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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Unveiling the chemical fingerprint of phosphorus-rich stars: I. In the infrared region of APOGEE-2

The origin of phosphorus, one of the essential elements for life on Earth, is currently unknown. Prevalent models of Galactic chemical evolution (GCE) systematically underestimate the amount of P compared to observations, especially at low metallicities. The recently discovered P-rich ([P/Fe] ≳ 1.2 dex) and metal-poor ([Fe/H] ≃ –1.0 dex) giants further challenge the GCE models, calling current theories on stellar nucleosynthesis into question. Since the observed low-mass giants are not expected to produce their high P contents themselves, our primary goal is to find clues on their progenitor or polluter. By increasing the number of known P-rich stars, we aim to narrow down a statistically reliable chemical abundance pattern that defines these peculiar stars. In this way, we place more robust constraints on the nucleosynthetic mechanism that causes the unusually high P abundances. In the long term, identifying the progenitor of the P-rich stars may contribute to the search for the source of P in our Galaxy. We performed a detailed chemical abundance analysis based on the high-resolution near-infrared (H band) spectra from the latest data release (DR17) of the APOGEE-2 survey. Employing the BACCHUS code, we measured the abundances of 13 elements in the inspected sample, which is mainly composed of a recent collection of Si-enhanced giants. We also analyzed the orbital motions and compared the abundance results to possible nucleosynthetic formation scenarios, and also to detailed GCE models. These models were produced with the OMEGA+ chemical evolution code, using four different massive star yield sets to investigate different scenarios for massive star evolution. We enlarged the sample of confirmed P-rich stars from 16 to a group of 78 giants, which represents the largest sample of P-rich stars to date. The sample includes the first detection of a P-rich star in a Galactic globular cluster. Significant enhancements in O, Al, Si, and Ce, as well as systematic correlations among the studied elements, unveil the unique chemical fingerprint of the P-rich stars. In contrast, the high [Mg/Fe] and [(C+N)/Fe] found in some of the P-rich stars with respect to P-normal stars is not confirmed over the full sample because of the current uncertainties. Strikingly, the strong overabundance in the α-element Si is accompanied by normal Ca and S abundances. This is at odds with current stellar nucleosynthesis models of massive stars. Our analysis of the orbital motion showed that the P-rich stars do not belong to a locally specific population in the Galaxy. In addition, we confirm that the majority of the sample stars are not part of binary systems.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

The X-ray spectrm of the Coma Cluster of galaxies

The HEAO 1 A-2 and OSO 8 observations of the Coma Cluster of galaxies in the 2-60 keV energy band are presented. The spectrum is not well fitted by an isothermal bremsstrahlung model with an Fe line. To model the spectrum more accurately, we have performed fits of polytropic models and find that the gas is well fitted by an equation of state with polytropic index ranging between 1.38 and 1.6. This analysis reduces the total cluster mass by a factor of 3-5 below the optically derived virial mass. Using the best-fitting parameters, the gas is found to be a significant fraction (25 percent) of the total cluster mass. The Fe K-alpha line blend is strongly detected, from which we infer 0.18-0.36 solar abundance in the X-ray emitting gas. The Fe K-beta line blend is also weakly detected. The spectrum is not consistent with the existence of a hard X-ray tail at the flux level reported by Bazzano el al. (1984). In addition, the HEAO I scanning mode data place strong constraints on possible contaminating sources in the region surrounding Coma.

Henriksen, M. J.↗
NASA NTRS2000

Rocket Science for the Internet

Rainfinity, a company resulting from the commercialization of Reliable Array of Independent Nodes (RAIN), produces the product, Rainwall. Rainwall runs a cluster of computer workstations, creating a distributed Internet gateway. When Rainwall detects a failure in software or hardware, traffic is shifted to a healthy gateway without interruptions to Internet service. It more evenly distributes workload across servers, providing less down time.

