Results for “Cluster detection” — Page 88

DOE OSTI2022

Discovery of peculiar radio morphologies with ASKAP using unsupervised machine learning

Abstract We present a set of peculiar radio sources detected using an unsupervised machine learning method. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope to train a self-organizing map (SOM). The radio maps from three ASKAP surveys, Evolutionary Map of Universe pilot survey (EMU-PS), Deep Investigation of Neutral Gas Origins pilot survey (DINGO), and Survey With ASKAP of GAMA-09 + X-ray (SWAG-X), are used to search for the rarest or unknown radio morphologies. We use an extension of the SOM algorithm that implements rotation and flipping invariance on astronomical sources. The SOM is trained using the images of all ‘complex’ radio sources in the EMU-PS which we define as all sources catalogued as ‘multi-component’. The trained SOM is then used to estimate a similarity score for complex sources in all surveys. We select 0.5% of the sources that are most complex according to the similarity metric and visually examine them to find the rarest radio morphologies. Among these, we find two new odd radio circle (ORC) candidates and five other peculiar morphologies. We discuss multiwavelength properties and the optical/infrared counterparts of selected peculiar sources. In addition, we present examples of conventional radio morphologies including: diffuse emission from galaxy clusters, and resolved, bent-tailed, and FR-I and FR-II type radio galaxies. We discuss the overdense environment that may be the reason behind the circular shape of ORC candidates.

Astronomy & Astrophysics↗

NASA NTRS2017

Evidence for Surface Water Ice in the Lunar Polar Regions Using Reflectance Measurements from the Lunar Orbiter Laser Altimeter and Temperature Measurements from the Diviner Lunar Radiometer Experiment

We find that the reflectance of the lunar surface within 5 deg of latitude of theSouth Pole increases rapidly with decreasing temperature, near approximately 110K, behavior consistent with the presence of surface water ice. The North polar region does not show this behavior, nor do South polar surfaces at latitudes more than 5 deg from the pole. This South pole reflectance anomaly persists when analysis is limited to surfaces with slopes less than 10 deg to eliminate false detection due to the brightening effect of mass wasting, and also when the very bright south polar crater Shackleton is excluded from the analysis. We also find that south polar regions of permanent shadow that have been reported to be generally brighter at 1064 nm do not show anomalous reflectance when their annual maximum surface temperatures are too high to preserve water ice. This distinction is not observed at the North Pole. The reflectance excursion on surfaces with maximum temperatures below 110K is superimposed on a general trend of increasing reflectance with decreasing maximum temperature that is present throughout the polar regions in the north and south; we attribute this trend to a temperature or illumination-dependent space weathering effect (e.g. Hemingway et al. 2015). We also find a sudden increase in reflectance with decreasing temperature superimposed on the general trend at 200K and possibly at 300K. This may indicate the presence of other volatiles such as sulfur or organics. We identified and mapped surfaces with reflectances so high as to be unlikely to be part of an ice-free population. In this south we find a similar distribution found by Hayne et al. 2015 based on UV properties. In the north a cluster of pixels near that pole may represent a limited frost exposure.

Emily A Fisher↗

NASA NTRS1993

Searching for Lyman alpha emission from a possible Zel'dovich pancake

The detection of 2 x 10(exp 14) solar mass of neutral hydrogen at a redshift of 3.397 has been reported. Top-down theories of structure formation predict such a mass of hydrogen collapsing to form a protocluster of galaxies. We sought to observe this object in Lyman-alpha, which could be produced through ionization by the metagalactic ionizing radiation field or through internal ionization processes. On 29 Apr. 1992, the region of the reported HI emission for 1800 seconds with the 1.3 meter McGraw-Hill reflector at Michigan-Dartmouth-M.I.T. Observatory was observed. Because the H1 emission reported has a transverse scale of 300 sec, a 1/3.06 reducing camera and a Thomson CCD were used to obtain a field of view of about 600 sec by 840 sec. A filter 88 A wide, centered at 5354 A was used; Lyman-alpha emission at z = 3.4 is redshifted to 5347 A. In order to avoid saturating the CCD with a bright star in the field, nine 200 second exposures were taken. The combination of these images shows no obvious extended Lyman-alpha emission at a level of about 28 magnitudes per square arcsecond. The field observed also shows a distant cluster of galaxies. In order to determine if the cluster could in any way be associated with the cloud of neutral hydrogen at z = 3.4, we sought to estimate its redshift from the size and magnitude of the galaxies and of the cluster as a whole. Omega = 1 and H(sub O) = 50 km s(sup -1) Mpc(sup -1) were adopted; our redshift estimates range from z = 0.2 to z = 0.6. The cluster is clearly not associated with the HI cloud at z = 3.4.

