Search NASA⌕ Search

SEARCH · Search NASA

Results for “event classification”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

The Zwicky Transient Facility Bright Transient Survey. I. Spectroscopic Classification and the Redshift Completeness of Local Galaxy Catalogs

The Zwicky Transient Facility (ZTF) is performing a three-day cadence survey of the visible northern sky (∼3π) with newly found transient candidates announced via public alerts. The ZTF Bright Transient Survey (BTS) is a large spectroscopic campaign to complement the photometric survey. BTS endeavors to spectroscopically classify all extragalactic transients with m(peak) ≤ 18.5 mag in either the g(ZTF) or r(ZTF) filters, and publicly announce said classifications. BTS discoveries are predominantly supernovae (SNe), making this the largest flux-limited SN survey to date. Here we present a catalog of 761 SNe, classified during the first nine months of ZTF (2018 April 1–2018 December 31). We report BTS SN redshifts from SN template matching and spectroscopic host-galaxy redshifts when available. We analyze the redshift completeness of local galaxy catalogs, the redshift completeness fraction (RCF; the ratio of SN host galaxies with known spectroscopic redshift prior to SN discovery to the total number of SN hosts). Of the 512 host galaxies with SNe Ia, 227 had previously known spectroscopic redshifts, yielding an RCF estimate of 44% ± 4%. The RCF decreases with increasing distance and decreasing galaxy luminosity (for z < 0.05, or ∼200 Mpc, RCF ≈ 0.6). Prospects for dramatically increasing the RCF are limited to new multifiber spectroscopic instruments or wide-field narrowband surveys. Existing galaxy redshift catalogs are only ∼50% complete at r ≈ 16.9 mag. Pushing this limit several magnitudes deeper will pay huge dividends when searching for electromagnetic counterparts to gravitational wave events or sources of ultra-high-energy cosmic rays or neutrinos.

C. Fremling↗
NASA NTRS2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Rainfall↗
NASA NTRS2024

Bulk Major and Trace Elemental Composition of an Aggregate Sample From Asteroid Bennu

On September 24, 2023, NASA’s OSIRIS-REx mission returned a capsule to Earth carrying material from asteroid Bennu. This event was the first time a U.S. mission delivered pristine samples from an asteroid and is the largest asteroid sample return to date. As these samples represent some of the oldest, most primitive, and pristine materials available to us, and which originate from a known and well-studied asteroid, they allow us a rare opportunity to gain a better understanding of the formation and evolution of our solar system. Key to understanding the material returned from asteroid Bennu is establishing its bulk chemical composition. Previous studies have shown that each chondrite group has a distinct elemental composition. For the carbonaceous chondrites specifically, each group exhibits a distinct pattern of moderately and highly volatile elemental depletions, relative to CI chondrites. CI chondrites are considered the most primitive chondrite group and broadly represent the solar photosphere composition. The two most striking features of these depletion patterns is that the moderately volatile element depletions increase with decreasing 50% condensation temperature and then plateau out at abundances that roughly correlate with matrix abundance. Due to these distinctive patterns, bulk elemental composition has become an important classification tool for establishing the different chondrite groups and the connections between them. As such, determining the bulk chemical composition of the Bennu aggregates will help to test two mission hypothesis: “Bennu’s bulk elemental composition reflects that of its main parent asteroid and is similar to the composition of the Sun, with depletions in moderately to highly volatile elements” and “Bennu’s dominant lithologies are comparable in bulk mineralogy, petrology, and composition to the most aqueously altered carbonaceous chondrites” . Furthermore as the carbonaceous and non-carbonaceous chondrites are thought to have likely formed in the inner and outer protoplanetary disk, respectively (e.g., [8]), determining the bulk chemical compositions of the Bennu aggregates will also help test the major mission hypothesis that “Bennu's parent body formed beyond the snow line by accretion of material in the pro-toplanetary disk” . Regarding chondrite formation, observed elemental patterns have been successfully used to model how the mixing of volatile-rich and volatile-poor chondritic components can produce the observed carbonaceous chondrites groups. They have also been significant in investigating how volatilization processes influenced chondrite formation. As such, establishing bulk chemical compositions of the pristine Bennu samples is vital for understanding the asteroid, and solar system formation. To begin this processes, we analyzed the bulk major and trace elemental compositions of Bennu aggregates.