Source record↗
NASA NTRS2020

Evaluation of NASA’s Remote-Sensing Capabilities in Coastal Environments

This work represents the second part of a two-part study set up by the National Aeronautics and Space Administration (NASA) and Bureau of Ocean Energy Management (BOEM) in 2017. The study summarizes a three-year investigation on the feasibility of using satellite remote sensing to monitor emissions of oil and gas operations over the outer continental shelf (OCS) to determine if they have negative impacts on coastal air quality (AQ). The target pollutant is nitrogen dioxide (NO2), for which BOEM conducts regular emissions surveys. The major data source for this report is a May 2019 oceanographic cruise (“SCOAPE” = Satellite Coastal and Oceanic Atmospheric Pollution Experiment) conducted in the Gulf of Mexico (GoM) off Louisiana with the Research Vessel Point Sur, loaded with a suite of trace-gas analyzers and a direct-sun remote sensor Pandora spectrometer that measures column NO2. The SCOAPE cruise, augmented by in situ NO2 and Pandora measurements at Cocodrie (Louisiana; 29.25° N, 90.66° W) showed that (1) under cloud-free conditions, satellites can detect elevated column NO2 amounts near isolated large platforms and from clusters of smaller operations; (2) satellite (the European Space Agency’s TROPOspheric Monitoring Instrument [TROPOMI]) total column (TC) NO2 agrees well with TC NO2 from ground-based Pandora spectrometers (to 11–18%), with the satellite biased low when pollution levels are higher; (3) in general, NO2 measured in situ and in column amounts by Pandora or TROPOMI is greater over coastal Louisiana than over the OCS; (4) the extent to which Pandora or satellite measurements correlate with surface NO2 is highly variable.

NO2↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
DOE OSTI2026

Brightest Cluster Galaxy ellipticity as proxy for halo shape: Orientation bias, assembly bias, and potential selection effects in SZ-selected clusters

The orientation of triaxial galaxy clusters with respect to the line-of-sight is expected to be one of the prime sources of scatter and potential bias in optical observables (e.g., richness and weak-lensing signal) of galaxy clusters. In this work, we use the observed shape of the central Brightest Cluster Galaxy (BCG) as proxy for the orientation along the line-of-sight for clusters selected via the Sunyaev-Zel'dovich (SZ) effect from the South Pole Telescope (SPT) and Atacama Cosmology Telescope (ACT) surveys, matched to optically selected clusters from the Dark Energy Survey Year 3 (DES). We construct two samples of clusters that are designed to be identical in SZ mass estimate and redshift but with the roundest vs. the most elliptical BCGs, which we expect to correspond to BCGs (and clusters) with major axes aligned along the line-of-sight vs. in the plane of the sky, respectively. We find that the optical richness of round-BCG clusters is $\sim 10$% larger than that of elliptical-BCG clusters, in agreement with the expectation from projection effects and presenting the first such detection in data. The density profiles, however, are not in agreement with the expectation from projection effects: the 1-halo term (below $6~h^{-1}\rm{Mpc}$) of both the weak-lensing and galaxy density profiles are the same for the subsamples, contrary to previous studies based on X-ray selected clusters. In the 2-halo regime (above $6~h^{-1}\rm{Mpc}$), we find a significant excess of the elliptical-BCG cluster profiles compared to the round-BCG cluster profiles, which is the opposite of the expectation from numerical simulations. We hypothesize that the intrinsic shape of the BCG reflects not just the orientation angle, but also intrinsic properties of the cluster which can affect both the SZ signal and the amplitude of the 2-halo term.