Cox, Caroline↗

DOE OSTI2022

Performance Before and After Irradiation of Pixelated 3D Silicon Sensors for the HL-LHC CMS Tracker

The Large Hadron Collider (LHC) particle accelerator at European Center for Nuclear Research (CERN) will be shut down starting in 2026 to achieve the High Luminosity Large Hadron Collider (HL-LHC) upgrade. The upgrade will allow for higher fluences, in order to increase the probability of detecting increasingly rare particles, and to obtain higher precision measurements of known particles. To accommodate the new accelerator conditions, many aspects of the Compact Muon Solenoid (CMS) detector will be upgraded; of particular interest for this thesis are the silicon pixel detectors located in the inner tracker. These will be replaced and upgraded to accommodate the higher fluences of the HL-LHC upgrade, as well as to replace existing sensors which have sustained radiation damage. In order for the new sensors to operate under high luminosity conditions, they must be increasingly radiation hard, and in order to detect rare particles, they must be increasingly more precise. The performance of one Centro Nacional de Microelectronica (CNM) 3D silicon sensor before and after undergoing irradiation at fluences similar to those which will be observed at the HL-LHC was investigated to determine radiation hardness and precision. Data was collected at Fermi National Laboratory (Fermilab), in the Fermi National Laboratory Test Beam Facility (FTBF) silicon tracker telescope, which can be used to determine the number of particles, and tracks made by high energy protons passing through. The sensor was also irradiated at Fermilab in the Irradiation Test Area (ITA). Prior to data collection a tuning procedure is carried out to determine ideal bias voltage operating conditions, mask noisy and dead pixels, adjust to the ideal threshold, and map sensor gain. Data is then collected at the FTBF, where the sensor is installed in the center of the FTBF silicon telescope. Variables, including angle and bias voltage, are varied throughout data collection. Data is then processed using an alignment software to determine the exact telescope geometry, along with the tracks which were observed passing through the sensor and telescope. Sensor performance was found to be comparable before and after irradiation, with irradiated results showing slightly lower efficiencies and cluster sizes. Position resolution is comparable both before and after irradiation, and similar distributions of cluster shape are observed. After irradiation, the sensor shows increasing collected charge with bias, an indication of increased width of the depletion region. Peak charge pre-irradiation is higher than post-irradiation peak charge, indicating the irradiated results are not taken under fully-depleted conditions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1994

Ultraviolet observations of galaxies with the FAUST experiment

We have used the set of point sources detected by the Far Ultraviolet Space Telescope (FAUST) instrument to identify galaxies and study the total galaxy flux in a 250 A wide band peaking at 1650 A. A sample of 144 galaxies has been obtained after cross-reference with the RC3 catalog, elimination of objects confused with stars and various corrections for the photometry. The UV-B color dispersion is found to increase while the galaxies get redder from late to early types. The irregular galaxies appear on average redder and the Sbc galaxies bluer than indicated by the spectral energy distributions currently used for the calculations of K-corrections. Various arguments lead us to make the assumption of a constant dust extinction within each galaxy. The UV flux per unit area decreases on average from late to early type spirals. We find a weak correlation between the UV and far infra-red emission while the infra-red to UV flux ratio gets lower when galaxies get bluer (as measured by the UV to B flux ratio). The UV flux per unit area correlates with the HI gas surface density and the total gas surface density when this quantity is available. The correlation with the molecular gas alone is weak. In the Virgo cluster, the UV flux per unit area does not decrease in direct proportion to the HI deficiency. Galaxy counts per square degree and per magnitude interval have been obtained at high-galactic latitudes. Combined with data at fainter magnitudes, they show a variation as a function of magnitude with a near-euclidean slope over a range of 8 magnitudes.

