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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Recovered supernova Ia rate from simulated LSST images

Aims.TheVera C. RubinObservatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods.We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg 2 region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results.We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.

Astronomy & 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↗
DOE OSTI2018

Development of a Computer-Aided Differential Diagnosis System to Distinguish Between Usual Interstitial Pneumonia and Non-specific Interstitial Pneumonia Using Texture- and Shape-Based Hierarchical Classifiers on HRCT Images

A computer-aided differential diagnosis (CADD) system that distinguishes between usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP) using high-resolution computed tomography (HRCT) images was developed, and its results compared against the decision of a radiologist. Six local interstitial lung disease patterns in the images were determined, and 900 typical regions of interest were marked by an experienced radiologist. A support vector machine classifier was used to train and label the regions of interest of the lung parenchyma based on the texture and shape characteristics. Based on the regional classifications of the entire lung using HRCT, the distributions and extents of the six regional patterns were characterized through their CADD features. The disease division index of every area fraction combination and the asymmetric index between the left and right lungs were also evaluated. A second SVM classifier was employed to classify the UIP and NSIP, and features were selected through sequential-forward floating feature selection. For the evaluation, 54 HRCT images of UIP (n = 26) and NSIP (n = 28) patients clinically diagnosed by a pulmonologist were included and evaluated. The classification accuracy was measured based on a fivefold cross-validation with 20 repetitions using random shuffling. For comparison, thoracic radiologists assessed each case using HRCT images without clinical information or diagnosis. The accuracies of the radiologists’ decisions were 75 and 87%. The accuracies of the CADD system using different features ranged from 70 to 81%. Finally, the accuracy of the proposed CADD system after sequential-forward feature selection was 91%.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

EXPLORING THE MORPHOLOGY OF RAVE STELLAR SPECTRA

The RAdial Velocity Experiment (RAVE) is a medium-resolution (R {approx} 7500) spectroscopic survey of the Milky Way that has already obtained over half a million stellar spectra. They present a randomly selected magnitude-limited sample, so it is important to use a reliable and automated classification scheme that identifies normal single stars and discovers different types of peculiar stars. To this end, we present a morphological classification of {approx}350, 000 RAVE survey stellar spectra using locally linear embedding, a dimensionality reduction method that enables representing the complex spectral morphology in a low-dimensional projected space while still preserving the properties of the local neighborhoods of spectra. We find that the majority of all spectra in the database ({approx} 90%-95%) belong to normal single stars, but there is also a significant population of several types of peculiars. Among them, the most populated groups are those of various types of spectroscopic binary and chromospherically active stars. Both of them include several thousands of spectra. Particularly the latter group offers significant further investigation opportunities since activity of stars is a known proxy of stellar ages. Applying the same classification procedure to the sample of normal single stars alone shows that the shape of the projected manifold in two-dimensional space correlates with stellar temperature, surface gravity, and metallicity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data—time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

machine learning, event detection, PMU↗
DOE OSTI2017

Automatic Attribute Profiles

Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multi-scale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able to provide representative and non-redundant threshold decomposition of the image. This paper presents a framework for the automatic selection of filter thresholds based on Granulometric Characteristic Functions (GCFs). GCFs describe the way that non-linear morphological filters simplify a scene according to a given measure. Since attribute filters rely on a hierarchical representation of an image (e.g., the Tree of Shapes) for their implementation, GCFs can be efficiently computed by taking advantage of the tree representation. Eventually, the study of the GCFs allows the identification of a meaningful set of thresholds. Therefore, a trial and error approach is not necessary for the threshold selection, automating the process and in turn decreasing the computational time. It is shown that the redundant information is reduced within the resulting profiles (a problem of high occurrence, as regards manual selection). The proposed approach is tested on two real remote sensing data sets, and the classification results are compared with strategies present in the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An ensemble model of QSAR tools for regulatory risk assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. In conclusion, this feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Learning Memento archive routing with Character-based Artificial Neural Networks

