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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Stratospheric dust collections: Valuable resources for space and atmospheric scientists

The stratospheric collection at the Johnson Space Center Curatorial Facility offers a unique opportunity to study well-documented, individual particles (or groups of particles) from a wide variety of sources. The nature of the collection and curation process, as well as the timeliness of some sampling periods, ensures that all data obtained from stratospheric particles is a valuable resource for scientists from a wide range of disciplines. A few samples of the uses of these stratospheric dust collections are outlined. An understanding of global parameters at a particular point in time in the stratosphere can be obtained from a study of complete collection surfaces. For example, an accurate assessment of particle concentration over a wide range of sizes was experimentally determined for the stratospheric cloud formed one month after the eruption of El Chichon. Additional studies on the El Chichon cloud over a six-month period showed that volcanic ash settles out of the stratosphere at a rate determined primarily by particle shape and density. Another study during a volcanically quiescent period has shown that total particle number density during the summer of 1981 was approx. 2.7 x 10(-1) cm(-3), for particles 1 micron diameter. However, 95% of these particles were 5 micrometers diameter. With the above classification scheme, an estimate of micrometeorite number density at 20km altitude can also be made. Continuation of these types of studies, for shorter collection periods at regular intervals, can provide important experimental data on the contributions of orbital debris, rocket firings and transient events on the total stratospheric particle budget.

Mackinnon, I. D. R.↗
DOE OSTI2025

deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.

Citizen science↗
DOE OSTI2026

Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging

Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events to store impose strict latency and accuracy constraints. While transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, their quadratic self-attention cost makes inference restrictive on trigger budget. Existing efficient variants reduce the computational cost, but hinder the classification performance. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT-JeT), which combines two mechanisms: a physics-inspired geometric message-passing module that encodes local detector-plane structure, and a hierarchical patch-based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch-token communication. Within a restricted budget, PHAT-JeT achieves state-of-the-art accuracy and background rejection among all resource-constrained jet tagging models on four benchmarks (\textsc{hls4ml}, JetClass, Top Tagging, and Quark--Gluon). Our code is available at https://github.com/aaronw5/PHAT-JeT.

Wang, Aaron [Illinois U., Chicago] (ORCID:00000003↗
DOE OSTI2022

SCAT uncovers ATLAS’s first tidal disruption event ATLAS18mlw: a faint and fast TDE in a quiescent Balmer strong Galaxy

ABSTRACT We present the discovery that ATLAS18mlw was a tidal disruption event (TDE) in the galaxy WISEA J073544.83+663717.3, at a luminosity distance of 334 Mpc. Initially discovered by the Asteroid Terrestrial Impact Last Alert System (ATLAS) on 2018 March 17.3, the TDE nature of the transient was uncovered only recently with the re-reduction of a SuperNova Integral Field Spectrograph (SNIFS) spectrum. This spectrum, taken by the Spectral Classification of Astronomical Transients (SCAT) survey, shows a strong blue continuum and a broad H α emission line. Here, we present roughly 6 yr of optical survey photometry beginning before the TDE to constrain active galactic nucleus activity, optical spectroscopy of the transient, and a detailed study of the host galaxy properties through analysis of archival photometry and a host spectrum. ATLAS18mlw was detected in ground-based light curves for roughly 2 months. From a blackbody fit to the transient spectrum and bolometric correction of the optical light curve, we conclude that ATLAS18mlw is best explained by a low-luminosity TDE with a peak luminosity of log(L [erg s−1]) = 43.5 ± 0.2. The TDE classification is further supported by the quiescent Balmer strong nature of the host galaxy. We also calculated the TDE decline rate from the bolometric light curve and find ΔL40 = −0.7 ± 0.2 dex, making ATLAS18mlw a member of the growing class of ‘faint and fast’ TDEs with low peak luminosities and fast decline rates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in ν μ CC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

End-to-end jet classification of boosted top quarks with the CMS open data

Here we describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique uses low-level detector representation of high-energy collision event as inputs to deep learning algorithms. In this study, we use low-level detector information from the simulated Compact Muon Solenoid (CMS) open data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an area under the receiver operator curve (AUC) score of 0.975 ± 0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to 0.9824 ± 0.0013, serving as the first performance benchmark for these CMS open data samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1994

The effect of repeating gamma-ray bursts on V/V(sub max)

I consider the effect of repeating burst sources on the the mean value of V/V(sub max) statistic. I find that if the burst sources are distributed homogeneously in a d-dimensional space, the mean value of V/V(sub max) converges to d/(d+3) as long as the luminosity function is dependent of position. Choosing the brightest event from a cluster of k events shifts the luminosity function to higher luminosities, but if the original luminosity function is position independent, it remains so. Therefore the treatment of repeating events, if applied consistently, will not affect the effectiveness the mean value of V/V(sub max) as a test of burst homogeneity. The calculation of the mean value of V/V(sub max) for apparent repeating and non-repeating source populations will be raised by the incorrect classification of faint bursts. In conclusion, the current practice of calculating the mean value of V/V(sub max) using all bursts, even apparent repeaters, and treating multispike bursts as single bursts, is valid and consistent.

