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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prospects for fundamental physics with LISA

In this paper, which is of programmatic rather than quantitative nature, we aim to further delineate and sharpen the future potential of the LISA mission in the area of fundamental physics. Given the very broad range of topics that might be relevant to LISA,we present here a sample of what we view as particularly promising fundamental physics directions. We organize these directions through a “science-first” approach that allows us to classify how LISA data can inform theoretical physics in a variety of areas. For each of these theoretical physics classes, we identify the sources that are currently expected to provide the principal contribution to our knowledge, and the areas that need further development. Overall, the classification presented here should not be thought of as cast in stone, but rather as a fluid framework that is amenable to change with the flow of new insights in theoretical physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The simulated catalogue of optical transients and correlated hosts (SCOTCH)

ABSTRACT As we observe a rapidly growing number of astrophysical transients, we learn more about the diverse host galaxy environments in which they occur. Host galaxy information can be used to purify samples of cosmological Type Ia supernovae, uncover the progenitor systems of individual classes, and facilitate low-latency follow-up of rare and peculiar explosions. In this work, we develop a novel data-driven methodology to simulate the time-domain sky that includes detailed modelling of the probability density function for multiple transient classes conditioned on host galaxy magnitudes, colours, star formation rates, and masses. We have designed these simulations to optimize photometric classification and analysis in upcoming large synoptic surveys. We integrate host galaxy information into the snana simulation framework to construct the simulated catalogue of optical transients and correlated hosts (SCOTCH, a publicly available catalogue of 5-million idealized transient light curves in LSST passbands and their host galaxy properties over the redshift range 0 < z < 3. This catalogue includes supernovae, tidal disruption events, kilonovae, and active galactic nuclei. Each light curve consists of true top-of-the-galaxy magnitudes sampled with high (≲2 d) cadence. In conjunction with SCOTCH, we also release an associated set of tutorials and transient-specific libraries to enable simulations of arbitrary space- and ground-based surveys. Our methodology is being used to test critical science infrastructure in advance of surveys by the Vera C. Rubin Observatory and the Nancy G. Roman Space Telescope.

Astronomy & Astrophysics↗
DOE OSTI2022

Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification

The peculiar nature of whole slide imaging (WSI), digitizing conventional glass slides to obtain multiple high resolution images which capture microscopic details of a patient’s histopathological features, has garnered increased interest from the computer vision research community over the last two decades. Given the unique computational space and time complexity inherent to gigapixel-size whole slide image data, researchers have proposed novel machine learning algorithms to aid in the performance of diagnostic tasks in clinical pathology. One effective algorithm represents a Whole slide image as a bag of smaller image patches, which can be represented as low-dimension image patch embeddings. Weakly supervised deep-learning methods, such as cluster-constrained-attention multiple instance learning (CLAM), have shown promising results when combined with image patch embeddings. While traditional ensemble classifiers yield improved task performance, such methods come with a steep cost in model complexity. Through knowledge distillation, it is possible to retain some performance improvements from an ensemble, while minimizing costs to model complexity. In this work, we implement a weakly supervised ensemble using clustering-constrained-attention multiple-instance learners (CLAM), which uses attention and instance-level clustering to identify task salient regions and feature extraction in whole slides. By applying logit-based and attention-based knowledge distillation, we show it is possible to retain some performance improvements resulting from the ensemble at zero cost to model complexity.

Alamudun, Folami↗
DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗
DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Predicting Building Envelope Construction from In-Situ Thermal Testing

When embarking on a retrofit of a building envelope, it is critical to understand the composition of its assembly. This practice is currently done by destructive and invasive material testing or demolition, which is sometimes not possible when in historic or protected buildings. To address this problem, in-situ thermal testing can be utilized along with machine learning classification algorithms to infer the composition of an assembly. In this paper, a proof-of-concept K-nearest neighbors classification model is developed to classify assembly composition from effective thermal resistance, effective thermal mass, and assembly cladding. This model was trained and tested utilizing a synthetic dataset producing an F1-score of 94.6%. This model was also validated with experimental data from a 100-year old wall assembly, confirming the model’s real-world validity. The paper presents a framework for inferring as-built envelope assemblies, all without having to damage or disturb the building and its occupants.

Pilet, Tyler J.↗
DOE OSTI2023

CSAPR2 cell-tracking data collected during TRACER

One of the challenges of analyzing convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great a data set to analyze the evolution of radar observables of convective precipitation clouds statistically, previous studies also suggested that, because of the quick evolution of cell life cycle, conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolution. The TRACER campaign deployed CSAPR2, which performed frequent update of RHI and sector PPI scans to track convective cells every < 2 minutes guided by a new cell-tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. The submitted data files are CSAPR2 data in CfRadial format collected during the TRACER field campaign from June to September 2020. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The miniJPAS survey: White dwarf science with 56 optical filters

