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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Operation and performance of the ATLAS semiconductor tracker in LHC Run 2

The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules. During Run 2 (2015–2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb ₋1 to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector. Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2. It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%. Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Luminosity determination in $pp$ collisions at $\sqrt{s}=13$ TeV using the ATLAS detector at the LHC

The luminosity determination for the ATLAS detector at the LHC during Run 2 is presented, with pp collisions at a centre-of-mass energy $\sqrt{s}$ = 13 TeV. The absolute luminosity scale is determined using van der Meer beam separation scans during dedicated running periods in each year, and extrapolated to the physics data-taking regime using complementary measurements from several luminosity-sensitive detectors. The total uncertainties in the integrated luminosity for each individual year of data taking range from 0.9% to 1.1%, and are partially corre lated between years. After standard data-quality selections, the full Run 2 pp data sample corresponds to an integrated luminosity of 140.1 ± 1.2 fb -1 , i.e. an uncertainty of 0.83%. A dedicated sample of low-pileup data recorded in 2017– 2018 for precision Standard Model physics measurements is analysed separately, and has an integrated luminosity of 338.1 ± 3.1 pb -1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
DOE OSTI2022

The Dark Energy Survey Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5000 Square Degree Footprint

We report the combined results of eight searches for strong gravitational lens systems in the full 5000 square degrees of Dark Energy Survey (DES) observations. The observations accumulated by the end of the third observing season fully covered the DES footprint in five filters (grizY), with an i-band limiting magnitude (at 10σ) of 23.44. In four searches, a list of potential candidates was identified using a color and magnitude selection from the object catalogs created from the first three observing seasons. Three other searches were conducted at the locations of previously identified galaxy clusters. Cutout images of potential candidates were then visually scanned using an object viewer. An additional set of candidates came from a data-quality check of a subset of the color–coadd tiles created from the full DES six-season data set. A short list of the most promising strong-lens candidates was then numerically ranked according to whether or not we judged them to be bona fide strong gravitational lens systems. These searches discovered a diverse set of 247 strong-lens candidate systems, of which 81 are identified for the first time. We provide the coordinates, magnitudes, and photometric properties of the lens and source objects, and an estimate of the Einstein radius for 81 new systems and 166 previously reported systems. This catalog will be of use for selecting interesting systems for detailed follow up, studies of galaxy cluster and group mass profiles, as well as a training/validation set for automated strong-lens searches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1974

Design of the Mariner Jupiter/Saturn 1977 telemetry system

In 1977 NASA will launch two spacecraft to perform scientific investigations of the Jupiter and Saturn planetary systems. The science payload includes a total of ten instruments to support both the interplanetary-cruise and planetary-encounter phases. These will be the first launches of a new generation of Mariner-class spacecraft designed for outer-planet missions. The telemetry-system design for these missions was especially challenging because of extreme communication ranges (1.5 billion km), high data-rate requirements (up to 115.2 kbit/sec), and more stringent data-quality requirements than previous Mariner missions. The present paper discusses the evolution and design of the Mariner Jupiter/Saturn 1977 telemetry system and presents the performance anticipated therefrom.

Wood, G. E.↗
NASA NTRS1987

Obtaining spectral reflectance factor measurements of stressed forest vegetation

Techniques and instruments used to collect spectral reflectance data on Vermont spruce and fir stands subjected to stress (due to acid rain and ozone) are described and illustrated. The healthy and stressed sites selected are characterized; the data-quality requirements are indicated; and particular attention is given to (1) a helicopter-mounted instrument package (radiometer, spectrometer, cameras, and video camera) for canopy-level remote sensing of reflectance and (2) a portable hemispherical light source for use in taking laboratory reflectance measurements from branches or needles. Typical data are presented in graphs and briefly discussed.

Case, David W.↗
NASA NTRS2003

Measurement and Control of the Variability of Scanning Pressure Transducer Measurements

This paper describes the new wall pressure measurement system and data-quality monitoring software installed at 14x22 Ft subsonic tunnel at the NASA Langley Research Center. The monitoring software was developed to enable measurement and control of the variability of the reference pressures and approximately 400 tunnel wall pressure measurements. Variability of the system, based upon data acquired over a year of wind tunnel tests and calibrations, is presented. The level of variation of the wall pressure measurements is shown to be predictable.

