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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The miniJPAS survey quasar selection – I. Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey . To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

THE GALACTIC O-STAR SPECTROSCOPIC SURVEY (GOSSS). III. 142 ADDITIONAL O-TYPE SYSTEMS

This is the third installment of the Galactic O-Star Spectroscopic Survey (GOSSS), a massive spectroscopic survey of Galactic O stars, based on new homogeneous, high signal-to-noise ratio, R ∼ 2500 digital observations selected from the Galactic O-Star Catalog. In this paper, we present 142 additional stellar systems with O stars from both hemispheres, bringing the total of O-type systems published within the project to 590. Among the new objects, there are 20 new O stars. We also identify 11 new double-lined spectroscopic binaries, 6 of which are of O+O type and 5 of O+B type, and an additional new tripled-lined spectroscopic binary of O+O+B type. We also revise some of the previous GOSSS classifications, present some egregious examples of stars erroneously classified as O-type in the past, introduce the use of luminosity class IV at spectral types O4-O5.5, and adapt the classification scheme to the work of Arias et al.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

Application of digital image processing techniques to astronomical imagery 1978

Techniques for using image processing in astronomy are identified and developed for the following: (1) geometric and radiometric decalibration of vidicon-acquired spectra, (2) automatic identification and segregation of stars from galaxies; and (3) display of multiband radio maps in compact and meaningful formats. Examples are presented of these techniques applied to a variety of objects.

Lorre, J. J.↗
DOE OSTI2023

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Astronomy & Astrophysics↗
NASA NTRSDate not supplied

Artificial Intelligence Enhancements to Imagery for Space Operations

Philosophy classes still ponder the question asked by Dr. George Berkely, an Anglican Bishop and philosopher in the 1600’s-- “If a tree falls in a forest and no one is around to hear it, does it make a sound?” With that in mind, I ask the following—If a still image or motion imagery from a space mission cannot be found during a search, does it exist? Since the beginning of spaceflight, imagery has been a key form of data collected. Whether for mere curiosity (what does Earth look like from Space?), or for operational reasons (did the solar panel deploy?), or for engineering purposes (what was that object that floated away from the spacecraft?), imagery has been included in space missions. To be useful, though, the image or motion imagery must be accessible and accessed when needed. During the analog era, that typically meant captions and numbers associated with the physical media. With “born digital” imagery, it is possible to add metadata to the image data file. This metadata might include the date and time of capture, mission, camera, exposure data, and similar data fields. Many modern cameras embed some basic metadata into the image file at the moment of capture. The reality, though, is even with today’s born-digital enhancements with embedded metadata at the time of capture, reviewing and cataloging still and motion imagery is very labor intensive. Humans review the imagery for sensitive content (privacy concerns, imagery containing proprietary data/subject matter), and to identify imagery containing crew members or imagery that should be reviewed for engineering or scientific reasons. All this review and manual data entry is very time-consuming. Many improvements in Artificial Intelligence (AI), Machine Learning, and processing power now make it possible to identify persons, objects, motion, color, audio with sensitive content, and other details after or while the imagery is captured.

Rodney Grubbs↗
DOE OSTI2011

GALACTIC S STARS: INVESTIGATIONS OF COLOR, MOTION, AND SPECTRAL FEATURES

Known bright S stars, recognized as such by their enhanced s-process abundances and C/O ratio, are typically members of the asymptotic giant branch (AGB) or the red giant branch. Few modern digital spectra for these objects have been published, from which intermediate resolution spectral indices and classifications could be derived. For published S stars, we find accurate positions using the Two-Micron All Sky Survey (2MASS), and use the FAST spectrograph of the Tillinghast reflector on Mt. Hopkins to obtain the spectra of 57 objects. We make available a digital S star spectral atlas consisting of 14 spectra of S stars with diverse spectral features. We define and derive basic spectral indices that can help distinguish S stars from late-type (M) giants and carbon stars. We convolve all our spectra with the Sloan Digital Sky Survey bandpasses, and employ the resulting gri magnitudes together with 2MASS JHK{sub s} mags to investigate S star colors. These objects have colors similar to carbon and M stars, and are therefore difficult to distinguish by color alone. Using near- and mid-infrared colors from IRAS and Akari, we identify some of the stars as intrinsic (AGB) or extrinsic (with abundances enhanced by past mass transfer). We also use V band and 2MASS magnitudes to calculate a temperature index for stars in the sample. We analyze the proper motions and parallaxes of our sample stars to determine upper and lower limit absolute magnitudes and distances, and confirm that most are probably giants.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