P Koefoed↗
DOE OSTI2024

Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm

Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout channels. With event rates as high as 40 MHz, these detectors will generate petabytes of data per second. To enable discovery within strict bandwidth and latency constraints, future trackers must be capable of fast, power efficient, and radiation hard data-reduction at the source. This effort is pursuing the co-design development of high-performance readout smart pixel ASICs for a future Phase III High Luminosity upgrade of the Large Hadron Collider. A 1.6mm2 ASIC prototype was designed by Fermilab in CMOS 28 nm bulk process and submitted for manufacturing in February 2024. It leverages the analog front-end pixel design of a previous prototype fabricated and tested in 2023, which achieved a simulated detection level of ~400e- with 30fF input capacitance. The ROIC consists of two matrices of 16×16 smart pixels, each 25×25 μm2 in size. Each smart pixel contains a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. There is digital space for the integration of our fully combinatorial AI that performs momentum classification at the bunch crossing rate. The total power consumption is ∼6μW per pixel, which corresponds to ~1mW/cm2. The ASIC incorporates programmable front-end charge injection circuitry to generate pixel cluster charges during characterization. The cluster profile will be generated to duplicate hit characteristics of various momentum (pT). We will present early results from chip testing.

Parpillon, Benjamin↗
NASA NTRS2021

Applying Machine Learning to Predict Alaskan Ionospheric Irregularities

In this work several machine-learning (ML) techniques for predicting ionospheric irregularities in the northern auroral zone were tested. The techniques include Ridge Regression, Long Short-Term Memory Neural Network (LSTM), Classification Neural Network (CNN), Autoencoder Classification Neural Network (ACNN), and LSTM Autoencoder Classification Neural Network (LACNN). These techniques were tested with the rate of total electron content (TEC) index (ROTI) data collected during 2008 and 2009 from a geodetic station in Fairbanks, Alaska (64.98°N, 147.50°W), which is in the auroral zone. Using ROTI data with the ML techniques, experiments were conducted to reach two goals: (1) examine what space weather measurements present good correlation with ROTI so that they may be helpful in ML-based prediction of ionospheric irregularities in the polar region; (2) predict ROTI hours and days ahead by training the neural network models with historical ROTI data alone. The Ridge Regression experiments indicate that a combination of measurements of local geomagnetic horizontal components, geomagnetic SYM-H index, 3-hour Kp and ap indices, and F10.7 solar flux index appears to be more correlated to the single-site ROTI measurements than other parameters. The neural network (NN) experiments show that although the LACNN model allows for predictions of non-irregularity and irregularity conditions defined by ROTI levels up to 3 hours in advance, with an overall accuracy ≥ 92%, a number of irregularity events can still be missed. Hence, further development is needed to reduce the number of missed events. In this paper, the models, data processing, model performance, prediction results, and potential applications are presented.

Pi, Xiaoqing↗
DOE OSTI2024

Whole-genome demography of COVID-19 virus during its pandemic period and on “panvalent” vaccine design

With over 16 million submitted genomic sequences, the SARS-CoV-2 (SC2) virus, the cause of the most recent worldwide COVID-19 pandemic, has become the most sequenced genome of all known viruses, revealing, for example, a vast number of expanding viral lineages. Since the pandemic phase appears to be over, we performed a retrospective re-examination of the demographic grouping pattern and their genomic characteristics during the entire pandemic period up to the peak of the last pandemic wave. For our study, we extracted from the NCBI only unique viral sequences and converted each sequence data to a relational vector, indicating the presence/absence of each variational event compared to a “reference” sequence. Our study revealed several genomic features that are unexpected or different from those of previous studies. For example, approximately 44,000 variants with unique sequences emerged during the pandemic period; they group into only four major viral-genomic groups and each has a set of mostly unique highly-conserved variant-genotypes (HCVGs); and a small set from the first (“ancestral”) group was inherited by the three (“descendant”) groups, suggesting that HCVGs in the next group may be predictable from the current group(s). Such a concept may be potentially important in designing “panvalent” vaccines against the current and future waves of viral infections.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Characterizing the Spread of COVID-19 from Human Mobility Patterns and SocioDemographic Indicators

