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NASA NTRSDate not supplied

HydroSAR: A Cloud-based SAR Data Analysis Service to Monitor Hydrological Disasters and their Impact on Population and Agriculture

Weather-related hazards are ubiquitous around the world including: 1) hurricane storm surges, 2) rapid snowmelt and heavy rainfall, 3) severe weather leading to flash floods, and 4) seasonal freeze and thaw of rivers that may lead to ice jams. Each of these hazards affects human settlements and has the potential to impact agricultural productivity. In each setting, end-users in disaster management need access to data processing tools helpful in mapping past and current disasters. Analysis of past events supports risk mitigation by understanding what has already occurred and how to alleviate those impacts in the future. Having capabilities to generate the same products in a response setting means that lessons learned from risk analysis will carry forward to event response. Synthetic aperture radar (SAR) data are particularly useful for these activities due to their all-weather 24/7 monitoring capabilities. In this effort we present HydroSAR, a cloud-based SAR data analysis service for the mapping of meteorological and hydrological disasters as well as their impact on population and agriculture. As part of this project we have developed a series of SAR-based value added products for the monitoring of surface hydrology (image time series, change detection, flood extent, flood depth) and the assessment of impacts on population (flood depth) and agriculture (active agriculture, inundated agriculture, flood duration). We also developed a cloud-based platform for generating these products over affected areas and are working with end-users to integrate derived product into decision-making workflows The paper will briefly introduce the SAR-based products that were developed for this effort. We describe the cloud-based production pipeline that was built to automatically generate these products in near-real time over extended regions. The integration of SAR-based information into hazard preparation and response activities is described for a number of recent disasters including the 2019 forest fires in Alaska, 2019 flooding in the U.S. Midwest, the 2020 U.S. severe weather easter outbreak, 2020 tropical storm Christobal, 2020 cyclone Amphan, 2020, Alaska Spring breakup flooding, and the 2020 flood season in Eastern India, Bangladesh, and Nepal.

Franz Josef Meyer↗
NASA NTRS2020

Atacama Cosmology Telescope: Component-separated maps of CMB temperature and the thermal Sunyaev-Zel'dovich effect

Optimal analyses of many signals in the cosmic microwave background (CMB) require map-level extraction of individual components in the microwave sky, rather than measurements at the power spectrum level alone. To date, nearly all map-level component separation in CMB analyses has been performed exclusively using satellite data. In this paper, we implement a component separation method based on the internal linear combination (ILC) approach which we have designed to optimally account for the anisotropic noise (in the 2D Fourier domain) often found in ground-based CMB experiments. Using this method, we combine multi-frequency data from the Planck satellite and the Atacama Cosmology Telescope Polarimeter (ACTPol) to construct the first wide area (≈2100 sq. deg.), arcminute-resolution component-separated maps of the CMB temperature anisotropy and the thermal Sunyaev-Zel'dovich (tSZ) effect sourced by the inverse-Compton scattering of CMB photons off hot, ionized gas. Our ILC pipeline allows for explicit deprojection of various contaminating signals, including a modified blackbody approximation of the cosmic infrared background (CIB) spectral energy distribution. The cleaned CMB maps will be a useful resource for CMB lensing reconstruction, kinematic SZ cross-correlations, and primordial non-Gaussianity studies. The tSZ maps will be used to study the pressure pro les of galaxies, groups, and clusters through cross-correlations with halo catalogs, with dust contamination controlled via CIB deprojection. The data products described in this paper are available on LAMBDA.