Band, David L.↗
DOE OSTI2021

Swift /UVOT follow-up of gravitational wave alerts in the O3 era

ABSTRACT In this paper, we report on the observational performance of the Swift Ultra-violet/Optical Telescope (UVOT) in response to the gravitational wave (GW) alerts announced by the Advanced Laser Interferometer Gravitational Wave Observatory and the Advanced Virgo detector during the O3 period. We provide the observational strategy for follow-up of GW alerts and provide an overview of the processing and analysis of candidate optical/UV sources. For the O3 period, we also provide a statistical overview and report on serendipitous sources discovered by Swift/UVOT. Swift followed 18 GW candidate alerts, with UVOT observing a total of 424 deg2. We found 27 sources that changed in magnitude at the 3σ level compared with archival u- or g-band catalogued values. Swift/UVOT also followed up a further 13 sources reported by other facilities during the O3 period. Using catalogue information, we divided these 40 sources into five initial classifications: 11 candidate active galactic nuclei (AGNs)/quasars, three cataclysmic variables (CVs), nine supernovae, 11 unidentified sources that had archival photometry, and six uncatalogued sources for which no archival photometry was available. We have no strong evidence to identify any of these transients as counterparts to the GW events. The 17 unclassified sources are likely a mix of AGN and a class of fast-evolving transient, and one source may be a CV.

Oates, S. R.↗
NASA NTRS2021

Swift/UVOT Follow-Up of Gravitational Wave Alerts in the O3 Era

In this paper, we report on the observational performance of the Swift Ultra-violet/Optical Telescope (UVOT) in response to the gravitational wave (GW) alerts announced by the Advanced Laser Interferometer Gravitational Wave Observatory and the Advanced Virgo detector during the O3 period. We provide the observational strategy for follow-up of GW alerts and provide an overview of the processing and analysis of candidate optical/UV sources. For the O3 period, we also provide a statistical overview and report on serendipitous sources discovered by Swift/UVOT. Swift followed 18 GW candidate alerts, with UVOT observing a total of 424 deg2. We found 27 sources that changed in magnitude at the 3σ level compared with archival u- or g-band catalogued values. Swift/UVOT also followed up a further 13 sources reported by other facilities during the O3 period. Using catalogue information, we divided these 40 sources into five initial classifications: 11 candidate active galactic nuclei (AGNs)/quasars, three cataclysmic variables (CVs), nine supernovae, 11 unidentified sources that had archival photometry, and six uncatalogued sources for which no archival photometry was available. We have no strong evidence to identify any of these transients as counterparts to the GW events. The 17 unclassified sources are likely a mix of AGN and a class of fast-evolving transient, and one source may be a CV.

gravitational waves↗
DOE OSTI2024

Photon classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π + π 0 and π - π 0 , were analyzed, requiring the reconstruction of diphoton pairs. Here, in this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π 0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π 0 's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Astrometric microlensing probes of the isolated neutron star population with Roman

Notoriously hard to detect and study, isolated neutron stars (NSs) might provide valuable answers to fundamental questions about stellar evolution and explosion physics. With the upcoming Roman Space Telescope, scheduled for launch in 2026, a new and powerful channel for their detection will become available: astrometric microlensing. We set out to create a realistic sample of simulated gravitational microlensing events as observed by Roman with the Galactic Bulge Time Domain Survey. We focus in particular on the population of NS lenses, which has until now been largely understudied. We used dedicated Galactic models tailored for application to microlensing by compact objects. In addition to populations of stars, white dwarfs, and black holes, we simulated four different NS populations with Maxwellian natal kick distributions: v = (150, 250, 350, 450) km/s. For each simulation, we applied projected Roman precision, cadence, and detectability criteria. We found that the parameter space log10 t E –log 10 θ E , which will be accessible to Roman observations, is efficient for the classification of stellar remnants. We found a feature in this space that is characteristic of NSs; using this feature, optimal samples of NS candidates can be constructed from Roman -like datasets. We describe the dependence of the observable parameter distributions on the assumed mean kick velocities. As the effects of natal kicks are very complex and mutually counteracting, we suggest that more detailed studies focused on the dynamics of NSs are needed in anticipation of Roman and future surveys. We estimate that Roman will observe approximately 11 000 microlensing events, including ~100 with NS lenses, whose photometric and astrometric signals are detectable; the event yield decreases by 38% when gap-filling low-cadence observations are not included. We make all simulated microlensing event datasets publicly available in preparation for Roman data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The phase space distance between collider events

