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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The DESI Early Data Release white dwarf catalogue

The Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) comprises spectroscopy obtained from 2020 December 14 to 2021 June 10. White dwarfs were targeted by DESI both as calibration sources and as science targets and were selected based on Gaia photometry and astrometry. Here, we present the DESI EDR white dwarf catalogue, which includes 2706 spectroscopically confirmed white dwarfs of which approximately 60 per cent have been spectroscopically observed for the first time, as well as 66 white dwarf binary systems. We provide spectral classifications for all white dwarfs, and discuss their distribution within the Gaia Hertzsprung–Russell diagram. We provide atmospheric parameters derived from spectroscopic and photometric fits for white dwarfs with pure hydrogen or helium photospheres, a mixture of those two, and white dwarfs displaying carbon features in their spectra. We also discuss the less abundant systems in the sample, such as those with magnetic fields, and cataclysmic variables. The DESI EDR white dwarf sample is significantly less biased than the sample observed by the Sloan Digital Sky Survey, which is skewed to bluer and therefore hotter white dwarfs, making DESI more complete and suitable for performing statistical studies of white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Chandra Discovery of a Candidate Hyperluminous X-Ray Source in MCG+11-11-032

We present a multiwavelength analysis of MCG+11-11-032, a nearby active galactic nucleus (AGN), with a unique classification as being both a binary and a dual AGN candidate. With new Chandra observations, we aim to resolve any dual AGN system via imaging data and search for signs of a binary AGN via analysis of the X-ray spectrum. Analyzing the Chandra spectrum, we find no evidence of the previously suggested double-peaked Fe Kα lines; the spectrum is instead best fit by an absorbed power law with a single Fe Kα line, as well as an additional line centered at ≈7.5 keV. The Chandra observation reveals faint, soft, and extended X-ray emission, possibly linked to low-level nuclear outflows. Further analysis shows evidence for a compact hard source—MCG+11-11-032 X2—located 3.″3 from the primary AGN. Modeling MCG+11-11-032 X2 as a compact source, we find that it is relatively luminous (L2–10 keV=1.5−0.5+0.9×1041erg s$^{−1}$), and the location is coincident with a compact and off-nuclear source resolved in Hubble Space Telescope infrared (F105W) and optical (F621M, F547M) bands. Pairing our X-ray results with a 144 MHz radio detection at the host galaxy location, we observe X-ray and radio properties similar to those of ESO 243-49 HLX-1, suggesting that MCG+11-11-032 X2 may be a hyperluminous X-ray source. This detection with Chandra highlights the importance of a high-resolution X-ray imager as well as how previous binary AGN candidates detected with large-aperture instruments can benefit from high-resolution follow-up. Future spatially resolved optical spectra, and deeper X-ray observations, can better constrain the origin of MCG+11-11-032 X2.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2005

Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and inform manual maneuver decisions. Our multi-modal display of this information provides speech output to issue control surface failure warnings to a lesser-used communication channel and provides graphical displays with pilot-selectable !eve!s of details to issues additional information about the failure. We also describe a potential presentation for flight envelope reduction that can be viewed separately or integrated with an existing attitude indicator instrument. Preliminary results suggest that the inductive approach is capable of detecting that a control surface has failed and determining the type of fault. Furthermore, preliminary evaluations suggest that the interface discloses a concise summary of this information to the pilot.

Spikovska, Lilly↗
NASA NTRS2013

Classification of Dust Days by Satellite Remotely Sensed Aerosol Products

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 nm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88 percent occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2 percent correct classifications of DD and NDD. Evaluation of the combined CART-logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

satellite remote sensing↗
DOE OSTI2024

Federated Machine Learning-Based Anomaly Detection System for Synchrophasor Network Using Heterogeneous Data Sets: Preprint

Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.

anomaly detection system↗
DOE OSTI2021

Identifying Periodic Variable Stars and Eclipsing Binary Systems with Long-term Las Cumbres Observatory Photometric Monitoring of ZTF J0139+5245

We present the results of our search for variable stars using the long-term Las Cumbres Observatory (LCO) monitoring of white dwarf ZTF J0139+5245 with the two 1.0 m telescope nodes located at McDonald Observatory using the Sinistro imaging instrument. In this search, we find 38 variable sources, of which 27 are newly discovered or newly classified (71%) based on comparisons with previously published catalogs, thereby increasing the number of detections in the field of view under consideration by a factor of ≈2.5. We find that the improved photometric precision per exposure due to longer exposure time for LCO images combined with the greater time sampling of LCO photometry enables us to increase the total number of detections in this field of view. Each LCO image covers a field of view of 26' × 26' and observes a region close to the Galactic plane (b = -9.°4) abundant in stars with an average stellar density of ≈8 arcmin{sup -2}. We perform aperture photometry and Fourier analysis on over 2000 stars across 1560 LCO images spanning 537 days to find 28 candidate BY Draconis variables, three candidate eclipsing binaries of type EA, and seven candidate eclipsing binaries of type EW. In assigning preliminary classifications to our detections, we demonstrate the applicability of the Gaia color–magnitude diagram as a powerful classification tool for variable-star studies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains

Natural language processing (NLP) tasks (text classification, named entity recognition, etc.) have seen amazing improvements over the last few years. This is due to models such as BERT that achieve deep knowledge transfer by using a large pre-trained model, then fine-tuning the model on specific tasks. The BERT architecture has shown even better performance on domain-specific tasks when the model is pre-trained using domain-relevant texts. Here, inspired by these recent advancements, we have developed NukeLM, a nuclear-domain BERT model pre-trained on 1.5 million abstracts from the DOE Office of Scientific and Technical Information (OSTI) database. This NukeLM model is then fine-tuned for the classification of research articles into either binary classes (related to the nuclear fuel cycle (NFC) or not) or multiple categories related to the subject of the article. We show that continued pre-training of a BERT-style architecture prior to fine-tuning results in greater performance in both article classification tasks. This information is critical for properly triaging manuscripts, a necessary task for better understanding citation networks that publish in the nuclear space and uncovering new areas of research in the nuclear (or nuclear relevant) domain.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2010

CATALOG OF 93 NOVA LIGHT CURVES: CLASSIFICATION AND PROPERTIES

We present a catalog of 93 very-well-observed nova light curves. The light curves were constructed from 229,796 individual measured magnitudes, with the median coverage extending to 8.0 mag below peak and 26% of the light curves following the eruption all the way to quiescence. Our time-binned light curves are presented in figures and as complete tabulations. We also calculate and tabulate many properties about the light curves, including peak magnitudes and dates, times to decline by 2, 3, 6, and 9 mag from maximum, the time until the brightness returns to quiescence, the quiescent magnitude, power-law indices of the decline rates throughout the eruption, the break times in this decline, plus many more properties specific to each nova class. We present a classification system for nova light curves based on the shape and the time to decline by 3 mag from the peak (t{sub 3}). The designations are 'S' for smooth light curves (38% of the novae), 'P' for plateaus (21%), 'D' for dust dips (18%), 'C' for cusp-shaped secondary maxima (1%), 'O' for quasi-sinusoidal oscillations superposed on an otherwise smooth decline (4%), 'F' for flat-topped light curves (2%), and 'J' for jitters or flares superposed on the decline (16%). Our classification consists of this single letter followed by the t{sub 3} value in parentheses; so, for example, V1500 Cyg is S(4), GK Per is O(13), DQ Her is D(100), and U Sco is P(3).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The X-Ray Binary Population of the Nearby Dwarf Starburst Galaxy IC 10: Variable and Transient X-Ray Sources

We have monitored the Cassiopeia dwarf galaxy (IC 10) in a series of 10 Chandra ACIS-S observations to capture its variable and transient X-ray source population, which is expected to be dominated by High Mass X-ray Binaries (HMXBs). We present a sample of 21 X-ray sources that are variable between observations at the 3 σ level, from a catalog of 110 unique point sources. We find four transients (flux variability ratio greater than 10) and a further eight objects with ratios >5. The observations span the years 2003–2010 and reach a limiting luminosity of >10{sup 35} erg s{sup −1}, providing sensitivity to X-ray binaries in IC 10 as well as flare stars in the foreground Milky Way. The nature of the variable sources is investigated from light curves, X-ray spectra, energy quantiles, and optical counterparts. The purpose of this study is to discover the composition of the X-ray binary population in a young starburst environment. IC 10 provides a sharp contrast in stellar population age (<10 My) when compared to the Magellanic Clouds (40–200 My) where most of the known HMXBs reside. We find 10 strong HMXB candidates, 2 probable background Active Galactic Nuclei, 4 foreground flare-stars or active binaries, and 5 not yet classifiable sources. Complete classification of the sample requires optical spectroscopy for radial velocity analysis and deeper X-ray observations to obtain higher S/N spectra and search for pulsations. A catalog and supporting data set are provided.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
DOE OSTI2013

Identification of five interacting binaries in the galactic bulge survey

We present optical light curves, spectroscopy, and classification of five X-ray sources in the Chandra Galactic Bulge Survey (CXOGBS J174009.1–284725 (CX5), CXOGBS J173935.7–272935 (CX18), CXOGBS J173946.9–271809 (CX28), CXOGBS J173729.1–292804 (CX37), CXOGBS J174607.6–261547 (CX561)). These objects were selected based on bright optical counterparts which were quickly found to have emission lines in their optical spectra. This paper presents an illustration of GBS optical follow-up, targeting emission line objects. Of these five objects, four exhibit photometric variability in the Sloan r' band. CX5 shows a tentative period of 2.1 hr and is clearly an intermediate polar (IP). CX28 and CX37 both exhibit flickering with no clear period. Both are also suggested to be IPs. CX18 was observed to undergo two dwarf nova outbursts. Finally, CX561 shows no detectable variability, although its characteristics would be consistent with either a quiescent low-mass X-ray binary or cataclysmic variable.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2005