Mathew S. Madhavacheril↗
NASA NTRSDate not supplied

Phenomena Portal: Large- Scale Visual Exploration of Atmospheric Phenomena

The Earth science community is experiencing a high influx of remote sensing data due to recent advancements in sensor technology. This enables the community to extend their research on a larger scale than ever before. Unfortunately, traditional data processing techniques do not scale well to these new, high volume data sources. State-of-the-art machine learning (ML) pipelines have been proven to overcome these burdens in various other fields but are underexploited within the physical sciences community. Moreover, ML is reliant on labeled data, which is currently sparsely available, owing to the fact that ML adoption is still in the early stages within the Earth and atmospheric science communities. To address these issues, we developed the Phenomena Portal, a visual exploration tool that uses ML to detect various atmospheric phenomena on a global scale. This allows the Earth and atmospheric science communities to view trends of occurrences of phenomena, identify potential relationships between them, and analyze spatiotemporal patterns over time. These detections can also serve as initial labeled data for ML research pertaining to the respective phenomena. The tool also incorporates feedback from subject matter experts to further improve the model detection accuracy, thereby facilitating human-in-the-loop. This presentation will provide an overview of the ML model development and cloud deployment. We also discuss the capabilities of the user interface for displaying the detections.

Muthukumaran Ramasubramanian↗
NASA NTRS2018

WebGeocalc and Cosmographia: Modern Tools to Access OPS SPICE Data

For more than two decades navigation and other ancillary data from most US and international planetary science missions have been packaged using "SPICE" (Spacecraft, Planet, Instrument, Camera-matrix, Events) system data files (a.k.a. SPICE kernels) and, in conjunction with SPICE Toolkit software used by scientists and engineers to compute observation geometry in various ground system tools ranging from mission planning and analysis applications to data production pipelines to science analysis tools. The traditional way for accessing SPICE data is by downloading necessary SPICE kernels to a user’s workstation, installing the SPICE Toolkit software available from NAIF, and writing an application calling APIs from the SPICE Toolkit library to compute numeric geometric parameters of interest. While this approach did and still does provide the greatest flexibility in implementing geometric computations of interest, it proved to be complicated for users with little programming abilities, required data to be always copied to the users’ workstations, and lacked any out-of-the-box visualization capabilities. To address these shortcomings NAIF developed the WebGeocalc (WGC) tool and extended the publicly available Cosmographia program to use SPICE. Employing these two new tools in mission operations enables easier access to SPICE computations and SPICE-based visualizations for a wider variety of mission personnel.

Semenov, Boris V.↗
NASA NTRS2021

Mass Inferencing Model Creation And Deployment To Lunar Excavation Robot, RASSOR

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. This research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. Radio wave propagation time to the Moon and back is ~2.56 seconds. Though teleoperation is possible with this delay, autonomous capability that enables RASSOR to plan and execute excavation missions intelligently and efficiently is preferred. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation (e.g. knowledge of whether drums are full informs the task of highest priority, whether it be continuing to dig, or returning to a processing plant to offload regolith). A configurable data reduction and analysis pipeline was created to allow for straightforward incorporation of new data, such as that from lunar excavation, to improve model performance in new environments. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All four models take in system states and output a mass prediction for each set of the robot’s bucket drums. Initial results from deployment to RASSOR and testing in a simulated lunar environment show that the models have <10% mean error during robot operation. Future work includes refinement of a model that estimates regolith mass in real-time during excavation as well as further testing of the developed models on the hardware.

ROS↗
NASA NTRS2020

A Habitable-zone Earth-sized Planet Rescued from False Positive Status

We report the discovery of an Earth-sized planet in the habitable zone of a low-mass star called Kepler-1649. The planet, Kepler-1649 c, is 1.06(+0.15, -0.10.) times the size of Earth and transits its 0.1977 ± 0.0051 Mʘ “mid” M-dwarf host star every 19.5 days. It receives 74% ± 3% the incident flux of Earth, giving it an equilibrium temperature of 234 ± 20 K and placing it firmly inside the circumstellar habitable zone. Kepler-1649 also hosts a previously known inner planet that orbits every 8.7 days and is roughly equivalent to Venus in size and incident flux. Kepler-1649 c was originally classified as a false positive (FP) by the Kepler pipeline, but was rescued as part of a systematic visual inspection of all automatically dispositioned Kepler FPs. This discovery highlights the value of human inspection of planet candidates even as automated techniques improve, and hints that terrestrial planets around mid to late M-dwarfs may be more common than those around more massive stars.