TU-FG-209-07: Medical Physics 1.0 Versus Medical Physics 2.0: A Case Study

Purpose: To illustrate how performance analytics can identify performance decrement in digital radiography systems. Methods: Subsequent to a radiologist’s image quality complaint, four different advanced methods contributed to root cause analysis. Our system was a GE Revolution XQi digital radiography unit. Initially, we reviewed weekly GE Quality Assurance Procedures (QAP) results in a database dating from 2001. Next, we evaluated objective image quality metrics of individual PA Chest radiographs acquired. These images were anonymized, securely transferred, and analyzed by the Duke University Clinical Imaging Physics Group with software previously described{sup 1} and validated{sup 2}. Third, we compared the exposure-dependent SNR{sup 2} (NEQ) of the unit with previously established confidence limits{sup 3}. Finally, we explored our service database to reveal events that might affect detector performance. Results: QAP reported a decrease in CNR reflected in a significant increase in lung noise(Ln), mediastinum noise(Mn), and subdiaphragm-lung contrast(Slc) with a significant decrease in lung grey level(Lgl) after detector replacement. Most change occurred during week 1, before the QAP indicated one-half the ultimate decrease in CNR. After detector recalibration, QAP CNR improved, but was not restored to previous levels. Lgl and Slc were no longer significantly different from before, however Ln and Mn remained significantly different. Exposure-dependent SNR2 show the detector to be operating within limits in October 2006 but subsequently became miscalibrated sometime before acquisition of the 2011–2014 data. Service records revealed catastrophic failure of the Image Detection Controller that contained the 2007 calibration. Traditional metrics did not indicate that the system was performing outside of normal limits. Conclusion: Performance analytics are powerful tools whose proper application could allow early intervention in degraded system performance. The image-quality metrics appear to be highly sensitive to system performance and are reported with every acquisition rather than at arbitrary intervals. Confidence intervals may require customization for individual systems or detectors.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1995

Flight test validation of a frequency-based system identification method on an F-15 aircraft

A frequency-based performance identification approach was evaluated using flight data from the NASA F-15 Highly Integrated Digital Electronic Control aircraft. The approach used frequency separation to identify the effectiveness of multiple controls simultaneously as an alternative to independent control identification methods. Fourier transformations converted measured control and response data into frequency domain representations. Performance gradients were formed using multiterm frequency matching of control and response frequency domain models. An objective function was generated using these performance gradients. This function was formally optimized to produce a coordinated control trim set. This algorithm was applied to longitudinal acceleration and evaluated using two control effectors: nozzle throat area and inlet first ramp. Three criteria were investigated to validate the approach: simultaneous gradient identification, gradient frequency dependency, and repeatability. This report describes the flight test results. These data demonstrate that the approach can accurately identify performance gradients during simultaneous control excitation independent of excitation frequency.

Schkolnik, Gerard S.↗
NASA NTRS2016

Optical Evaluation of Digital Micromirror Devices (DMDs) with UV-Grade Fused Silica, Sapphire, and Magnesium Fluoride Windows and Longterm Reflectance of Bare Devices

Digital micromirror devices (DMDs) are commercial micro-electromechanical systems, consisting of millions of mirrors which can be individually addressed and tilted into one of two states (+/-12deg). These devices were developed to create binary patterns in video projectors, in the visible range. Commercially available DMDs are hermetically sealed and extremely reliable. Recently, DMDs have been identified as an alternative to microshutter arrays for space-based multi-object spectrometers (MOS). Specifically, the MOS at the heart of the proposed Galactic Evolution Spectroscopic Explorer (GESE) uses the DMD as a reprogrammable slit mask. Unfortunately, the protective borosilicate windows limit the use of DMDs in the UV and IR regimes, where the glass has insufficient throughput. In this work, we present our efforts to replace standard DMD windows with custom windows made from UV-grade fused silica, low-absorption optical sapphire (LAOS) and magnesium fluoride (MgF2). We present transmission measurements of the antireflection coated windows and the reflectance of bare (window removed) DMDs. Furthermore, we investigated the long-term stability of the DMD reflectance and experiments for coating DMD active area with a layer of pure aluminum (Al) to boost reflectance performance in the UV spectral range (200−400 nm).

reflectance↗
DOE OSTI2014

SU-E-J-136: Multimodality-Image-Based Target Delineation for Dose Painting of Pancreatic Cancer