Srinivasan, Radhakrishnan [SUNY, Stony Brook] (ORC↗
NASA NTRS2019

The Future of Exoplanet Direct Detection

Diffraction fundamentally limits our ability to image and characterize exoplanets. Currant and planned coronagraphic searches for exoplanets are making incredible strides but are fundamentally limited by the inner working angle of a few λ/D. Some crucial topics, such as demographics of exoplanets within the first 50 Myr and the infrared characterization of terrestrial planets, are beyond the reach of the single aperture angular resolution for the foreseeable future. Interferometry offers some advantages in exoplanet detection and characterization and we explore in this white paper some of the potential scientific breakthroughs possible. We demonstrate here that investments in "exoplanet interferometry" could open up new possibilities for speckle suppression through spatial coherence, a giant boost in astrometric precision for determining exoplanet orbits, ability to take a census of young giant exoplanets (clusters <50 Myr age), and an unrivaled potential for infrared nulling from space to detect terrestrial planets and search for atmospheric biomarkers. All signs point to an exciting future for exoplanets and interferometers, albeit a promise that will take decades to fulfill.

John Monnier↗
NASA NTRS2010

Reactive Collision Avoidance Algorithm

The reactive collision avoidance (RCA) algorithm allows a spacecraft to find a fuel-optimal trajectory for avoiding an arbitrary number of colliding spacecraft in real time while accounting for acceleration limits. In addition to spacecraft, the technology can be used for vehicles that can accelerate in any direction, such as helicopters and submersibles. In contrast to existing, passive algorithms that simultaneously design trajectories for a cluster of vehicles working to achieve a common goal, RCA is implemented onboard spacecraft only when an imminent collision is detected, and then plans a collision avoidance maneuver for only that host vehicle, thus preventing a collision in an off-nominal situation for which passive algorithms cannot. An example scenario for such a situation might be when a spacecraft in the cluster is approaching another one, but enters safe mode and begins to drift. Functionally, the RCA detects colliding spacecraft, plans an evasion trajectory by solving the Evasion Trajectory Problem (ETP), and then recovers after the collision is avoided. A direct optimization approach was used to develop the algorithm so it can run in real time. In this innovation, a parameterized class of avoidance trajectories is specified, and then the optimal trajectory is found by searching over the parameters. The class of trajectories is selected as bang-off-bang as motivated by optimal control theory. That is, an avoiding spacecraft first applies full acceleration in a constant direction, then coasts, and finally applies full acceleration to stop. The parameter optimization problem can be solved offline and stored as a look-up table of values. Using a look-up table allows the algorithm to run in real time. Given a colliding spacecraft, the properties of the collision geometry serve as indices of the look-up table that gives the optimal trajectory. For multiple colliding spacecraft, the set of trajectories that avoid all spacecraft is rapidly searched on-line. The optimal avoidance trajectory is implemented as a receding-horizon model predictive control law. Therefore, at each time step, the optimal avoidance trajectory is found and the first time step of its acceleration is applied. At the next time step of the control computer, the problem is re-solved and the new first time step is again applied. This continual updating allows the RCA algorithm to adapt to a colliding spacecraft that is making erratic course changes.

Scharf, Daniel↗
NASA NTRS1994

Detections of hard X-ray emissions from bright early-type galaxies with ASCA

Five bright elliptical galaxies in the Virgo Cluster, NGC 4365, NGC 4374 (M84), NGC 4406 (M86), NGC 4472 (M49), and NGC 4636, were observed with ASCA. In addition to the extended thermal X-ray emission of temperature kT approximately 1 keV, harder X-rays with color temperature kT greater than or equal to 2 keV were detected from all of them. The 2-10 keV luminosities of this hard component for the five galaxies, integrated within 5 min, are distributed within a relatively narrow range of (1-4) x 10(exp 40) ergs/s. The hard X-ray component is primarily attributed to the integrated emission from discrete X-ray sources. In NGC 4406 and NGC 4374 the data indicate that the hard component is contributed additionally by foreground/background emission from the hot intracluster medium (ICM) of the Virgo Cluster. The hard component of NGC 4472 seems also contributed by the Virgo ICM emission, but in this case there is evidence that the ICM brightness is locally enhanced within approximately 10 min of NGC 4472.