Deharveng, J.-M.↗

DOE OSTI2021

A simplified framework for prediction of sensor network coverage in real-time structural health monitoring of plate-like structures

In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method.

Engineering↗

NASA NTRS2006

Satellite Detection of Ebola River Hemorrhagic Fever Epidemics Trigger Events

Ebola hemorrhagic fever, named after the Ebola River in Central Africa, first appeared in June 1976, during an outbreak in Nzara and Maridi, Sudan. In September 1976, a separate outbreak was recognized in Yambuku, Democratic Republic of the Congo (DRC). One fatal case was identified in Tandala, DRC, in June 1977, followed by another outbreak in Nzara, Sudan, in July 1979. Ebola hemorrhagic fever outbreaks results in a very high mortality of patients who contract the disease: from 50 to 80% of infected people perish from this highly virulent disease. Death is gruesome, with those afflicted bleeding to death from massive hemorrhaging of organs and capillaries. The disease was not identified again until the end of 1994, when three outbreaks occurred almost simultaneously in Africa. In October, an outbreak was identified in a chimpanzee community studied by primatologists in Tal, Cote d'lvoire, with one human infection. The following month, multiple cases were reported in northeast Gabon in the gold panning camps of Mekouka, Andock, and Minkebe. Later that same month, the putative index case of the 1995 Kikwit, DRC, outbreak was exposed through an unknown mechanism while working in a charcoal pit. In Gabon, two additional outbreaks were reported in February and JuIy,1996, respectively, in Mayibout II, a village 40 km south of the original outbreak in the gold panning camps, and a logging camp between Ovan and Koumameyong, near Booue. The largest Ebola hemorrhagic fever epidemic occurred in Gulu District, Uganda from August 2000 to January 2001. In December 2001, Ebola reappeared in the Ogooue-lvindo Province, Gabon with extension into Mbomo District, The Republic of the Congo lasting until July 2002. Since 2002 there have been several outbreaks of Ebola hemorrhagic fever in Gabon and adjacent areas of Congo. Of interest is the seasonal context and occasional temporal clustering of Ebola hemorrhagic fever outbreaks. Near simultaneous appearances of Ebola epidemics in Nzara, Sudan and Yambuku, DRC in 1976 occurred within two months of each other in two geographic locations separated by hundreds of kilometers involving two separate viral strains (Sudan and Zaire EBO strains). The outbreaks of Tal, Cote d'lvoire; Mekouka, Gabon; and Kikwit, DRC in late 1994 also occurred within months of each other in three different geographic regions involving two different viral strains (Cote d'lvoire and Zaire EBO strains). Fifteen years passed between the 1976-9 and 1994-6 temporal clusters of Ebola cases without identification of additional cases.

Tucker, Compton J.↗

NASA NTRS2021

Satellite Remote Sensing to Assess Cyanobacterial Bloom Frequency Across the United States at Multiple Spatial Scales

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Imager for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

remote sensing↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRS2001

Development of Low-Noise High Value Chromium Silicide Resistors for Cryogenic Detector Applications

Extremely high sensitivity detectors, such as silicon bolometers are required in many NASA missions for detection of photons from the x-ray to the far infrared regions. Typically, these detectors are cooled to well below the liquid helium (LHe) temperature (4.2 K) to achieve the maximum detection performance. As photoconductors, they are generally operated with a load resistor and a pre-set bias voltage, which is then coupled to the input gate of a source-follower Field Effect Transistor (FET) circuit. It is imperative that the detector system signal to noise performance be limited by the noise of the detector and not by the noise of the external components. The load resistor value is selected to optimize the detector performance. These two criteria tend to be contradictory in that these detectors require load resistors in the hundreds of megaohms, which leads to a higher Johnson noise. Additionally, the physical size of the resistor must be small for device integration as required by such missions as the NASA High Resolution Airborne Wide-Band Camera (HAWC) instrument and the Submillimeter High Angular Resolution Camera (SHARC) for the Caltech Submillimeter Observatory (CSO). We have designed, fabricated and characterized thin film resistors using a CrSi/TiW/Al metal system on optical quality quartz substrates. The resistor values range from 100 megaohms to over 650 megaohms and are Johnson noise limited at LHe temperatures. The resistor film is sputtered with a sheet resistance ranging from 300 ohms to 1600 ohms and the processing sequence developed for these devices allows for chemically fine tuning the sheet resistance in-situ. The wafer fabrication process was of sufficiently high yield (>80%) providing clusters of good resistors for integrated multiple detector channels, a very important feature in the assembly of these two instruments.