We analyze the white dwarf population in miniJPAS, the first square degree observed with 56 medium-band, 145 Å in width optical filters by the Javalambre Physics of the accelerating Universe Astrophysical Survey (J-PAS), to provide a data-based forecast for the white dwarf science with low-resolution ($R$ ~ 50) photo-spectra. We define the sample of the bluest point-like sources in miniJPAS with $r$ < 21.5 mag, a point-like probability larger than 0.5, ($u$ - $r$)< 0.80 mag, and ($g$ - $i$)< 0.25 mag. This sample comprises 33 sources with spectroscopic information: 11 white dwarfs and 22 quasi-stellar objects (QSOs). We estimate the effective temperature ($T$ eff ), the surface gravity, and the composition of the white dwarf population by a Bayesian fitting to the observed photo-spectra. The miniJPAS data are sensitive to the Balmer series and the presence of polluting metals. Our results, combined with those from the Javalambre Photometric Local Universe Survey (J-PLUS) which has a lower spectral resolution but has already observed thousands of white dwarfs, suggest that J-PAS photometry would permit – down to $r$ ~ 21.5 mag and at least for sources with 7000 < $T$ eff < 22 000 K – both the classification of the observed white dwarfs into H-dominated and He-dominated with 99% confidence and the detection of calcium absorption for equivalent widths larger than 15 Å. The effective temperature is estimated with a 2% uncertainty, which is close to the 1% from spectroscopy. A precise estimation of the surface gravity depends on the available parallax information. In addition, the white dwarf population at $T$ eff > 7000 K can be segregated from the bluest extragalactic QSOs, providing a clean sample based on optical photometry alone. The J-PAS low-resolution photo-spectra would produce precise effective temperatures and atmospheric compositions for white dwarfs, complementing the data from Gaia. J-PAS will also detect and characterize new white dwarfs beyond the Gaia magnitude limit, providing faint candidates for spectroscopic follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Searching for Low-mass Population III Stars Disguised as White Dwarfs

It is uncertain whether or not low-mass Population III stars ever existed. While limits on the number density of Population III stars with M {sub *} ≈ 0.8 M {sub ⊙} have been derived, using Sloan Digital Sky Survey (SDSS) data, little is known about the occurrence of Population III stars at lower masses. In the absence of reliable parallaxes, the spectra of metal-poor main-sequence (MPMS) stars with M {sub *} ≲ 0.8 M {sub ⊙} can easily be confused with those of cool white dwarfs. To resolve this ambiguity, in this paper we present a classifier that differentiates between MPMS stars and white dwarfs, based on photometry and/or spectroscopy without the use of parallax information. We build and train our classifier using state-of-the-art theoretical spectra, and evaluate it on existing SDSS-based classifications for objects with reliable Gaia DR2 parallaxes. We then apply our classifier to a large catalog of objects with SDSS photometry and spectroscopy to search for MPMS candidates. We discover several previously unknown, extremely metal-poor (EMP) candidate stars, and recover numerous confirmed EMP stars already noted in the literature. We conclude that archival SDSS spectroscopy has already been exhaustively searched for EMP stars. We predict that the lowest-mass stars of primordial composition will have redder optical-to-infrared colors than cool white dwarfs at constant effective temperature, due to surface gravity-dependent collision-induced absorption from molecular hydrogen. We suggest that the application of our classifier to data produced by next-generation spectroscopic surveys will set stronger constraints on the number density of low-mass Population III stars in the Milky Way.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.

Johnson, Christopher W.↗
DOE OSTI2022

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗
DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

On Feature, Classifier and Detector Fusers for 235U Signatures Using Gamma Spectral Counts

Three types of information fusion strategies are studied to assess the performance of classifiers for detecting low-level 235 U radiation sources, using features obtained from gamma spectra of NaI detectors. These three strategies are based on using two spectral region features, fusing eight classifiers of diverse designs, and fusing multiple detectors located at different positions around the source. The inner, middle and outer groups of detectors, within a formation of two concentric circles and a spiral of 21 detectors, are identified based on their distance to the source, which is located at the center. This study provides two main qualitative insights into this classification task. First, the fusion of detectors leads to an overall improved classification performance, least in the inner group, most in the outer group, and in between for the middle group. Second, several classifiers and fusers achieve lower training error which does not translate to lower generalization error, indicating their over-fitting to training data.

Rao, Nageswara↗
DOE OSTI2021

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Biodefense Knowledge Management System

After the Amerithrax attacks, the Department of Homeland Security (DHS) recognized the need to link technical scientific information with law enforcement and national security information in the fight against bioterrorism. The Biodefense Knowledge Center (BKC) was formed to provide timely analysis of technical biodefense-related topics to support DHS and its customers in the interagency. The Biodefense Knowledge Management System (BKMS) was created as the backbone for information management within the BKC. It is a scalable, web-based knowledge management system for organizing disparate data sources. By combining information from 35+ data sources, the BKMS provides a searchable repository for technical information including scientific reports, journal articles, news articles, research funding databases, patent databases, and genomic datasets. The current system contains over 58 million documents and 450 million genomic records, and operates at several classification levels.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

On Feature, Classifier and Detector Fusers for U-235 Signatures Using Gamma Spectral Counts

Three types of information fusion strategies are studied to assess the performance of classifiers for detecting low-level U-235 radiation sources, using features obtained from gamma spectra of NaI detectors. These three strategies are based on using two spectral region features, fusing eight classifiers of diverse designs, and fusing multiple detectors located at different positions around the source. The inner, middle and outer groups of detectors, within a formation of two concentric circles and a spiral of 21 detectors, are identified based on their distance to the source, which is located at the center. This study provides two main qualitative insights into this classification task. First,the fusion of detectors leads to an overall improved classification performance, least in the inner group, most in the outer group, and in between for the middle group. Second, several classifiers and fusers achieve lower training error which does not translate to lower generalization error, indicating their over-fitting to training data.

Rao, Nageswara↗
DOE OSTI2023

Astronomical image time series classification using CONVolutional attENTION (ConvEntion)

Aims: The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera C. Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods: This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results: In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.

79 ASTRONOMY AND ASTROPHYSICS↗