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All sources NASA NTRS DOE OSTI

DOE OSTI2012

DISCOVERY OF 'WARM DUST' GALAXIES IN CLUSTERS AT z {approx} 0.3: EVIDENCE FOR STRIPPING OF COOL DUST IN THE DENSE ENVIRONMENT?

Using far-infrared imaging from the 'Herschel Lensing Survey', we derive dust properties of spectroscopically confirmed cluster member galaxies within two massive systems at z {approx} 0.3: the merging Bullet Cluster and the more relaxed MS2137.3-2353. Most star-forming cluster sources ({approx}90%) have characteristic dust temperatures similar to local field galaxies of comparable infrared (IR) luminosity (T{sub dust} {approx} 30 K). Several sub-luminous infrared galaxy (LIRG; L{sub IR} < 10{sup 11} L{sub Sun }) Bullet Cluster members are much warmer (T{sub dust} > 37 K) with far-infrared spectral energy distribution (SED) shapes resembling LIRG-type local templates. X-ray and mid-infrared data suggest that obscured active galactic nuclei do not contribute significantly to the infrared flux of these 'warm dust' galaxies. Sources of comparable IR luminosity and dust temperature are not observed in the relaxed cluster MS2137, although the significance is too low to speculate on an origin involving recent cluster merging. 'Warm dust' galaxies are, however, statistically rarer in field samples (>3{sigma}), indicating that the responsible mechanism may relate to the dense environment. The spatial distribution of these sources is similar to the whole far-infrared bright population, i.e., preferentially located in the cluster periphery, although the galaxy hosts tend toward lower stellar masses (M{sub *} < 10{sup 10} M{sub Sun }). We propose dust stripping and heating processes which could be responsible for the unusually warm characteristic dust temperatures. A normal star-forming galaxy would need 30%-50% of its dust removed (preferentially stripped from the outer reaches, where dust is typically cooler) to recover an SED similar to a 'warm dust' galaxy. These progenitors would not require a higher IR luminosity or dust mass than the currently observed normal star-forming population.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Timing and Spectral Study of IGR J19294+1816 with the RXTE: The Discovery of Cyclotron Features

The Rossi X-Ray Timing Explorer/Proportional Counter Array (PCA) observations of IGR J19294+1816 covering two outburst episodes are reported. The first outburst happened during MJD 54921-54925 (2009 C.E.), and the second one happened during MJD 55499–55507 (2010 C.E.). In both cases, the PCA observations were made during the decay phase of the outburst, with the source exhibiting a temporal and spectral evolution with the change in flux. At the bright flux level, an absorption feature at 35.5 keV is detected in the spectra, which may be attributed to the Cyclotron Resonance Scattering Feature corresponding to a magnetic field of B=4.13×10{sup 12} Gauss. This is also detected at a lower significance in two other observations. In addition, an Fe line emission at 6.4 keV is prominently detected during the highest flux. X-ray pulsations are detected in 9 out of 10 observations; no pulsations were found in the observation with the lowest flux level. During this observation with the lowest flux, the pulsation phenomenon becomes detectable only at the soft X-ray bands.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness -- A Big Data Visualization Platform

In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this big-data poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A high-speed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmit the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver's campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

Expanding the Discovery Potential of VERITAS via Moonlight Observations

This grant partially supported the base research efforts of the Smithsonian Astrophysical Observatory (SAO), Very-High-Energy (VHE; E > 100 GeV) gamma-ray research group from 8/1/09 to 7/31/14. During the project period, the SAO gamma-ray group carried out a wide-range of research efforts, but focused on VHE observations of extragalactic sources with VERITAS. The SAO group led or co-lead nearly all VERITAS extragalactic working groups and the observations addressed themes in Particle Physics and Fundamental Laws, Cosmology, and Black Holes. The primary topics of this research were processes in exotic galaxies, especially active galactic nuclei and starburst galaxies, which have implications for cosmology and Lorentz invariance violation, as well as indirect dark matter detection via VERITAS observations of dwarf spheroidal galaxies. In addition, the SAO group let the development of unique capabilities for VERITAS to observe during all periods of moonlight. Overall, this has increased the VERITAS data yield by 60% and these data are both scientifically useful and regularly published. This grant funded research that led to contributions towards the publication of 51 refereed journal articles during the project period, including several led by, or with significant contributions from, the SAO group.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Process Discovery through Assimilation of Complex Biogeochemical Datasets

This white paper addresses two of three focal areas identified in the white paper call: 1) Biogeochemical data acquisition and assimilation enabled by machine learning and 3) Insight gleaned from complex data using AI. We focus on AI application to complex biogeochemistry (BGC) data (e.g. laboratory experimental data, field manipulation data, literature data), which is an untapped source of information for improving Earth System Predictability (ESP).