Jhabvala, Murzy↗

DOE OSTI2023

Performance portable ice-sheet modeling with MALI

High-resolution simulations of polar ice sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution; therefore, it has become essential to ensure performance on these platforms. Many of today’s supercomputers contain a diverse set of computing architectures and require specific programming interfaces in order to obtain optimal efficiency. In an effort to avoid architecture-specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MPAS-Albany Land Ice (MALI) uses high-level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this article, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights not only the performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26 and 1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We perform a weak scalability study and show that simulations on GPU-based machines perform 1.24–1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly, which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.6% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of 2 years of development.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Spatial orientation of semicircular canals and afferent sensitivity vectors in pigeons

Rotational head motion in vertebrates is detected by the semicircular canal system, whose innervating primary afferent fibers carry information about movement in specific head planes. The semicircular canals have been qualitatively examined over a number of years, and the canal planes have been quantitatively characterized in several animal species. The present study first determined the geometric relationship between individual semicircular canals and between the canals and the stereotactic head planes in pigeons. Stereotactic measurements of multiple points along the circumference of the bony canals were taken, and the measured points fitted with a three-dimensional planar surface. Direction normals to the plane's surface were calculated and used to define angles between semicircular canal pairs. Because of the unusual shape of the anterior semicircular canals in pigeons, two planes, a major and a minor, were fitted to the canal's course. Calculated angle values for all canals indicated that the horizontal and posterior semicircular canals are nearly orthogonal, but the anterior canals have substantial deviations from orthogonality with other canal planes. Next, the responses of the afferent fibers that innervate each of the semicircular canals to 0.5 Hz sinusoidal rotation about an earth-vertical axis were obtained. The head orientation relative to the rotation axis was systematically varied so that directions of maximum sensitivity for each canal afferent could be determined. These sensitivity vectors were then compared with the canal plane direction normals. The afferents that innervated specific semicircular canals formed homogeneous clusters of sensitivity vectors in different head planes. The horizontal and posterior afferents had average sensitivity vectors that were largely co-incident with the innervated canal plane direction normals. Anterior canal afferents, however, appeared to synthesize contributions from the major and minor plane components of the bony canal structure to produce a resultant sensitivity vector that was positioned between the canal planes. Calculated angles between the average canal afferent sensitivity vectors revealed that direction orthogonality is preserved at the afferent signal level, even though deviations from canal plane orthogonality exist.

NASA Discipline Neuroscience↗

DOE OSTI2020

KELT-25 b and KELT-26 b: A Hot Jupiter and a Substellar Companion Transiting Young A Stars Observed by TESS

We present the discoveries of KELT-25 b (TIC 65412605, TOI-626.01) and KELT-26 b (TIC 160708862, TOI-1337.01), two transiting companions orbiting relatively bright, early A stars. The transit signals were initially detected by the KELT survey and subsequently confirmed by Transiting Exoplanet Survey Satellite (TESS) photometry. KELT-25 b is on a 4.40 day orbit around the V = 9.66 star CD-24 5016 (T{sub eff}=8280{sub -180}{sup +440} K, M&lowast; = 2.18{sub -0.11}{sup +0.12} M{sub ⊙}), while KELT-26 b is on a 3.34 day orbit around the V = 9.95 star HD 134004 (T{sub eff} = 8640{sub -240}{sup +500} K, M&lowast; = 1.93{sub -0.16}{sup +0.14} M{sub ⊙}), which is likely an Am star. We have confirmed the substellar nature of both companions through detailed characterization of each system using ground-based and TESS photometry, radial velocity measurements, Doppler tomography, and high-resolution imaging. For KELT-25, we determine a companion radius of R{sub P} = 1.64{sub -0.043}{sup +0.039} R{sub J} and a 3σ upper limit on the companion’s mass of ~64 M{sub J}. For KELT-26 b, we infer a planetary mass and radius of M{sub P} = 1.41{sub -0.51}{sup +0.43} M{sub J} and R{sub P} = 1.94{sub -0.058}{sup +0.060} R{sub J}. From Doppler tomographic observations, we find KELT-26 b to reside in a highly misaligned orbit. This conclusion is weakly corroborated by a subtle asymmetry in the transit light curve from the TESS data. KELT-25 b appears to be in a well-aligned, prograde orbit, and the system is likely a member of the cluster Theia 449.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual seismic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. Here, we present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation-Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid-search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.