Matsushita, K.↗
NASA NTRSDate not supplied

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗
NASA NTRS1993

Gamma rays from globular clusters

Globular clusters are known to contain a relatively large number of pulsars whose individual and collective emission in the X-ray and gamma-ray energy bands may be detectable by the instruments on board the Compton Gamma-Ray Observatory (CGRO), ROSAT, and possibly SIGMA. We discuss the several types of high-energy emission expected from isolated and interacting binary pulsars in globular clusters. Individual or collective high-energy emission from isolated pulsars is expected to be too low to be detected with current instruments. However, a class of high-luminosity hidden millisecond pulsars enshrouded in the evaporating material from irradiated companion stars can produce unpulsed shock emission detectable by the high-sensitivity instruments of ROSAT and CGRO. Establishing upper limits of high-energy emission from globular clusters will be valuable in constraining models for the formation of cluster millisecond pulsars.

Tavani, Marco↗
DOE OSTI2024

Behavioral Segmentation and Clustering of Geospatial Trajectories

The rapid growth of global positioning system (GPS) devices has led to a corresponding increase in the size of GPS datasets. While these large GPS datasets contain a wealth of information about the behaviors of the moving objects in them, manual classification and anomaly detection are prohibitively time consuming. We utilize unsupervised machine learning techniques to first identify the behaviors for individual moving objects and then cluster those objects by their behavioral sequences. In this way, trajectories behaving unusually as well as common patterns of behavior are both detectable in large datasets without requiring an a priori definition of "unusual" or "common."

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Mapping the Perseus galaxy cluster with XRISM: Gas kinematic features and their implications for turbulence

We present extended gas kinematic maps of the Perseus cluster based on a combination of five new XRISM/Resolve pointings observed in 2025 with four performance verification datasets from 2024, totaling a net exposure of 745 ks. To date, Perseus remains the only cluster that has been extensively mapped out to ≃0.7 r 2500 by XRISM/Resolve, while simultaneously offering sufficient spatial resolution to resolve gaseous substructures driven by mergers and active galactic nucleus (AGN) feedback. Our observations cover multiple radial directions and a broad range of dynamical scales, enabling us to characterize the kinematic properties of the intracluster medium up to a scale of ∼500 kpc. In the measurements, we detected high-velocity dispersions (≃300km s −1 ) in the eastern region of the cluster that are spatially coincident with the extended X-ray surface brightness excess and correspond to a nonthermal pressure fraction of ≃7 − 13%. The velocity field outside the AGN-dominant region can be effectively described by a single, large-scale kinematic driver based on the velocity structure function, which statistically favors an energy injection scale of at least a few hundred kpc. The estimated turbulent dissipation energy is comparable to the gravitational potential energy released by a recent merger, implying a significant role of turbulent cascade in the merger energy conversion. In the bulk velocity field, we observed a dipole-like pattern along the east-west direction with an amplitude of ≃ ± 200 − 300 km s −1 , indicating rotational motions induced by the recent merger event. This feature constrains the viewing direction to ≃30° −50° relative to the normal of the merger plane. Our hydrodynamic simulations suggest that Perseus has experienced at least two energetic mergers since redshift z ∼ 1, the most recent of which is associated with the radio galaxy IC310, in agreement with recent SRG/eROSITA findings. This study showcases exciting scientific opportunities for future missions with high-resolution spectroscopic capabilities (e.g., HUBS, LEM, and NewAthena).

