. DIHM concentrations (order 1 to 1000 particles ml(exp −1)) showed a linear agreement (r(exp 2) = 0.73) with the other instruments, but individual comparisons at times had large uncertainty. Overall, we found the DIHM and the CCV Pipeline required extensive manual correction, but once corrected, provided concentration and size estimates comparable to the other imaging systems assessed in this study. Holographic cameras are mechanically simple, autonomous, can operate at very high pressures, and provide a larger sampling volume than comparable lens‐based tools. Thus, we anticipate that these characterization efforts will be rewarded with novel discovery in new oceanic environments.

Noah L. Walcutt↗

NASA NTRSDate not supplied

Primary Mission Threshold Crossing Events in the TESS SPOC Transit Search

We present an overview of the single- and multiple-sector results of the Science Processing Operations Center (SPOC) transit search in the primary Transiting Exoplanet Survey Satellite (TESS) mission. TESS was designed to survey bright stars in the greater Solar neighborhood in search of transiting exoplanets. Data were acquired at a 2-minute cadence for 16,000-20,000 pre-selected target stars in each 28-day observation sector and processed in the SPOC pipeline at NASA Ames Research Center. The photometry pipeline produced a systematic error corrected light curve for each target star. Light curves were searched for transiting planet signatures by sector for all target stars, and separately for target stars observed in multiple sectors. Potential transit signals for which the transiting planet detection threshold was exceeded and a series of transit consistency tests were passed are referred to as Threshold Crossing Events (TCEs). We highlight the full TCE population and the population of SPOC TCEs that were later identified as TESS Objects of Interest (TOIs). Characteristics of the TCE populations implied by limb-darkened transiting planet model fits are also presented. SPOC pipeline data products are delivered to the Mikulski Archive for Space Telescopes (MAST)(http://archive.stsci.edu/missions-and-data/tess) for access by the community. Funding for the TESS Mission has been provided by the NASA Science Mission Directorate.

TESS↗

NASA NTRS2018

The Zwicky Transient Facility: Data Processing, Products, and Archive

The Zwicky Transient Facility (ZTF) is a new robotic time-domain survey currently in progress using the Palomar 48-inch Schmidt Telescope. ZTF uses a 47 square degree field with a 600 megapixel camera to scan the entire northern visible sky at rates of ∼3760 square degrees/hour to median depths of g ~ 20.8 and r ~ 20.6 mag (AB, 5σ in 30 sec). We describe the Science Data System that is housed at IPAC, Caltech. This comprises the data-processing pipelines, alert production system, data archive, and user interfaces for accessing and analyzing the products. The real-time pipeline employs a novel image-differencing algorithm, optimized for the detection of point-source transient events. These events are vetted for reliability using a machine-learned classifier and combined with contextual information to generate data-rich alert packets. The packets become available for distribution typically within 13 minutes (95th percentile) of observation. Detected events are also linked to generate candidate moving-object tracks using a novel algorithm. Objects that move fast enough to streak in the individual exposures are also extracted and vetted. We present some preliminary results of the calibration performance delivered by the real-time pipeline. The reconstructed astrometric accuracy per science image with respect to Gaia DR1 is typically 45 to 85 milliarcsec. This is the RMS per-axis on the sky for sources extracted with photometric S/N ≥10 and hence corresponds to the typical astrometric uncertainty down to this limit. The derived photometric precision (repeatability) at bright unsaturated fluxes varies between 8 and 25 millimag. The high end of these ranges corresponds to an airmass approaching ∼2—the limit of the public survey. Photometric calibration accuracy with respect to Pan-STARRS1 is generally better than 2%. The products support a broad range of scientific applications: fast and young supernovae; rare flux transients; variable stars; eclipsing binaries; variability from active galactic nuclei; counterparts to gravitational wave sources; a more complete census of Type Ia supernovae; and solar-system objects.

Frank J. Masci↗

NASA NTRS2024

Automatic Detection of Large-scale Flux Ropes and Their Geoeffectiveness with a Machine-learning Approach

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential, since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary classification machine-learning models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of autodetected FRs. Using the properties of the southward magnetic field, the second model determines the geoeffectiveness of FRs. Our method identifies 88.6% and 80% of large-scale ICMEs (duration 1day) observed at 1au by the Wind and the Solar TErrestrial RElations Observatory missions, respectively. While testing with continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during the 2008–2014 period (recall = 0.875), but also many false positives (precision = 0.56), as we do not take into account any additional solar wind properties other than the magnetic properties. We find an accuracy of 0.88 when estimating the geoeffectiveness of the autodetected FRs using our method. Thus, in space-weather nowcasting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and their geoeffectiveness.