Mobility is an indicator of human movement through space and time. With the increasing availability of geolocated data (from GPS, accelerometers, etc.), it is now possible to examine individual as well as group human mobility patterns. Human mobility is influenced by both intrinsic (i.e. personal motivations) and extrinsic (i.e., events like natural hazards or a pandemic like the COVID-19) factors. However, the intricate relationships between human mobility patterns and sociodemographic characteristics in the context of a pandemic are yet to be fully explored. Our goal is to overcome this gap by using human mobility data at the census block group level from mobile phones and combining those with social vulnerability indicators to examine the overall spread of COVID-19 at local spatial scales. We used 585,878 weekly visits to 37,871 points of interests (POIs) from Safegraph to quantify mobility indices and social distancing metrics in 2,820 census block groups in the city of Los Angeles (LA) - before and during lockdown as well as during the phase1 and phase 2 reopening. Finally, using supervised machine learning algorithms, we classified the census block groups in LA into High, Medium and Low categories that represented the vulnerability of these block groups based on the cumulative number of occurrences of COVID-19 cases till July 24, 2020. Our results indicate that the tree-based classifiers performed well in comparison to the Support Vector Machines and Multinomial Logit models. Gradient Boosting had the highest classification accuracy of 97.4% COVID-19 with an AUC score of 0.987. The block groups with high COVID-19 cases also had a high concentration of socially vulnerable populations, high human mobility index and a low social distancing index.

Roy, Avipsa↗
DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗
NASA NTRS2013

NASA Models of Space Radiation Induced Cancer, Circulatory Disease, and Central Nervous System Effects

The risks of late effects from galactic cosmic rays (GCR) and solar particle events (SPE) are potentially a limitation to long-term space travel. The late effects of highest concern have significant lethality including cancer, effects to the central nervous system (CNS), and circulatory diseases (CD). For cancer and CD the use of age and gender specific models with uncertainty assessments based on human epidemiology data for low LET radiation combined with relative biological effectiveness factors (RBEs) and dose- and dose-rate reduction effectiveness factors (DDREF) to extrapolate these results to space radiation exposures is considered the current "state-of-the-art". The revised NASA Space Risk Model (NSRM-2014) is based on recent radio-epidemiology data for cancer and CD, however a key feature of the NSRM-2014 is the formulation of particle fluence and track structure based radiation quality factors for solid cancer and leukemia risk estimates, which are distinct from the ICRP quality factors, and shown to lead to smaller uncertainties in risk estimates. Many persons exposed to radiation on earth as well as astronauts are life-time never-smokers, which is estimated to significantly modify radiation cancer and CD risk estimates. A key feature of the NASA radiation protection model is the classification of radiation workers by smoking history in setting dose limits. Possible qualitative differences between GCR and low LET radiation increase uncertainties and are not included in previous risk estimates. Two important qualitative differences are emerging from research studies. The first is the increased lethality of tumors observed in animal models compared to low LET radiation or background tumors. The second are Non- Targeted Effects (NTE), which include bystander effects and genomic instability, which has been observed in cell and animal models of cancer risks. NTE's could lead to significant changes in RBE and DDREF estimates for GCR particles, and the potential effectiveness of radiation mitigator's. The NSRM- 2014 approaches to model radiation quality dependent lethality and NTE's will be described. CNS effects include both early changes that may occur during long space missions and late effects such as Alzheimer's disease (AD). AD effects 50% of the population above age 80-yr, is a degenerative disease that worsens with time after initial onset leading to death, and has no known cure. AD is difficult to detect at early stages and the small number of low LET epidemiology studies undertaken have not identified an association with low dose radiation. However experimental studies in mice suggest GCR may lead to early onset AD. We discuss modeling approaches to consider mechanisms whereby radiation would lead to earlier onset of occurrence of AD. Biomarkers of AD include amyloid beta (A(Beta)) plaques, and neurofibrillary tangles (NFT) made up of aggregates of the hyperphosphorylated form of the micro-tubule associated, tau protein. Related markers include synaptic degeneration, dentritic spine loss, and neuronal cell loss through apoptosis. Radiation may affect these processes by causing oxidative stress, aberrant signaling following DNA damage, and chronic neuroinflammation. Cell types to be considered in multi-scale models are neurons, astrocytes, and microglia. We developed biochemical and cell kinetics models of DNA damage signaling related to glycogen synthase kinase-3(Beta) (GSK3(Beta)) and neuroinflammation, and considered multi-scale modeling approaches to develop computer simulations of cell interactions and their relationships to A(Beta) plaques and NFTs. Comparison of model results to experimental data for the age specific development of A(Beta) plaques in transgenic mice will be discussed.