Purpose: Dose escalated RT may provide improved disease local-control for selected unresectable pancreatic cancer. Accurate delineation of the gross tumor volume (GTV) inside pancreatic head or body would allow safe dose escalation considering the tolerances of adjacent organs at risk (OAR). Here we explore the potential of multi-modality imaging (DCE-MRI, ADC-MRI, and FDG-PET) to define the GTV for dose painting of pancreatic cancer. Volumetric variations of DCE-MRI, ADC-MRI and FDG-PET defined GTVs were assessed in comparison to the findings on CT, and to pathology specimens for resectable and borderline reseactable cases of pancreatic cancer. Methods: A total of 19 representative patients with DCE-MRI, ADC-MRI and FDG-PET data were analyzed. Of these, 8 patients had pathological specimens. GTV, inside pancreatic head/neck, or body, were delineated on MRI (denoted GTVDCE, and GTVADC), on FDG-PET using SUV of 2.5, 40% SUVmax, and 50% SUVmax (denoted GTV2.5, GTV40%, and GTV50%). A Kruskal-Wallis test was used to determine whether significant differences existed between GTV volumes. Results: Significant statistical differences were found between the GTVs defined by DCE-MRI, ADC-MRI, and FDG-PET, with a mean and range of 4.73 (1.00–9.79), 14.52 (3.21–25.49), 22.04 (1.00–45.69), 19.10 (4.84–45.59), and 9.80 (0.32–35.21) cm3 (p<0.0001) for GTVDCE, GTVADC, GTV2.5, GTV40%, and GTV50%, respectively. The mean difference and range in the measurements of maximum dimension of GTVs based on DCE-MRI, ADC-MRI, SUV2.5, 40% SUVmax, and 50% SUVmax compared with pathologic specimens were −0.84 (−2.24 to 0.9), 0.41 (−0.15 to 2.3), 0.58 (−1.41 to 3.69), 0.66 (−0.67 to 1.32), and 0.15 (−1.53 to 2.38) cm, respectively. Conclusion: Differences exists between DCE, ADC, and PET defined target volumes for RT of pancreatic cancer. Further studies combined with pathological specimens are required to identify the optimal imaging modality and/or acquisition method to define the GTV.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Taxonomy for Common-Cause Failure Vulnerability and Mitigation

Applying current guidance and practices for common-cause failure (CCF) mitigation to digital instrumentation and control (I&C) systems has proven problematic, and the regulatory environment has been unpredictable. The potential for CCF vulnerability inhibits I&C modernization, thereby challenging the long-term sustainability of existing plants. For new plants and advanced reactor concepts, concern about CCF vulnerability in highly integrated digital I&C systems imposes a design burden that results in higher costs and increased complexity. The regulatory uncertainty in determining which mitigation strategies will be acceptable (e.g., what diversity is needed and how much is sufficient) drives designers to adopt complicated, costly solutions devised for existing plants. To address the conditions that constrain the transition to digital I&C technology by the US nuclear industry, crosscutting research is needed to resolve uncertainty, demonstrate necessary characteristics, and establish an objective basis for qualification of digital technology for nuclear power plant (NPP) I&C applications. To fulfill this research need, Oak Ridge National Laboratory is investigating mitigation of CCF vulnerability for nuclear-qualified applications. The outcome of this research is expected to contribute to a fundamentally sound, comprehensive basis to qualify digital technology for nuclear power applications. This report documents the development of a CCF taxonomy. The basis for the CCF taxonomy was generated by determining consistent terminology and establishing a classification approach. The terminology is based on definitions from standards, guides, and relevant nuclear power industry technical reports. The classification approach is derived from identified classification schemes focused on I&C systems and key characteristics, including failure modes. The CCF taxonomy provides the basis for a systematic organization of key systems aspects relevant to analyzing the potential for CCF vulnerability and the suitability of mitigation techniques. Development of an effective CCF taxonomy will help to provide a framework for establishing the objective analysis and assessment capabilities desired to facilitate rigorous identification of fault types and triggers that are the fundamental elements of CCF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Three moving groups detected in the LAMOST DR1 archive

We analyze the kinematics of thick disk and halo stars observed by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. We have constructed a sample of 7993 F, G, and K nearby main-sequence stars (d < 2 kpc) with estimates of position (x, y, z) and space velocity (U, V, W) based on color and proper motion from the Sloan Digital Sky Survey DR9 catalog. Three 'phase-space overdensities' are identified in (V, √(U{sup 2}+2V{sup 2})) with significance levels of σ > 3. Two of them (the Hyades-Pleiades stream and the Arcturus-AF06 stream) have been identified previously. We also find evidence for a new stream (centered at V ∼ –180 km s{sup –1}) in the halo. The formation mechanisms of these three streams are analyzed. Our results support the hypothesis that the Arcturus-AF06 stream and the new stream originated from the debris of a disrupted satellite, while the Hyades-Pleiades stream has a dynamical origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Morpho-Hawk