Andrew Vanderburg↗
NASA NTRS2020

Data Accountability and Uncertainty Analysis for the Mars Science Laboratory

This paper presents machine learning-based approaches to automate and optimize the detection of volume loss for the downlink process of telemetry data from the Mars Curiosity Rover. The Curiosity observes volume loss and data corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the data flow. To resolve this issue, we created a data pipeline to accumulate data from various data sources in the downlink process and detect where the data is missed. In this paper, we benchmarked different methodologies based on the accuracy and excitability of them to identify whether a downlink data that is received to the ground system is complete or incomplete. Our results show that machine learning methods can improve the performance of the GDSA by 55% while the user can diagnose why data is missed and provide an explanation for the data accountability problem.

Chowdhury, Ameera↗
NASA NTRSDate not supplied

Turbulence, Transition, and Numerical Method Technologies

The close collaboration in the validation experimentation effort was excellent, the complementary research efforts related tofundamental numerical model development, and the activities related to effective HPC utilization on near term architectures that are coming down the pipeline were exactly what NASA should be doing. Even though the TQR panel cannot say that the success criteria were fully met, we do feel that the research funded by TTT/RCA was successful and useful in determining the next suite of CFD validation tests and the problematic flow conditions that continue to be difficult to predict using our current suite of computational tools. Given the TQR Panel’s review criteria: 1. The deliverables meet the technical requirements; 2.The appropriate technical approaches were followed in producing the deliverables; and 3. All technical caveats and concerns have been identified and the deliverables: a.Development of more accurate physics-based methods (e.g., higher moment closure); b. Large eddy simulation (LES); c. Advanced numerical methods; d. Transition prediction and modeling; e.Validation experiments; f. Multidisciplinary analysis and design (high fidelity). TQR panel feels that the technical processes were all completed and that the review criteria and all the deliverables were met. Thus, the Milestone TACP-2016-001 has been completed.

Mujeeb R Malik↗
NASA NTRS2018

The Zwicky Transient Facility: System Overview, Performance, and First Results

The Zwicky Transient Facility (ZTF) is a new optical time-domain survey that uses the Palomar 48 inch Schmidt telescope. A custom-built wide-field camera provides a 47 deg ^(2) field of view and 8 s readout time, yielding more than an order of magnitude improvement in survey speed relative to its predecessor survey, the Palomar Transient Factory. We describe the design and implementation of the camera and observing system. The ZTF data system at the Infrared Processing and Analysis Center provides near-real-time reduction to identify moving and varying objects. We outline the analysis pipelines, data products, and associated archive. Finally, we present on-sky performance analysis and first scientific results from commissioning and the early survey. ZTF’s public alert stream will serve as a useful precursor for that of the Large Synoptic Survey Telescope.

Eric C. Bellm↗
NASA NTRSDate not supplied

Physical Properties of the Solar Atmosphere Derived from Comparison of Spectro-Polarimetric SDO/HMI Observables with 3D Radiative MHD Simulations

In this study, we compare the SDO/HMI line-of-sight observables (magnetic field, velocity, continuum intensity, and line depth) with the related physical properties for several dynamo simulation runs performed using the "StellarBox" 3D Radiative MHD code. The modeling of the Fe I 6173 A Stokes profiles is performed using the SPINOR radiative transfer code in the LTE approximation. The reproduced SDO/HMI line-of-sight pipeline is applied to the modeled spectra, and the observables are synthesized with high (numerical) and SDO/HMI (instrumental) resolutions. Correlations between the observables and the physical properties at various heights in the atmosphere are studied for a set of view angles (0, 30, 45, 60, 70, and 80 degrees away from the solar disk center). It is found that the SDO/HMI magnetic field and velocity measurements are unambiguously correlated with physical parameters at certain heights of the solar atmosphere. These heights increase from about 100 km above the photosphere for the disk center case to 300-600 km above the photosphere for the 80-degree case. The heights are found to be slightly lower in regions where stronger magnetic fields are found. The comparison of the photospheric magnetic flux and integrated continuum intensity derived from the SDO/HMI observables and high-resolution observations and spectra is discussed. The results of our study improve physics-based interpretations of the SDO/HMI observables and provide a better understanding of the physical properties of the solar atmosphere.