Cucinotta, Francis A.↗
DOE OSTI2023

Carbon-enhanced metal-poor star candidates from BP/RP spectra in Gaia DR3

ABSTRACT Carbon-enhanced metal-poor (CEMP) stars comprise almost a third of stars with [Fe/H] < −2, although their origins are still poorly understood. It is highly likely that one sub-class (CEMP-s stars) is tied to mass-transfer events in binary stars, while another sub-class (CEMP-no stars) are enriched by the nucleosynthetic yields of the first generations of stars. Previous studies of CEMP stars have primarily concentrated on the Galactic halo, but more recently they have also been detected in the thick disc and bulge components of the Milky Way. Gaia DR3 has provided an unprecedented sample of over 200 million low-resolution (R ≈ 50) spectra from the BP and RP photometers. Training on the CEMP catalogue from the SDSS/SEGUE database, we use XGBoost to identify the largest all-sky sample of CEMP candidate stars to date. In total, we find 58 872 CEMP star candidates, with an estimated contamination rate of 12 per cent. When comparing to literature high-resolution catalogues, we positively identify 60–68 per cent of the CEMP stars in the data, validating our results and indicating a high completeness rate. Our final catalogue of CEMP candidates spans from the inner to outer Milky Way, with distances as close as r ∼ 0.8 kpc from the Galactic centre, and as far as r > 30 kpc. Future higher resolution spectroscopic follow-up of these candidates will provide validations of their classification and enable investigations of the frequency of CEMP-s and CEMP-no stars throughout the Galaxy, to further constrain the nature of their progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗
DOE OSTI2012

THE CHANDRA MULTI-WAVELENGTH PROJECT: OPTICAL SPECTROSCOPY AND THE BROADBAND SPECTRAL ENERGY DISTRIBUTIONS OF X-RAY-SELECTED AGNs

From optical spectroscopy of X-ray sources observed as part of the Chandra Multi-wavelength Project (ChaMP), we present redshifts and classifications for a total of 1569 Chandra sources from our targeted spectroscopic follow-up using the FLWO/1.5 m, SAAO/1.9 m, WIYN 3.5 m, CTIO/4 m, KPNO/4 m, Magellan/6.5 m, MMT/6.5 m, and Gemini/8 m telescopes, and from archival Sloan Digital Sky Survey (SDSS) spectroscopy. We classify the optical counterparts as 50% broad-line active galactic nuclei (AGNs), 16% emission line galaxies, 14% absorption line galaxies, and 20% stars. We detect QSOs out to z {approx} 5.5 and galaxies out to z {approx} 3. We have compiled extensive photometry, including X-ray (ChaMP), ultraviolet (GALEX), optical (SDSS and ChaMP-NOAO/MOSAIC follow-up), near-infrared (UKIDSS, Two Micron All Sky Survey, and ChaMP-CTIO/ISPI follow-up), mid-infrared (WISE), and radio (FIRST and NVSS) bands. Together with our spectroscopic information, this enables us to derive detailed spectral energy distributions (SEDs) for our extragalactic sources. We fit a variety of template SEDs to determine bolometric luminosities, and to constrain AGNs and starburst components where both are present. While {approx}58% of X-ray Seyferts (10{sup 42} erg s{sup -1} < L{sub 2-10keV} <10{sup 44} erg s{sup -1}) require a starburst event (>5% starburst contribution to bolometric luminosity) to fit observed photometry only 26% of the X-ray QSO (L{sub 2-10keV} >10{sup 44} erg s{sup -1}) population appear to have some kind of star formation contribution. This is significantly lower than for the Seyferts, especially if we take into account torus contamination at z > 1 where the majority of our X-ray QSOs lie. In addition, we observe a rapid drop of the percentage of starburst contribution as X-ray luminosity increases. This is consistent with the quenching of star formation by powerful QSOs, as predicted by the merger model, or with a time lag between the peak of star formation and QSO activity. We have tested the hypothesis that there should be a strong connection between X-ray obscuration and star formation but we do not find any association between X-ray column density and star formation rate both in the general population or the star-forming X-ray Seyferts. Our large compilation also allows us to report here the identification of 81 X-ray Bright Optically inactive Galaxies, 78 z > 3 X-ray sources, and eight Type-2 QSO candidates. Also, we have identified the highest redshift (z = 5.4135) X-ray-selected QSO with optical spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Sifting for Sapphires: Systematic Selection of Tidal Disruption Events in iPTF