58 GEOSCIENCES↗

NASA NTRS1992

Ultraviolet observations of clusters of Wolf-Rayet stars in the SBm3 galaxy NGC 4214 and Ultraviolet and optical observations of LINER's

The purpose of the grant was to obtain and analyze IUE (UV) and ground-based (optical) spectra of the central bar of NGC 4214, which contains several bright H II regions, in order to further explore the properties of the Wolf-Rayet stars in this galaxy. Several spatially distinct regions, with widely different equivalent widths of optical Wolf-Rayet lines, could be sampled by the large IUE entrance aperture. By using newly developed extraction techniques, the spectra of these H II regions could be isolated, and differences in their stellar populations would be systematically studied. Data were obtained with IUE in late February and early March, 1992. Some of the shifts were successful, but a few were not -- apparently the blind offset from the nearby star did not work equally well in all cases. Thus, the signal-to-noise ratio is somewhat lower than we had hoped. This necessitated a more careful extraction of the spectra of individual H II regions from the two-dimensional spectra. (A program that models the point spread function in the spatial direction was used to deblend the distinct H II regions.) The IUE data are currently being analyzed in conjunction with ground-based optical spectra. There appear to be obvious variations in the stellar population over angular scales of only a few arc seconds. The second part of the research performed under this grant was a continuation of a project that uses IUE (UV) and ground-based (optical) spectra to infer the physical conditions in Low-Ionization Nuclear Emission-Line Regions (LINER's). We have obtained spectra of a few key objects that cover a representative range in LINER continuum and emission-line properties. The overall goals are to (1) separate the emission into spatially distinct components, (2) establish whether the observed nuclear ultraviolet continua indicate sufficient photoionizing fluxes to account for the emission lines, (3) determine whether the nuclear emission can be explained by hot stars alone, (4) detect and measure the strengths of UV emission lines, and (5) search for systematic differences in the UV spectra of LINER's whose other properties differ in some respects.

Filippenko, Alexei V.↗

DOE OSTI2021

Stability of Neptune’s Distant Resonances in the Presence of Planet Nine

Trans-Neptunian Objects (TNOs) in the scattered disk with 50 ≲ a ≲ 100 au are thought to cluster near Neptune’s n:1 resonances (e.g., 3:1, 4:1, and so on). While these objects spend lengthy periods of time at large heliocentric distances, if their perihelia remain less than around 40 au, their dynamical evolution is still largely coupled to Neptune’s. Conversely, around a dozen extreme TNOs with a ≳ 250 au and detached perihelia seem to exist in a regime where they are too distant to be affected by the giant planets and too close for their dynamics to be governed by external forces. Recent work suggests that the apparent alignment of these orbits in physical space is a signature of gravitational shepherding by a distant massive planet. In this paper, we investigate the evolution of TNOs in each of Neptune’s n:1 resonances between the 3:1 and 14:1. We conclude that both resonant and nonresonant objects beyond the 12:1 near ∼157 au are removed rather efficiently via perturbations from the hypothetical Planet Nine. Additionally, we uncover a population of simulated TNOs with a ≲ 100 au, 40 ≲ q ≲ 45 au, and low inclinations that experience episodes of resonant interactions with both Neptune and Planet Nine. Finally, we simulate the evolution of observed objects with a > 100 au and identify several TNOs that are potentially locked in n:1 resonances with Neptune, including the most distant known resonant candidates, 2014 JW{sub 80} and 2014 OS{sub 394},which appear to be in the 10:1 and 11:1 resonances, respectively. Our results suggest that the detection of similar remote objects might provide a useful constraint on hypotheses invoking the existence of additional distant planets.

79 ASTRONOMY AND ASTROPHYSICS↗