58 GEOSCIENCES↗

DOE OSTI2021

AI4ESP: Process Discovery through Assimilation of Complex Geochemical Datasets

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

CORE-COLLAPSE SUPERNOVAE AND HOST GALAXY STELLAR POPULATIONS

We have used images and spectra of the Sloan Digital Sky Survey to examine the host galaxies of 519 nearby supernovae (SN). The colors at the sites of the explosions, as well as chemical abundances, and specific star formation rates (SFRs) of the host galaxies provide circumstantial evidence on the origin of each SN type. We examine separately SN II, SN IIn, SN IIb, SN Ib, SN Ic, and SN Ic with broad lines (SN Ic-BL). For host galaxies that have multiple spectroscopic fibers, we select the fiber with host radial offset most similar to that of the SN. Type Ic SN explode at small host offsets, and their hosts have exceptionally strongly star-forming, metal-rich, and dusty stellar populations near their centers. The SN Ic-BL and SN IIb explode in exceptionally blue locations, and, in our sample, we find that the host spectra for SN Ic-BL show lower average oxygen abundances than those for SN Ic. SN IIb host fiber spectra are also more metal-poor than those for SN Ib, although a significant difference exists for only one of two strong-line diagnostics. SN Ic-BL host galaxy emission lines show strong central specific SFRs. In contrast, we find no strong evidence for different environments for SN IIn compared to the sites of SN II. Because our SN sample is constructed from a variety of sources, there is always a risk that sampling methods can produce misleading results. We have separated the SN discovered by targeted surveys from those discovered by galaxy-impartial searches to examine these questions and show that our results do not depend sensitively on the discovery technique.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

A luminous X-ray flare from the nucleus of the dormant bulgeless spiral galaxy NGC 247

NGC 247 is a nearby late-type bulgeless spiral galaxy that contains an inactive nucleus. We report a serendipitous discovery of an X-ray flare from the galaxy center with a luminosity of up to 2×10{sup 39} erg s{sup −1} in the 0.3–10 keV band with XMM-Newton. A Chandra observation confirms that the new X-ray source is spatially coincident with the galaxy nucleus. The XMM-Newton data revealed a hard power-law spectrum with a spectral break near 3–4 keV, no pulsations on timescales longer than 150 ms, and a flat power spectrum consistent with Poisson noise from 1 mHz to nearly 10 Hz. Follow-up observations with Swift detected a second flux peak followed by a luminosity drop by a factor of almost 20. The spectral and temporal behaviors of the nuclear source are consistent with the scenario that the flare was due to an outburst of a low-mass X-ray binary that contains a stellar-mass black hole emitting near its Eddington limit at the peak. However, it cannot be ruled out that the sudden brightening in the nucleus was due to accretion onto a possible low-mass nuclear black hole, fed by a tidally disrupted star or a gas cloud; the Monitor of All-sky X-ray Image observations limit the peak luminosity of the flare to less than ∼10{sup 43} erg s{sup −1}, suggesting that it is either a low-mass black hole or an inefficient tidal disruption event.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

SuperCDMS Underground Detector Fabrication Facility

The SuperCDMS SNOLAB dark matter experiment processes Ge and Si crystals into fully tested phonon and ionization detectors at surface fabrication and test facilities. If not mitigated, it is anticipated that trace-level production of radioisotopes in the crystals due to exposure to cosmic rays at (or above) sea level will result in the dominant source of background events in future dark matter searches using the current SuperCDMS detector technology. Fabrication and testing of detectors in underground facilities shielded from cosmic radiation is one way to directly reduce production of trace levels of radioisotopes, thereby improving experimental sensitivity for the discovery of dark matter beyond the level of the current experiment. In this report, we investigate the cost and feasibility to establish a complete detector fabrication processing chain in an underground location to mitigate cosmogenic activation of the Ge and Si detector substrates. For a specific and concrete evaluation, we explore options for such a facility located at SNOLAB, an underground laboratory in Sudbury, Canada hosting the current and future experimental phases of SuperCDMS.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Tripling the Census of Dwarf AGN Candidates Using DESI Early Data