Data Format Classification for Autonomous Software Defined Radios

We present maximum-likelihood (ML) coherent and noncoherent classifiers for discriminating between NRZ and Manchester coded (biphase-L) data formats for binary phase-shift-keying (BPSK) modulation. Such classification of the data format is an essential element of so-called autonomous software defined radio (SDR) receivers (similar to so-called cognitive SDR receivers in the military application) where it is desired that the receiver perform each of its functions by extracting the appropriate knowledge from the received signal and, if possible, with as little information of the other signal parameters as possible. Small and large SNR approximations to the ML classifiers are also proposed that lead to simpler implementation with comparable performance in their respective SNR regions. Numerical performance results obtained by a combination of computer simulation and, wherever possible, theoretical analyses, are presented and comparisons are made among the various configurations based on the probability of misclassification as a performance criterion. Extensions to other modulations such as QPSK are readily accomplished using the same methods described in the paper.

classification↗
DOE OSTI2025

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Furthermore, although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, here we extract lessons learned and make recommendations for future research.

malware obfuscation↗
DOE OSTI2024

CMOS-Based Single-Cycle in-Memory XOR/XNOR

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for large-scale data copy verification, encryption, and classification algorithms. The disproportionate speed of existing compute and memory units makes the von Neumann architecture inefficient to perform these Boolean operations. Compute-in-memory (CiM) has proved to be an optimum approach for such bulk computations. The existing CiM-based XOR/XNOR techniques either require multiple cycles for computing or add to the complexity of the fabrication process. Here, we propose a CMOS-based hardware topology for single-cycle in-memory XOR/XNOR operations. Our design provides at least 2× improvement in the latency compared with other existing CMOS-compatible solutions. We verify the proposed system through circuit/system-level simulations and evaluate its robustness using a 5000-point Monte Carlo variation analysis. This all-CMOS design paves the way for practical implementation of CiM XOR/XNOR at scaled technology nodes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

The eclipsing AM Herculis variable H1907 + 690

The discovery is reported of an eclipsing cataclysmic variable that exhibits up to 10 percent circular polarization at optical wavelengths, securing its classification as an AM Herculis type binary. The object, H1907 + 609, was located with the guidance of X-ray positions from the HEAO 1 survey. Optical CCD photometry exhibits deep eclipses, from which is derived a precise orbital period of 1.743750 hr. The eclipse duration suggests an inclination angle about 80 deg for a main-sequence secondary star. The optical flux has been persistently faint during observations spanning 1987-1990, while the X-ray measurements suggest long-term X-ray variability. The polarization and photometric light curves can be interpreted with a geometric model in which most of the accretion is directed toward a single magnetic pole, with an accretion spot displaced about 17 deg in longitude from the projection of the secondary star on the white dwarf surface.

Remillard, R. A.↗
NASA NTRS1991

Non-parametric analysis of LANDSAT maps using neural nets and parallel computers

Nearest neighbor approaches and a new neural network, the Binary Diamond, are used for the classification of images of ground pixels obtained by LANDSAT satellite. The performances are evaluated by comparing classifications of a scene in the vicinity of Washington DC. The problem of optimal selection of categories is addressed as a step in the classification process.

Salu, Yehuda↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2023

Direct analysis of the broad-line SN 2019ein: connection with the core-normal SN 2011fe

Type Ia supernovae (SNe Ia) are important cosmological probes and contributors to galactic nucleosynthesis, particularly of the iron group elements. To improve both their reliability as cosmological probes and to understand galactic chemical evolution, it is vital to understand the binary progenitor system and explosion mechanism. The classification of SNe Ia into Branch groups has led to some understanding of the similarities and differences among the varieties of observed SNe Ia. Branch groups are defined by the pseudo-equivalent widths of the two prominent Si ii lines, leading to four distinct groups: core-normal (CN), shallow-silicon, cool, and broad-line (BL). However, partly due to small sample size, little work has been done on the BL group. We perform direct spectral analysis on the pre-maximum spectra of the BL SN 2019ein, comparing and contrasting to the CN SN 2011fe. Both SN 2019ein and SN 2011fe were first observed spectroscopically within 2 d of discovery, allowing us to follow the spectroscopic evolution of both SNe in detail. We find that the optical depths of the primary features of both the CN and BL SNe are very similar, except that there is a Doppler shift between them. We further examine the BL group and show that for nine objects with pre-maximum spectra in the range (–6)–(–2) d with respect to B-maximum all the emission peaks of the Si ii λ6355 line of BL are blueshifted pre-maximum, suggesting a possible classification criterion.

79 ASTRONOMY AND ASTROPHYSICS↗