SMD↗
NASA NTRS2021

Real-time Unimpeded Taxi Out Machine Learning Service

This paper describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.

machine learning↗
NASA NTRS2021

Real-time Unimpeded Taxi Out Machine Learning Service

This presentation describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.

Machine Learning↗
NASA NTRSDate not supplied

Synthetic Hyperspectral Data for Global Water Quality Algorithm Development

Eutrophication and increasing prevalence of potentially toxic algal blooms (cyanoHABs) among global inland water bodies have become a major ecological concern and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local aquatic processes, to spatially resolved global products. Planned aquatic biogeochemistry remote sensing data products from hyperspectral imagers such as NASA’s Surface Biology and Geology (SBG) mission and relevant aquatic sensor sensitivity precursor airborne imaging spectrometer data provide unprecedented radiometric resolution and sensor sensitivity for characterizing complex aquatic ecosystems. However, scarcity of high-quality freshwater in-situ optical data hinders our capability to develop and validate robust retrieval algorithms. A state-of-the-art synthetic dataset of paired top-of-atmosphere, bottom-of-atmosphere, and optical and biogeophysical data was developed through radiative transfer modeling to simulate natural freshwater ecosystems. A synthetic or precursor dataset for SBG is being used to train robust machine learning models to derive water quality products pertinent to SBG mission objectives. The dataset is also used to show the potential of performing vigorous aquatic sensitivity studies and explored pathways for how best to optimize hyperspectral data for machine learning development. A processing pipeline and resultant global synthetic/precursor dataset for inland waters is presented to establish the innovation for water quality studies of inland waters globally. Optical Society of America Imaging and Applied Optics Congress, Hyperspectral Imaging and Sounding of the Environment (OSA HISE) Meeting, 19-23 July 2021, Virtual Meeting, https://www.osa.org/enus/meetings/osa_meetings/optical_sensors_and_sensing_congress/program/hyperspectral_imaging_and_sounding_of_the_environm/

Synthetic↗
NASA NTRSDate not supplied

Using Machine Learning to Classify Earth Science Articles

Developing a machine learning pipeline to automatic the classification of research article topics, where topics are geophysical descriptors. The resulting classified documents will be added to the Usage-Based Discovery graph database, along with corresponding NASA Earth Science datasets, with the intent of linking the NASA data product with uses of it that can be searched by topic. The project will aid in the following goals (1) creating a wider representation of how and how much NASA Earth Science data products are used, (2) identifying when NASA data products have been used even when they are not clearly cited, and (3) providing the UBD search tool end-user with set of recommended datasets based on a given topic query.

Machine Learning↗
NASA NTRS2021

TESS Data Release Notes: Sectors 1 – 36, Multi-sector Search, DR53

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). This data release is a combined, multi-sector transit search only. The underlying data products from individual observing sectors have been previously released. The data products included in this data release are the Data Validation (DV) reports, time series, and associated xml files for the threshold crossing events (TCEs) found by searching a combined data set including data from multiple observing sectors. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD)1. The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2020)2. The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018) contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

TESS↗
NASA NTRSDate not supplied

Growth Curve Parameterization of Metabolic Activity of Yeast Cells for BioSentinel