This white paper describes a series of tests that were performed to determine if a neural network could learn patterns from a service that maintains a cache of routing decisions for the discovery of version information for discrete web-at-large URLs. Labeled training data was derived from a log file that records the by-archive availability of Memento availability for a given URL. Training data sets were generated from this log file on a by-archive basis (thus making it a binary classification problem). Each training data set consisted of equal numbers of hit and miss URLs, selected at random. The URLs were converted to a normalized numeric representation where each integer in a URL training vector represents a character in that URL. The corresponding label indicates whether or not Mementos were available for that URL in the selected archive. The training data matrix and the label vector became input to a neural network. A number of neural network architectures and network hyperparameters were explored, however the log entries themselves were used as-is, without any feature engineering, beyond the aforementioned normalization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Air Pollutant Emissions and Regulatory Implications of a Biorefinery Producing Raw Bio-Oil: Preprint

Co-processing bio-oil with petroleum-derived intermediates in existing refineries attracted refineries' interest because it offers a promising opportunity to reduce the carbon intensity of transportation fuels produced and requires relatively low capital investments to retrofit the refineries. A biorefinery producing bio-oil is considered a chemical process plant under the Clean Air Act's permitting program and could be classified as a major or minor source based on the size of the facility and the magnitude of regulated pollutants expected to be emitted. Our analysis examines the air pollutant emissions and potentially applicable federal air quality regulations for a biorefinery utilizing an ex situ catalytic fast pyrolysis process with a design capacity of processing 2,000 dry metric tons of biomass per day to produce 4,200 barrels of raw bio-oil per day. We quantify the preliminary potential-to-emit (PTE) for the biorefinery producing raw bio-oil and examine the ramifications on potential air permit classifications. We also provide an alternative set of PTE estimate by assuming the adoption of selected technically feasible emission control options that could help the biorefinery avoid being subject to major source permitting requirements (e.g., prevention of significant deterioration). Our analysis is expected to provide insights for new bio-oil project developers to identify opportunities to mitigate emissions and develop strategies to overcome potential challenges associated with air permitting.

09 BIOMASS FUELS↗
DOE OSTI2014

Dual-energy cone-beam CT with a flat-panel detector: Effect of reconstruction algorithm on material classification

Purpose: Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size. Methods: Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50–200 mg/ml, 3–28.4 mm diameter) and solid iodine inserts (2–10 mg/ml, 3–28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy. Results: Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼88% for FBP and PLQ, and ∼95% for PLTV at 3.1 mGy total dose, increasing to ∼95% for FBP and PLQ, and ∼98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy >80% was ∼15 mm for FBP and PLQ and ∼10 mm for PLTV, improving to ∼7 mm for FBP and PLQ and ∼3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose. Conclusions: For iodine concentrations >5 mg/ml and detail size ∼20 mm, material classification accuracy of >90% was achieved in DE CBCT with both FBP and PL at total doses <10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement)

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

The merger fraction of post-starburst galaxies in UNIONS

ABSTRACT Post-starburst galaxies (PSBs) are defined as having experienced a recent burst of star formation, followed by a prompt truncation in further activity. Identifying the mechanism(s) causing a galaxy to experience a post-starburst phase therefore provides integral insight into the causes of rapid quenching. Galaxy mergers have long been proposed as a possible post-starburst trigger. Effectively testing this hypothesis requires a large spectroscopic galaxy survey to identify the rare PSBs as well as high-quality imaging and robust morphology metrics to identify mergers. We bring together these critical elements by selecting PSBs from the overlap of the Sloan Digital Sky Survey and the Canada–France Imaging Survey and applying a suite of classification methods: non-parametric morphology metrics such as asymmetry and Gini-M20, a convolutional neural network trained to identify post-merger galaxies, and visual classification. This work is therefore the largest and most comprehensive assessment of the merger fraction of PSBs to date. We find that the merger fraction of PSBs ranges from 19 per cent to 42 per cent depending on the merger identification method and details of the PSB sample selection. These merger fractions represent an excess of 3–46× relative to non-PSB control samples. Our results demonstrate that mergers play a significant role in generating PSBs, but that other mechanisms are also required. However, applying our merger identification metrics to known post-mergers in the IllustrisTNG simulation shows that 70 per cent of recent post-mergers (≲200 Myr) would not be detected. Thus, we cannot exclude the possibility that nearly all PSBs have undergone a merger in their recent past.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient’s specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox’s proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