How can one fully harness the power of physics encoded in relativistic N-body phase space? Topologically, phase space is isomorphic to the product space of a simplex and a hypersphere and can be equipped with explicit coordinates and a Riemannian metric. This natural structure that scaffolds the space on which all collider physics events live opens up new directions for machine learning applications and implementation. Here we present a detailed construction of the phase space manifold and its differential line element, identifying particle ordering prescriptions that ensure that the metric satisfies necessary properties. We apply the phase space metric to several binary classification tasks, including discrimination of high-multiplicity resonance decays or boosted hadronic decays of electroweak bosons from QCD processes, and demonstrate powerful performance on simulated data. Our work demonstrates the many benefits of promoting phase space from merely a background on which calculations take place to being geometrically entwined with a theory’s dynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use lowlevel detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves a ROC-AUC score of 0.975±0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier ROC-AUC score increases to 0.9824±0.0013, serving as the first performance benchmark for these CMS Open Data samples.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1982

Forest management applications of Landsat data in a geographic information system

The utility of land-cover data resulting from Landsat MSS classification can be greatly enhanced by use in combination with ancillary data. A demonstration forest management applications data base was constructed for Santa Cruz County, California, to demonstrate geographic information system applications of classified Landsat data. The data base contained detailed soils, digital terrain, land ownership, jurisdictional boundaries, fire events, and generalized land-use data, all registered to a UTM grid base. Applications models were developed from problems typical of fire management and reforestation planning.

Maw, K. D.↗
NASA NTRS2018

Interactive Multi-Instrument Database of Solar Flares

The fundamental motivation of the project is that the scientific output of solar research can be greatly enhanced by better exploitation of the existing solar/heliosphere space-data products jointly with ground-based observations. Our primary focus is on developing a specific innovative methodology based on recent advances in "big data" intelligent databases applied to the growing amount of high-spatial and multi-wavelength resolution, high-cadence data from NASA's missions and supporting ground-based observatories. Our flare database is not simply a manually searchable time-based catalog of events or list of web links pointing to data. It is a preprocessed metadata repository enabling fast search and automatic identification of all recorded flares sharing a specifiable set of characteristics, features, and parameters. The result is a new and unique database of solar flares and data search and classification tools for the Heliophysics community, enabling multi-instrument/multi-wavelength investigations of flare physics and supporting further development of flare-prediction methodologies.

Heliophysics↗
NASA NTRS1973

A summary of major NASA launchings, 1 October 1958 - 30 September 1973

This report was compiled from several different sources. Documents consulted included: Operations Summaries, post-launch Flash Flight Reports, Final Field Reports, Mission Operations Reports (both pre-launch and post-launch), and Satellite Situation Reports. Other major references were publications of the NASA Historical Office, such as: Aeronautics and Astronautics 1916-1960; Aeronautical and Astronautical Events of 1961-1962; Aeronautics and Astronautics (yearly editions since 1963); and NASA's Pocket Statistics (published monthly.) The report is divided into projects within broad mission categories, with each project being treated chronologically. Mission results have been categorized as: successful (S); unsuccessful (U); or, partially successful (P). These are arbitrary classifications, made after a comparison of objectives and actual results.

Source record↗
DOE OSTI2021

Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Tennant, C. D.↗
NASA NTRS1993

Identification of two classes of gamma-ray bursts

We have studied the duration distribution of the gamma-ray bursts of the first BATSE catalog. We find a bimodality in the distribution, which separates GRBs into two classes: short events (less than 2 s) and longer ones (more than 2 s). Both sets are distributed isotropically and inhomogeneously in the sky. We find that their durations are anticorrelated with their spectral hardness ratios: short GRBs are predominantly harder, and longer ones tend to be softer. Our results provide a first GRB classification scheme based on a combination of the GRB temporal and spectral properties.

Kouveliotou, Chryssa↗