Using early data from the Dark Energy Spectroscopic Instrument (DESI) survey, we search for active galactic nuclei (AGN) signatures in 410,757 line-emitting galaxies. By employing the BPT emission-line ratio diagnostic diagram, we identify AGNs in 75,928/296,261 (≈25.6%) high-mass ($\mathrm{log}({M}_{\star }/{M}_{\odot })\,\gt$ 9.5) and 2444/114,496 (≈2.1%) dwarf ($\mathrm{log}({M}_{\star }/{M}_{\odot })\,\leqslant$ 9.5) galaxies. Of these AGN candidates, 4181 sources exhibit a broad Hα component, allowing us to estimate their BH masses via virial techniques. This study more than triples the census of dwarf AGNs and doubles the number of intermediate-mass black hole (M BH ≤ 10 6 M ⊙ ) candidates, spanning a broad discovery space in stellar mass (7 $\lt \mathrm{log}({M}_{\star }/{M}_{\odot })\,\lt$ 12) and redshift (0.001 < z < 0.45). The observed AGN fraction in dwarf galaxies (≈2.1%) is nearly four times higher than prior estimates, primarily due to DESI’s smaller fiber size, which enables the detection of lower-luminosity dwarf AGN candidates. We also extend the M BH –M⋆ scaling relation down to ${\rm{log}}({M}_{\star }/{M}_{\odot })\,\approx$ 8.5 and $\mathrm{log}({M}_{\mathrm{BH}}/{M}_{\odot })\,\approx$ 4.4, with our results aligning well with previous low-redshift studies. The large statistical sample of dwarf AGN candidates from current and future DESI releases will be invaluable for enhancing our understanding of galaxy evolution at the low-mass end of the galaxy mass function.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

GAMMA-RAY BURSTS FROM MAGNETIC RECONNECTION: VARIABILITY AND ROBUSTNESS OF LIGHT CURVES

The dissipation mechanism that powers gamma-ray bursts (GRBs) remains uncertain almost half a century after their discovery. The two main competing mechanisms are the extensively studied internal shocks and the less studied magnetic reconnection. Here we consider GRB emission from magnetic reconnection accounting for the relativistic bulk motions that it produces in the jet's bulk rest frame. Far from the source the magnetic field is almost exactly normal to the radial direction, suggesting locally quasi-spherical thin reconnection layers between regions of oppositely directed magnetic field. We show that if the relativistic motions in the jet's frame are confined to such a quasi-spherical uniform layer, then the resulting GRB light curves are independent of their direction distribution within this layer. This renders previous results for a delta-function velocity-direction distribution applicable to a much more general class of reconnection models, which are suggested by numerical simulations. Such models that vary in their velocity-direction distribution differ mainly in the size of the bright region that contributes most of the observed flux at a given emission radius or observed time. The more sharply peaked this distribution, the smaller this bright region, and the stronger the light curve variability that may be induced by deviations from a uniform emission over the thin reconnection layer, which may be expected in a realistic GRB outflow. This is reflected both in the observed image at a given observed time and in the observer-frame emissivity map at a given emission radius, which are calculated here for three simple velocity-direction distributions.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗

NASA NTRSDate not supplied

Enhancing NASA Earth Science Data Discovery from Scientific Publications

Earth observations from space borne instruments have evolved explosively in the past decades. Following closely are reanalysis systems assimilating model and observational data, yielding even longer records and larger number of variables. Thanks to advances in internet technology, it is now easier than ever to visualize and analyze these data using web interfaces. On the other hand, it also becomes an increasingly daunting task to build upon the existing knowledge published in various peer reviewed sources, and navigate toward the most relevant data, analysis, and visualization. We present an analysis of a subset of publications that utilized a popular visualization web interface at the NASA Goddard Earth Science Data and Information Services Center. Known as "Giovanni", it allows researchers from wide backgrounds to work with hundreds of variables from space observations and assimilation systems. Since coming online more than a decade ago, Giovanni has been credited in more than 100 papers per year, and the total count now is estimated to be nearly 1,500. Many of these papers contain valuable information about when, where and how Giovanni has been used, and hence forge an opportunity to learn and share the knowledge of which variables were used for what research projects. The purpose of our work is to retrieve the information from the papers and organize it as a knowledge repository which links together datasets, variables, places, dates and phenomena all of which reflect the essence of the published research. Since the publications are unstructured texts, we use natural language processing along with machine learning methods in the retrieval process. One of the challenges is deciphering the dataset names, because in many cases researchers refer to variables, rather than the datasets containing them. To constrain the number of terms, we deploy Earth Science ontologies as dictionaries for the term extraction. We demonstrate that storing these terms and underlying ontologies, along with datasets, variables and papers in the knowledge graph database, enables various linkages between all these entities facilitating the data discovery. Thus, we are setting a qualitatively new stage in improvements of web data interfaces, where machine learning techniques are used to establish and optimize usage-based discovery of data.