We present results from a systematic selection of tidal disruption events (TDEs) in a wide-area (4800~deg 2 ), g+R band, Intermediate Palomar Transient Factory (iPTF) experiment. Our selection targets typical optically-selected TDEs: bright (>60\% flux increase) and blue transients residing in the center of red galaxies. Using photometric selection criteria to down-select from a total of 493 nuclear transients to a sample of 26 sources, we then use follow-up UV imaging with the Neil Gehrels Swift Telescope, ground-based optical spectroscopy, and light curve fitting to classify them as 14 Type Ia supernovae (SNe Ia), 9 highly variable active galactic nuclei (AGNs), 2 confirmed TDEs, and 1 potential core-collapse supernova. We find it possible to filter AGNs by employing a more stringent transient color cut (g-r< -0.2 mag); further, UV imaging is the best discriminator for filtering SNe, since SNe Ia can appear as blue, optically, as TDEs in their early phases. However, when UV-optical color is unavailable, higher precision astrometry can also effectively reduce SNe contamination in the optical. Our most stringent optical photometric selection criteria yields a 4.5:1 contamination rate, allowing for a manageable number of TDE candidates for complete spectroscopic follow-up and real-time classification in the ZTF era. We measure a TDE per galaxy rate of $1.7^{+2.9}_{-1.3}$ ×10 -4 gal -1 yr -1 (90% CL in Poisson statistics). This does not account for TDEs outside our selection criteria, thus may not reflect the total TDE population, which is yet to be fully mapped.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
DOE OSTI2013

THE PROPERTIES OF POST-STARBURST QUASARS BASED ON OPTICAL SPECTROSCOPY

We present optical spectroscopy of a sample of 38 post-starburst quasars (PSQs) at z {approx} 0.3, 29 of which have morphological classifications based on Hubble Space Telescope imaging. These broad-lined active galactic nuclei (AGNs) possess the spectral signatures of massive intermediate-aged stellar populations, making them potentially useful for studying connections between nuclear activity and host galaxy evolution. We model the spectra in order to determine the ages and masses of the host stellar populations, and the black hole masses and Eddington fractions of the AGNs. Our model components include an instantaneous starburst, a power law, and emission lines. We find that the PSQs have M {sub BH} {approx} 10{sup 8} M {sub Sun} accreting at a few percent of Eddington luminosity and host {approx}10{sup 10.5} M {sub Sun} stellar populations which are several hundred Myr to a few Gyr old. We investigate relationships among these derived properties, spectral properties, and morphologies. We find that PSQs hosted in spiral galaxies have significantly weaker AGN luminosities, older starburst ages, and narrow emission-line ratios diagnostic of ongoing star formation when compared to their early-type counterparts. We conclude that the early-type PSQs are likely the result of major mergers and were likely luminous infrared galaxies in the past, while spiral PSQs with more complex star formation histories are triggered by less dramatic events (e.g., harassment, bars). We provide diagnostics to distinguish the early-type and spiral hosts when high spatial resolution imaging is not available.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Utilization of Machine Learning Techniques for Managing the Tracking and Data Relay Satellite Constellation