Morpho-Hawk applies projectional mathematical morphology in a fundamentally new way. Morpho-Hawk extracts object information from digital images by filtering with simple geometrical figures such as rectangles or simple curves. Two core algorithms are used to accomplish this: 1) Object Detection and Feature Extraction from Images: Using projectional morphology, Morpho-Hawk finds features of interest within an image, such as contours, shapes, colors, infrared spectra, and more. Because Morpho-Hawk identifies features based on shape or form, the method can uniquely handle different image or object conditions and directions. MorphoHawk can evaluate all possible images of the analyzed scene using the same transformations that are applied just to one image of that scene. Hence, recognizing a defect within an image or part of an image provides information to assist in recognizing other objects in the image. In addition, known background image information can be morphologically subtracted out in order to focus on the appearance or disappearance of the features of interest. 2) Object Analysis: Upon detection of an object of interest, Morpho-Hawk can analyze the object based learned information from prior images or end-user defined criteria for certain object of special interest by selecting feature (e.g., color, size, shape, apparent volume) and/or symbol (e.g., letter, number). The novel algorithm can analyze targeted objects, even as conditions of such as illumination, shadows, and spectral regions change strongly in following images as compared to the original image. This enables robust recognition, interpolation and prediction. As the analysis shows the presence of these features in the optical signal, the algorithm can make a determination based on user defined probability. The object information can be stored for later analysis using simple morphological data compression methods and/or retention of the original optical images.

Glazoff, MichaelV.↗
NASA NTRS1993

Using machine learning techniques to automate sky survey catalog generation

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Fayyad, Usama M.↗
DOE OSTI2022

Design and Implementation of Smart Buildings: A Review of Current Research Trend

The building sector is one of the largest contributors to the world’s total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques in energy and environmental systems for buildings have recently emerged to address the global challenges. This study reviews existing articles in the literature, mostly since 2000, to explore technological advancement in building energy and environmental systems that can be applied to smart homes and buildings. This review study focuses on an overview of the design and implementation of energy-related smart building technologies, including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance. To review the advancement in building energy-related technologies, a systematic review process is adopted based on available published reviews and research types of articles. Review-type articles are first assessed to explore the current literature on the relevant keywords and to capture major research scopes. Research-type papers are then examined to investigate associated keywords and work scopes, including objectives, focuses, limitations, and future needs. Throughout the comprehensive literature review, this study identifies various techniques of smart home/building applications that have provided detailed solutions or guidelines in different applications to enhance the quality of people’s daily activities and the sustainability of the built environmental system. This paper shows trends in human activities and technology advancements in digital solutions with energy management systems and practical designs. Understanding the overall energy flow between a building and its environmentally connected systems is also important for future buildings and community levels. This paper assists in understanding the pathway toward future smart homes/buildings and their technologies for researchers in related research fields.

renewable system integration↗
NASA NTRS1986

Energy Efficient Engine: Control system preliminary definition report

The object of the Control Preliminary Definition Program was to define a preliminary control system concept as a part of the Energy Efficient Engine program. The program was limited to a conceptual definition of a full authority digital electronic control system. System requirements were determined and a control system was conceptually defined to these requirements. Areas requiring technological development were identified and a plan was established for implementing the identified technological features, including a control technology demonstration. A significant element of this program was a study of the potential benefits of closed-loop active clearance control, along with laboratory tests of candidate clearance sensor elements for a closed loop system.

Howe, David C.↗
NASA NTRS1974

System cost performance analysis (study 2.3). Volume 1: Executive summary

A study is described which was initiated to identify and quantify the interrelationships between and within the performance, safety, cost, and schedule parameters for unmanned, automated payload programs. The result of the investigation was a systems cost/performance model which was implemented as a digital computer program and could be used to perform initial program planning, cost/performance tradeoffs, and sensitivity analyses for mission model and advanced payload studies. Program objectives and results are described briefly.

Campbell, B. H.↗
NASA NTRS2001

Not Your Ordinary GEM

Through Small Business Innovation Research (SBIR) funding from NASA's Stennis Space Center, Geophex devised a new design for broadband electromagnetic sensors. Geophex developed a patented sensing technology, capable not only of coastal monitoring, but also a variety of other functions, including environmental pollution characterization, groundwater contamination detection, archaeological study, and mineral detection. The new technology is offered in several of the company's products the GEM-2, GEM-2A, and the GEM-3. The Geophex products consist of two primary electromagnetic coils, which are stimulated by alternating currents that generate a magnetic field in the object targeted for investigation. GEM-2 is a handheld, lightweight, programmable, digital device. GEM-2A is an airborne version of the sensor. Suspended from a helicopter, the GEM-2A is used to search for mineral deposits and to survey large tracts of land. The GEM-3 is capable of detecting buried landmines and other active munitions. GEM-3 identifies landmines by their brand names. Because each landmine has its own unique electromagnetic response to the broad frequency band emitted by the GEM-3, bomb identification and disposal strategies are made easier.

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