Sanchita Pal↗

NASA NTRSDate not supplied

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗

NASA NTRS1973

Air-cushion tankers for Alaskan North Slope oil

A concept is described for transporting oil from the Arctic to southern markets in 10,000-ton, chemically fueled air-cushion vehicles (ACV's) configured as tankers. Based on preliminary cost estimates the conceptual ACV tanker system as tailored to the transportation of Alaskan North Slope oil could deliver the oil for about the same price per barrel as the proposed trans-Alaska pipeline with only one-third of the capital investment. The report includes the description of the conceptual system and its operation; preliminary cost estimates; an appraisal of ACV tanker development; and a comparison of system costs, versatility, vulnerability, and ecological effect with those of the trans-Alaska pipeline.

Anderson, J. L.↗

NASA NTRS1973

A parallel variable metric optimization algorithm

An algorithm, designed to exploit the parallel computing or vector streaming (pipeline) capabilities of computers is presented. When p is the degree of parallelism, then one cycle of the parallel variable metric algorithm is defined as follows: first, the function and its gradient are computed in parallel at p different values of the independent variable; then the metric is modified by p rank-one corrections; and finally, a single univariant minimization is carried out in the Newton-like direction. Several properties of this algorithm are established. The convergence of the iterates to the solution is proved for a quadratic functional on a real separable Hilbert space. For a finite-dimensional space the convergence is in one cycle when p equals the dimension of the space. Results of numerical experiments indicate that the new algorithm will exploit parallel or pipeline computing capabilities to effect faster convergence than serial techniques.

Straeter, T. A.↗

NASA NTRS1974

Digital registration of ERTS-1 imagery

Two requirements for the registration of Earth Resources Technology Satellite (ERTS) data are discussed. These requirements are registration of ERTS data acquired on separate passes and registration of ERTS data to a ground reference. Performances of a semi-automatic warp algorithm and an automatic pipeline processing algorithm demonstrate that either procedure is useful, depending upon the user's requirements. In two cases where the time lapse between passes of the satellite were 90 days and 18 days the automatic pipeline processor reduced the mean radial registration error to 0.28 and 0.58 pixel, respectively. It is concluded that this technique is promissing for high-volume production processing.

Bonrud, L. O.↗

NASA NTRS1975

A preliminary systems-engineering study of an advanced nuclear-electrolytic hydrogen-production facility

An advanced nuclear-electrolytic hydrogen-production facility concept was synthesized at a conceptual level with the objective of minimizing estimated hydrogen-production costs. The concept is a closely-integrated, fully-dedicated (only hydrogen energy is produced) system whose components and subsystems are predicted on ''1985 technology.'' The principal components are: (1) a high-temperature gas-cooled reactor (HTGR) operating a helium-Brayton/ammonia-Rankine binary cycle with a helium reactor-core exit temperature of 980 C, (2) acyclic d-c generators, (3) high-pressure, high-current-density electrolyzers based on solid-polymer electrolyte technology. Based on an assumed 3,000 MWt HTGR the facility is capable of producing 8.7 million std cu m/day of hydrogen at pipeline conditions, 6,900 kPa. Coproduct oxygen is also available at pipeline conditions at one-half this volume. It has further been shown that the incorporation of advanced technology provides an overall efficiency of about 43 percent, as compared with 25 percent for a contemporary nuclear-electric plant powering close-coupled contemporary industrial electrolyzers.

Escher, W. J. D.↗

NASA NTRS1978

Adaptation of a program for nonlinear finite element analysis to the CDC STAR 100 computer

The conversion of a nonlinear finite element program to the CDC STAR 100 pipeline computer is discussed. The program called DYCAST was developed for the crash simulation of structures. Initial results with the STAR 100 computer indicated that significant gains in computation time are possible for operations on gloval arrays. However, for element level computations that do not lend themselves easily to long vector processing, the STAR 100 was slower than comparable scalar computers. On this basis it is concluded that in order for pipeline computers to impact the economic feasibility of large nonlinear analyses it is absolutely essential that algorithms be devised to improve the efficiency of element level computations.

Pifko, A. B.↗

NASA NTRS1978

Synthetic natural gas in California: When and why

A coal gasification plant planned for northwestern New Mexico to produce 250 MMCFD of pipeline quality gas (SNG) using the German Lurgi process is discussed. The SNG will be commingled with natural gas in existing pipelines for delivery to southern California and the Midwest. Cost of the plant is figured at more than $1.4 billion in January 1978 dollars with a current inflation rate of $255,000 for each day of delay. Plant start-up is now scheduled for 1984.

Wood, W. B.↗