Irina V Gerasimov↗

DOE OSTI2014

DISCOVERY OF A STRONG LENSING GALAXY EMBEDDED IN A CLUSTER AT z = 1.62

We identify a strong lensing galaxy in the cluster IRC 0218 (also known as XMM-LSS J02182–05102) that is spectroscopically confirmed to be at z = 1.62, making it the highest-redshift strong lens galaxy known. The lens is one of the two brightest cluster galaxies and lenses a background source galaxy into an arc and a counterimage. With Hubble Space Telescope (HST) grism and Keck/LRIS spectroscopy, we measure the source redshift to be z {sub S} = 2.26. Using HST imaging in ACS/F475W, ACS/F814W, WFC3/F125W, and WFC3/F160W, we model the lens mass distribution with an elliptical power-law profile and account for the effects of the cluster halo and nearby galaxies. The Einstein radius is θ{sub E}=0.38{sub −0.01}{sup +0.02} arcsec (3.2{sub −0.1}{sup +0.2} kpc) and the total enclosed mass is M {sub tot}(<θ{sub E})=1.8{sub −0.1}{sup +0.2}×10{sup 11} M{sub ⊙}. We estimate that the cluster environment contributes ∼10% of this total mass. Assuming a Chabrier initial mass function (IMF), the dark matter fraction within θ{sub E} is f{sub DM}{sup Chab}=0.3{sub −0.3}{sup +0.1}, while a Salpeter IMF is marginally inconsistent with the enclosed mass (f{sub DM}{sup Salp}=−0.3{sub −0.5}{sup +0.2}). The total magnification of the source is μ{sub tot}=2.1{sub −0.3}{sup +0.4}. The source has at least one bright compact region offset from the source center. Emission from Lyα and [O III] are likely to probe different regions in the source.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.

Yoginath, Srikanth↗

DOE OSTI2022

Sodium-ion Battery

Room temperature rechargeable sodium-ion batteries (NIBs) are one of the most promising candidates for large-scale energy storage and load leveling of renewable energy sources due to the abundance, low cost of Na resources, and their relatively high energy density. NIBs are primarily based on intercalation chemistry and are similar to Lithium-ion batteries (LIB). The rich knowledge gained in LIBs for the past 4 decades have accelerated the discovery and development of high energy density NIBs cathode and anode materials. The use of sustainable Na, Al current collector, and Mn and Fe based cathode materials is expected to stabilize the cost and demand for Li, Co, Ni and Cu.

Omenya, Fredrick O.↗

DOE OSTI2022

The population of hot subdwarf stars studied with Gaia: IV. Catalogues of hot subluminous stars based on Gaia EDR3

In light of substantial new discoveries of hot subdwarfs by ongoing spectroscopic surveys and the availability of the Gaia mission Early Data Release 3 (EDR3), we compiled new releases of two catalogues of hot subluminous stars: The data release 3 (DR3) catalogue of the known hot subdwarf stars contains 6616 unique sources and provides multi-band photometry, and astrometry from Gaia EDR3 as well as classifications based on spectroscopy and colours. This is an increase of 742 objects over the DR2 catalogue. This new catalogue provides atmospheric parameters for 3087 stars and radial velocities for 2791 stars from the literature. In addition, we have updated the Gaia Data Release 2 (DR2) catalogue of hot subluminous stars using the improved accuracy of the Gaia EDR3 data set together with updated quality and selection criteria to produce the Gaia EDR3 catalogue of 61 585 hot subluminous stars, representing an increase of 21 785 objects. Furthermore, the improvements in Gaia EDR3 astrometry and photometry compared to Gaia DR2 have enabled us to define more sophisticated selection functions. In particular, we improved hot subluminous star detection in the crowded regions of the Galactic plane as well as in the direction of the Magellanic Clouds by including sources with close apparent neighbours but with flux levels that dominate the neighbourhood.

79 ASTRONOMY AND ASTROPHYSICS↗