National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The TDRS constellation consists of multiple geosynchronous communication relay satellites located around the equator so they can provide continual coverage of any mission in low earth orbit. The TDRS are located primarily in three oceanic regions around the earth. NASA’s White Sands Complex provides the ground communication support for TDRS located over the Atlantic and Pacific Oceans. Another TDRS ground station in Guam supports the TDRS over the Indian Ocean. With these satellites the TDRS network can provide continuous coverage of satellites in low-earth orbit. The NASA Space Network (SN) project office at GSFC manages the constellation of spacecraft. Major customers of the TDRS constellation include, but are not limited to, the International Space Station and the Hubble Space Telescope. The TDRS constellation has three generations of satellites and has been active for over 30 years providing reliable communication links between customer satellites and corresponding ground stations. However, one of the major concerns for TDRS, and in any space mission, is to ensure the health and safety of the spacecraft. Generally, engineers use telemetry data to monitor and analyze the performance and state of health of the spacecraft. Telemetry data contains hundreds of parameters that monitor each important component in the spacecraft, which can be utilized to recognize and characterize the behavior of the spacecraft. Each parameter contains considerable information to represent time-dependent properties of each spacecraft subsystem and component. During the entire life of a TDRS spacecraft, thousands of gigabytes of telemetry data are transmitted in real-time from the spacecraft to the ground station at the White Sands Complex in Las Cruces, New Mexico, and recorded as historical data sets for engineers to process and analyze the events that occurred on-orbit. These parameters contain the function of multiple spacecraft subsystems, such as the attitude control system (ACS), Thermal, Electrical Power Subsystem (EPS), etc. . The first and second generations have exceeded their required lifetime and NASA is keen to manage these spacecrafts carefully in order to maximize the remaining life using the spacecraft telemetry. The challenge is to know when the risk of losing a spacecraft in geosynchronous orbit exceeds the benefit of continued operations for customer support. In the TDRS fleet, the EPS is the most critical subsystem related to spacecraft operations. Failure of the EPS would strand a spacecraft in geosynchronous orbit. Since EPS provides power to the spacecraft, component failures ultimately lead to the inability to support the spacecraft loads and the communications payload. For instance, TDRS-8 has several anomalies in EPS including the Bus Voltage Limiter (BVL) shunt current, solar array loss of circuits, and failed battery cells. Any of these anomalies can cause critical issues to the spacecraft. Therefore, developing a system to analyze and perform early detection of a potential anomaly is an important issue in telemetry data analysis. In recent years, Telemetry Mining (TM) has been proposed to process telemetry data by using Data Mining (DM) techniques such as classification, clustering, regression and anomaly detection. Anomaly detection, also known as outlier detection, has been widely used in many data mining areas such as remote sensing, medical data processing and digital image processing. The goal of anomaly detection is to detect abnormal data, which contains a relatively low probability of occurrence among the entire data set. Early detection of anomalies is one of the most significant issues in managing the spacecraft configuration. If anomalies can be detected early enough, then the redundant resources can be used to extend the life of the operational spacecraft. We present an unsupervised anomaly detection method to process the EPS data extracted from TDRS-8. This is different from traditional analytical methods, which use telemetry data to illustrate behavior and physical meaning of each spacecraft component. TM connects multiple parameters as a vector and then conducts data analysis on this high dimension telemetry vector. This method is looking at the properties of a high dimensional vector that is able to consider the relationship between different parameters in the anomaly detection problem. This kind of method performs much better than the traditional limit checking method. In addition, we propose a new approach of real-time anomaly detection to process telemetry data in real-time, which can then be applied to spacecraft monitoring with high reliability, low cost and high accuracy.

Machine Learning (ML)↗
DOE OSTI2016

Characterization of 3D Cirrus Cloud and Radiation Fields Using ARS/AIRS/MODIS data and its Application to Climate Model