Kuhl, David D.↗
NASA NTRS2009

MISR Aerosol Product Attributes and Statistical Comparisons with MODIS

In this paper, Multi-angle Imaging SpectroRadiometer (MISR) aerosol product attributes are described, including geometry and algorithm performance flags. Actual retrieval coverage is mapped and explained in detail using representative global monthly data. Statistical comparisons are made with coincident aerosol optical depth (AOD) and Angstrom exponent (ANG) retrieval results from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The relationship between these results and the ones previously obtained for MISR and MODIS individually, based on comparisons with coincident ground-truth observations, is established. For the data examined, MISR and MODIS each obtain successful aerosol retrievals about 15% of the time, and coincident MISR-MODIS aerosol retrievals are obtained for about 6%-7% of the total overlap region. Cloud avoidance, glint and oblique-Sun exclusions, and other algorithm physical limitations account for these results. For both MISR and MODIS, successful retrievals are obtained for over 75% of locations where attempts are made. Where coincident AOD retrievals are obtained over ocean, the MISR-MODIS correlation coefficient is about 0.9; over land, the correlation coefficient is about 0.7. Differences are traced to specific known algorithm issues or conditions. Over-ocean ANG comparisons yield a correlation of 0.67, showing consistency in distinguishing aerosol air masses dominated by coarse-mode versus fine-mode particles. Sampling considerations imply that care must be taken when assessing monthly global aerosol direct radiative forcing and AOD trends with these products, but they can be used directly for many other applications, such as regional AOD gradient and aerosol air mass type mapping and aerosol transport model validation. Users are urged to take seriously the published product data-quality statements.

Kahn, Ralph A.↗
NASA NTRS2012

QuakeSim 2.0

QuakeSim 2.0 improves understanding of earthquake processes by providing modeling tools and integrating model applications and various heterogeneous data sources within a Web services environment. QuakeSim is a multisource, synergistic, data-intensive environment for modeling the behavior of earthquake faults individually, and as part of complex interacting systems. Remotely sensed geodetic data products may be explored, compared with faults and landscape features, mined by pattern analysis applications, and integrated with models and pattern analysis applications in a rich Web-based and visualization environment. Integration of heterogeneous data products with pattern informatics tools enables efficient development of models. Federated database components and visualization tools allow rapid exploration of large datasets, while pattern informatics enables identification of subtle, but important, features in large data sets. QuakeSim is valuable for earthquake investigations and modeling in its current state, and also serves as a prototype and nucleus for broader systems under development. The framework provides access to physics-based simulation tools that model the earthquake cycle and related crustal deformation. Spaceborne GPS and Inter ferometric Synthetic Aperture (InSAR) data provide information on near-term crustal deformation, while paleoseismic geologic data provide longerterm information on earthquake fault processes. These data sources are integrated into QuakeSim's QuakeTables database system, and are accessible by users or various model applications. UAVSAR repeat pass interferometry data products are added to the QuakeTables database, and are available through a browseable map interface or Representational State Transfer (REST) interfaces. Model applications can retrieve data from Quake Tables, or from third-party GPS velocity data services; alternatively, users can manually input parameters into the models. Pattern analysis of GPS and seismicity data has proved useful for mid-term forecasting of earthquakes, and for detecting subtle changes in crustal deformation. The GPS time series analysis has also proved useful as a data-quality tool, enabling the discovery of station anomalies and data processing and distribution errors. Improved visualization tools enable more efficient data exploration and understanding. Tools provide flexibility to science users for exploring data in new ways through download links, but also facilitate standard, intuitive, and routine uses for science users and end users such as emergency responders.

Donnellan, Andrea↗
NASA NTRS2019

The volumetric rate of normal type Ia supernovae in the local Universe discovered by the Palomar Transient Factory

We present the volumetric rate of normal type Ia supernovae (SNe Ia) discovered by the Palomar Transient Factory (PTF). Using strict data-quality cuts, and considering only periods when the PTF maintained a regular cadence, PTF discovered 90 SNe Ia at z ≤ 0.09 in a well-controlled sample over three years of operation (2010–2012). We use this to calculate the volumetric rate of SN Ia events by comparing this sample to simulations of hundreds of millions of SN Ia light curves produced in statistically representative realizations of the PTF survey. This quantifies the recovery efficiency of each PTF SN Ia event, and thus the relative weighting of each event. From this, the volumetric SN Ia rate was found to be r(v) = 2.43 ± 0.29 (stat)(+0.33,−0.19)(sys) × 10^(−5) SNe per yr Mpc^(-3) h(3,70). This represents the most precise local measurement of the SNIa rate. We fit a simple SNIa delay-time distribution model, ∝ t^(−β) , to our PTF rate measurement combined with a literature sample of rate measurements from surveys at higher redshifts. We find β ∼ 1, consistent with a progenitor channel governed by the gravitational inspiral of binary white dwarfs.

C. Frohmaier↗
DOE OSTI2020

Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data

Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. Here, a synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response

controlled islanding↗