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2002

The Soft X-Ray Emission in a Large Sample of Galaxy Clusters with ROSAT PSPC

The study of soft X-ray emission of 38 X-ray selected galaxy clusters observed by ROSAT PSPC indicates that the soft excess phenomenon may be a common occurrence in galaxy clusters. Excess soft X-ray radiation, above the contribution from the hot intra-cluster medium, is evident in a large fraction of sources, and is clearly detected with large statistical significance in the deepest observations. The investigation relies on new, high resolution 21 cm HI observations. The sample selection also features analysis of infrared images, to further ensure reliability of results with respect to the characteristics of Galactic absorption. The possibility of background or calibration effects as cause of the excess emission is likewise investigated; a detailed analysis of the distribution of the excess emission with respect to detector position and Galactic HI column density shows that the excess emission is a genuine celestial phenomenon. We find evidence for a preferential distribution of the soft excess emission at distances larger than approx. 150-200 kpc from the centers of clusters; this behavior may be naturally explained in the context of a non-thermal Inverse-Compton scenario. Alternatively, we propose that the phenomenon maybe caused by thermal emission of very large-scale 'warm' filaments seen in recent hydrodynamic simulations. This new interpretation relieves the very demanding requirements of either the traditional intra-cluster 'warm' gas and the non-thermal scenarios. We also investigate the possibility of the soft excess originating from unresolved, X-ray faint cluster galaxies.

Bonamente, Massimiliano↗
NASA NTRS1979

VV 5-32-63/64 - The 'supermassive double galaxy' in Abell 1775 revisited

Spectroscopy is presented for galaxies in A 1775, the cluster containing VV 5-32-63/64, an apparent double galaxy which has long been considered a gravitationally bound system with supermassive components (10 to the 13th solar masses). However, this model has run afoul of recent radio observations, which detected an extremely long straight radio tail emanating from the eastern galaxy in the pair. The present observations indicate that if both galaxies are members of the same cluster, the cluster radial-velocity dispersion (1522 km/sec) is large enough to account for the velocity difference between the two VV galaxies (1650 km/sec). It is therefore suggested that the VV galaxies are not gravitationally bound and need not be supermassive. The large velocity dispersion measured for A 1775 also strains suggested velocity dispersion vs X-ray luminosity relations for clusters of galaxies, as A 1775 has not yet been detected at X-ray wavelengths.

Hintzen, P.↗
DOE OSTI2026

Machine Learning–Based Condition Monitoring of a Circulating Water System of a Canadian Nuclear Plant

With the need to maintain long-term reliable energy using nuclear power plants, there is an underlying demand to ensure that the maintenance of plant components and systems is also done in an efficient and cost-effective manner. One way to achieve this is by moving from time-based maintenance to condition-based maintenance. The research presented in this paper focuses on applying statistical and machine-learning-based methods to capture anomalies within data for fault detection to further develop into condition monitoring. This paper focuses on system data for a circulating water system (CWS) of a pressurized heavy-water reactor for detecting anomalies. The different methodologies used for detecting and capturing anomalies in the CWS data are matrix profile, density-based spatial clustering of applications with noise (DBSCAN), and support vector machines (SVMs). Matrix profile and DBSCAN are used to distinguish between normal data and anomalous data. This paper presents a hybrid method using DBSCAN and SVM when a portion of the data is used for DBSCAN to generate clusters. This portion of data is then used to train the SVM along with the clusters generated by DBSCAN as output. SVM is then tested on unseen data as a predictive tool, which can work in real time to categorize data points as either normal or anomalous. This paper presents results that show the high accuracies of DBSCAN and SVM in capturing anomalies within the data for a CWS for fault detection. Thus, the maintenance plan would be focused on component condition rather than a time-based schedule by switching to an automated system to identify and predict faults within a CWS.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Machine Learning Atom Probe Tomography Tool For Automatic And Fast Clustering

The software uses a YOLO11 segmentation model trained on synthetic data to analyze APT datasets. The workflow operates as follows: 1. Data Slicing: The APT dataset is divided into multiple 2D cross-sections of a specified thickness. 2. Segmentation: The model identifies point-dense regions within each 2D slice. 3. 3D Reconstruction: Detected regions (masks) from all slices are combined and reconstructed back into the original 3D space, forming clusters. The integration with HPC resources enables the software to process large-scale APT datasets efficiently. This combination of automation and scalability reduces manual intervention, improves reproducibility, and accelerates the clustering workflow.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