Rehemtulla, Nabeel (ORCID:0000000256832389)↗
DOE OSTI2011

SEARCHES FOR METAL-POOR STARS FROM THE HAMBURG/ESO SURVEY USING THE CH G BAND

We describe a new method to search for metal-poor candidates from the Hamburg/ESO objective-prism survey (HES) based on identifying stars with apparently strong CH G-band strengths for their colors. The hypothesis we exploit is that large overabundances of carbon are common among metal-poor stars, as has been found by numerous studies over the past two decades. The selection was made by considering two line indices in the 4300 A region, applied directly to the low-resolution prism spectra. This work also extends a previously published method by adding bright sources to the sample. The spectra of these stars suffer from saturation effects, compromising the index calculations and leading to an undersampling of the brighter candidates. A simple numerical procedure, based on available photometry, was developed to correct the line indices and overcome this limitation. Visual inspection and classification of the spectra from the HES plates yielded a list of 5288 new metal-poor (and by selection, carbon-rich) candidates, which are presently being used as targets for medium-resolution spectroscopic follow-up. Estimates of the stellar atmospheric parameters, as well as carbon abundances, are now available for 117 of the first candidates, based on follow-up medium-resolution spectra obtained with the SOAR 4.1 m and Gemini 8 m telescopes. We demonstrate that our new method improves the metal-poor star fractions found by our pilot study by up to a factor of three in the same magnitude range, as compared with our pilot study based on only one CH G-band index. Our selection scheme obtained roughly a 40% success rate for identification of stars with [Fe/H] <-1.0; the primary contaminant is late-type stars with near-solar abundances and, often, emission line cores that filled in the Ca II K line on the prism spectrum. Because the selection is based on carbon, we greatly increase the numbers of known carbon-enhanced metal-poor stars from the HES with intermediate metallicities -2.0 < [Fe/H] <-1.0, which previous survey efforts undersampled. There are eight newly discovered stars with [Fe/H] <-3.0 in our sample, including two with [Fe/H] <-3.5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Bayesian model selection for GRB 211211A through multiwavelength analyses

ABSTRACT Although GRB 211211A is one of the closest gamma-ray bursts (GRBs), its classification is challenging because of its partially inconclusive electromagnetic signatures. In this paper, we investigate four astrophysical scenarios as possible progenitors for GRB 211211A: a binary neutron star merger, a black hole–neutron star merger, a core-collapse supernova, and an r-process enriched core collapse of a rapidly rotating massive star (a collapsar). We perform a large set of Bayesian multiwavelength analyses based on different models describing these scenarios and priors to investigate which astrophysical scenarios and processes might be related to GRB 211211A. Our analysis supports previous studies in which the presence of an additional component, likely related to r-process nucleosynthesis, is required to explain the observed light curves of GRB 211211A, as it cannot be explained solely as a GRB afterglow. Fixing the distance to about $350~\rm Mpc$, namely the distance of the possible host galaxy SDSS J140910.47+275320.8, we find a statistical preference for a binary neutron star merger scenario.

(transients:) gamma-ray bursts↗
DOE OSTI2026

Micro-photoluminescence mapping and Chemometrics for the rapid classification of rare earth materials

This article introduces advancements in chemically mapping rare earth materials using photoluminescence (PL) and chemometrics. By leveraging the high sensitivity and selectivity of PL compared to alternative optical techniques, as well as its compatibility with microscopy, we present enhanced capabilities for noninvasive material screening and characterization. Exemplary PL spectra of samarium(III) and europium(III) in oxide, nitrate, and chloride forms demonstrated the ability to extract detailed chemical information of diverse rare earth particles. Additionally, we introduced efficient PL mapping sequences capable of covering a 9 mm diameter carbon tab within minutes, which highlighted the benefits of rapid, large-area imaging. Furthermore, an integrated approach combining PL mapping with principal component analysis and a random forest classifier enabled the resolution of overlapping spectral peaks from different chemistries and provided accurate material classification. In conclusion, these advancements underscored the versatility and robustness of PL for chemically mapping rare earth materials, with the potential to support applications in mining, energy, environmental monitoring, isotope production and beyond.

Chemometrics↗
DOE OSTI2021

Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314,000 galaxies

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r = 23.6 versus r = 22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314 000 galaxies. 140 000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314 000 galaxies. We use active learning to focus our volunteer effort on the galaxies which, if labelled, would be most informative for training our ensemble. When measured against confident volunteer classifications, the trained networks are approximately 99 per cent accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.

79 ASTRONOMY AND ASTROPHYSICS↗