The goal of the BioSentinel small satellite payload is to measure the effect of deep space radiation on the growth and metabolic activity of yeast cells. Raw test data is generated by fluidics cards containing yeast cells rehydrated at different periods, with metabolic activity measured by the reduction of alamarBlue. Each card well has a sensor array that measures the amount of red, green, and infrared light transmitted through the yeast culture. This illumination data is then converted to absorbance values, which are further converted into concentrations. The ultimate objective is to convert these concentrations into biologically-relevant metrics that can be compared against one another to determine changes due to differential radiation exposure. Beginning with IR absorbance data (corresponding to cell density) from ground studies, three parameters from a sigmoidal growth curve were extracted and analyzed: 𝜆 (lag phase), 𝜇 (max growth rate), and A (max cell growth). The data was fit to the Gompertz model of microbial growth using non-linear regression (Minitab), as the fit error was reduced compared to the simpler logistic growth curve. Graphs showed that the data contained a discrepancy (drift) in the lag phase that is attributable to a slow, constant loss of moisture. Correcting this discrepancy by fitting the first 25 hours of the data to a power function and subtracting these values from the absorbance readings obtained a better statistical fit to the growth curve in the lag phase. A power fit was selected over a linear fit because it reflected the effects of constant volume loss. This correction to the BioSentinel data analysis pipeline will enable quantitative statistical analysis of the effect of different levels of deep space radiation on yeast cells. Future work includes automation of drift correction and curve modeling to extract these parameters directly from data.

Growth Curve↗
NASA NTRS2021

All-sky search in early O3 LIGO data for continuous gravitational-wave signals from unknown neutron stars in binary systems

Rapidly spinning neutron stars are promising sources of continuous gravitational waves. Detecting such a signal would allow probing of the physical properties of matter under extreme conditions. A significant fraction of the known pulsar population belongs to binary systems. Searching for unknown neutron stars in binary systems requires specialized algorithms to address unknown orbital frequency modulations. We present a search for continuous gravitational waves emitted by neutron stars in binary systems in early data from the third observing run of the Advanced LIGO and Advanced Virgo detectors using the semicoherent, GPU-accelerated, binaryskyhough pipeline. The search analyzes the most sensitive frequency band of the LIGO detectors, 50–300 Hz. Binary orbital parameters are split into four regions, comprising orbital periods of three to 45 days and projected semimajor axes of two to 40 light seconds. No detections are reported. We estimate the sensitivity of the search using simulated continuous wave signals, achieving the most sensitive results to date across the analyzed parameter space.

Gravitation↗
NASA NTRS2021

Search for anisotropic gravitational-wave backgrounds using data from Advanced LIGO and Advanced Virgo's first three observing runs

We report results from searches for anisotropic stochastic gravitational-wave backgrounds using data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. For the first time, we include Virgo data in our analysis and run our search with a new efficient pipeline called PyStochon data folded over one sidereal day. We use gravitational-wave radiometry(broadband and narrow band) to produce sky maps of stochastic gravitational-wave backgrounds and to search for gravitational waves from point sources. A spherical harmonic decomposition method is employed to look for gravitational-wave emission from spatially-extended sources. Neither technique found evidence of gravitational-wave signals. Hence we derive 95% confidence-level upper limit sky maps on the gravitational-wave energy flux from broadband point sources, ranging from F(α,Θ) < (0.013−7.6)×10^(−8)erg/sq. cm s Hz,and on the (normalized) gravitational-wave energy density spectrum from extended sources, ranging from Ω(α,Θ) < (0.57−9.3)×10^(−9) per sr, depending on direction (Θ) and spectral index (α). These limits improve upon previous limits by factors of 2.9−3.5. We also set 95% confidence level upper limits on the frequency-dependent strain amplitudes of quasimonochromatic gravitational waves coming from three interesting targets, Scorpius X-1, SN1987A and the Galactic Center, with best upper limits range fromh(0) < (1.7−2.1)×10^(−25), a factor of ≥ 2.0 improvement compared to previous stochastic radiometer searches.

R. Abbott↗