During the report period, we have made the following research accomplishments. First, we performed analysis for a number of MODIS scenes comprising of heavy dust events and ice clouds, covering regions of frequent dust outbreaks in East Asia, Middle East, and West Africa, as well as areas associated with long-range dust transports over the Equatorial Tropical Atlantic Ocean. These scenes contain both dust/aerosols and clouds. We collected suitable aerosol/ice-cloud data, correlated ice cloud and aerosol parameters by means of statistical analysis, and interpreted resulting correlation trends based on the physical principles governing cloud microphysics. Aerosol and cloud optical depths and cloud effective particle size inferred from MODIS for selected domains were analyzed from which the parameters including dust aerosol number concentration, ice cloud water path, and ice particle number concentration were subsequently derived. We illustrated that the Twomey (solar albedo) effect can be statistically quantified based on the slope of best-fit straight lines in the correlation study. Analysis of aerosol and cloud retrieval products revealed that for all cases, the region with a larger dust aerosol optical depth is always characterized by a smaller cloud particle size, consistent with the Twomey hypothesis for aerosol-cloud interactions. Second, we developed mean correlation curves with uncertainties associated with small ice-crystal concentration observations for the mean effective ice crystal size (De) and ice water content (IWC) by dividing the atmosphere into three characteristic regions: Tropics cirrus, Midlatitude cirrus, including a temperature classification to improve correlation, and Arctic ice clouds. We illustrated that De has a high correlation with IWC based on theoretical consideration and analysis of thousands of observed ice crystal data obtained from a number of ARM-DOE field campaigns and other experiments. The correlation has the form: ln(De) = a + b ln(IWC) + c ((ln(IWC))2, where a, b, and c are fitting coefficients and are functions of three regions. We demonstrated that this correlation can be effectively incorporated in GCMs and climate models that predict IWC - a significant advance in ice microphysics parameterization for interactive cloud-radiation analysis and feedback. Substantial July mean differences are shown in the OLR (W/m2) and precipitation (mm/day) patterns between UCLA GCM simulations based on Des determined from the De-IWC correlations and the control run using a fixed ice crystal size. Third, in order to improve the computation of spectral radiative transfer processes in the WRF model, we developed a consistent and efficient radiation scheme that can better resolve the spectral bands, determine the cloud optical properties, and provide more reliable and accurate radiative heating fields. In the newly developed radiation module, we have implemented in WRF a modified and improved version referred to as the Fu-Liou-Gu scheme, which includes a combination of delta-four-stream and delta-two-stream approximations for solar and IR flux calculations, respectively. This combination has been proven to be computationally efficient and at the same time to produce a high degree of accuracy. The incorporation of nongray gaseous absorption in multiple scattering atmospheres was based on the correlated k-distribution method. The solar and IR spectra are divided into 6 and 12 bands, respectively, according to the location of absorption bands of H2O, CO2, O3, CH4, N2O, and CFCs. We further included absorption by the water vapor continuum and a number of minor absorbers in the solar spectrum leading to an additional absorption of solar flux in a clear atmosphere on the order of 1-3 W/m2. Additionally, we incorporated the ice microphysics parameterization that includes an interactive mean effective ice crystal size in association with radiation parameterizations. The Fu-Liou-Gu scheme is an ideal tool for the simulation of radiative transfer and ice microphysics within the domain of WRF. It is particularly useful for studying direct and indirect aerosol radiative effects associated with ice cloud formation. The newly implemented radiation module has been demonstrated to work well in WRF and can be effectively used for studies related to cirrus cloud formation and evolution as well as aerosol-cloud-radiation interactions. With the newly implemented radiation scheme, the simulations of cloud cover and ice water path have been improved for cirrus clouds, with a more consistent comparison with the corresponding MODIS observations, especially for optically thin cirrus with an improvement of about 20% in the simulated mean ice water path.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Are Compton-thick AGNs the missing link between mergers and black hole growth?

We examine the host morphologies of heavily obscured active galactic nuclei (AGNs) at z∼1 to test whether obscured super-massive black hole growth at this epoch is preferentially linked to galaxy mergers. Our sample consists of 154 obscured AGNs with N{sub H}>10{sup 23.5} cm{sup −2} and z<1.5. Using visual classifications, we compare the morphologies of these AGNs to control samples of moderately obscured (10{sup 22} cm{sup −2}<N{sub H}<10{sup 23.5} cm{sup −2}) and unobscured (N{sub H}<10{sup 22} cm{sup −2}) AGN. These control AGNs have similar redshifts and intrinsic X-ray luminosities to our heavily obscured AGN. We find that heavily obscured AGNs are twice as likely to be hosted by late-type galaxies relative to unobscured AGNs (65.3{sub −4.6}{sup +4.1}% versus 34.5{sub −2.7}{sup +2.9}%) and three times as likely to exhibit merger or interaction signatures (21.5{sub −3.3}{sup +4.2}% versus 7.8{sub −1.3}{sup +1.9}%). The increased merger fraction is significant at the 3.8σ level. If we exclude all point sources and consider only extended hosts, we find that the correlation between the merger fraction and obscuration is still evident, although at a reduced statistical significance (2.5σ). The fact that we observe a different disk/spheroid fraction versus obscuration indicates that the viewing angle cannot be the only thing differentiating our three AGN samples, as a simple unification model would suggest. The increased fraction of disturbed morphologies with obscuration supports an evolutionary scenario, in which Compton-thick AGNs are a distinct phase of obscured super-massive black hole (SMBH) growth following a merger/interaction event. Our findings also suggest that some of the merger-triggered SMBH growth predicted by recent AGN fueling models may be hidden among the heavily obscured, Compton-thick population.

79 ASTRONOMY AND ASTROPHYSICS↗