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At least 649 records · Page 36

Dark Photon Search at the Short-Baseline Near Detector

Neutrino physics has long been a key field in elementary particle physics, both enhancing our understanding of the Standard Model (SM) and raising new questions. Among these are the so-called "Short-Baseline Anomalies" observed by neutrino experiments, particularly the MiniBooNE experiment at Fermilab, which detected an excess of low-energy electron-like events. In recent years, beyond Standard Model (BSM) explanations have been proposed to address this anomaly, with a focus on neutrino beam-related processes. A novel interpretation involving a dark-sector explanation was recently suggested, introducing a vector portal that connects the SM and dark sectors through a new interaction mediated by a bosonic particle, the Dark Photon. This work investigates the production of dark-sector particles, specifically Dark Photons, in the Booster Neutrino Beam (BNB) at Fermilab and their potential detection at the Short-Baseline Near Detector (SBND). The BNB produces mesons which decay into Dark Photons, detectable via their decay into electron-positron pairs at SBND. By exploiting the temporal structure of the neutrino beam, we propose a method to isolate Dark Photon signals from neutrino backgrounds using time-delayed event detection. In this thesis, the sensitivity of SBND to Dark Photons is assessed using a three-year exposure, demonstrating that SBND has the potential to significantly improve current experimental constraints on Dark Photons. This analysis provides a promising avenue for future dark sector searches in neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA Tech Briefs, December 2007

Topics include: Ka-Band TWT High-Efficiency Power Combiner for High-Rate Data Transmission; Reusable, Extensible High-Level Data-Distribution Concept; Processing Satellite Imagery To Detect Waste Tire Piles; Monitoring by Use of Clusters of Sensor-Data Vectors; Circuit and Method for Communication Over DC Power Line; Switched Band-Pass Filters for Adaptive Transceivers; Noncoherent DTTLs for Symbol Synchronization; High-Voltage Power Supply With Fast Rise and Fall Times; Waveguide Calibrator for Multi-Element Probe Calibration; Four-Way Ka-Band Power Combiner; Loss-of-Control-Inhibitor Systems for Aircraft; Improved Underwater Excitation-Emission Matrix Fluorometer; Metrology Camera System Using Two-Color Interferometry; Design and Fabrication of High-Efficiency CMOS/CCD Imagers; Foam Core Shielding for Spacecraft CHEM-Based Self-Deploying Planetary Storage Tanks Sequestration of Single-Walled Carbon Nanotubes in a Polymer PPC750 Performance Monitor Application-Program-Installer Builder Using Visual Odometry to Estimate Position and Attitude Design and Data Management System Simple, Script-Based Science Processing Archive Automated Rocket Propulsion Test Management Online Remote Sensing Interface Fusing Image Data for Calculating Position of an Object Implementation of a Point Algorithm for Real-Time Convex Optimization Handling Input and Output for COAMPS Modeling and Grid Generation of Iced Airfoils Automated Identification of Nucleotide Sequences Balloon Design Software Rocket Science 101 Interactive Educational Program Creep Forming of Carbon-Reinforced Ceramic-Matrix Composites Dog-Bone Horns for Piezoelectric Ultrasonic/Sonic Actuators Benchtop Detection of Proteins Recombinant Collagenlike Proteins Remote Sensing of Parasitic Nematodes in Plants Direct Coupling From WGM Resonator Disks to Photodetectors Using Digital Radiography To Image Liquid Nitrogen in Voids Multiple-Parameter, Low-False-Alarm Fire-Detection Systems Mosaic-Detector-Based Fluorescence Spectral Imager Plasmoid Thruster for High Specific-Impulse Propulsion Analysis Method for Quantifying Vehicle Design Goals Improved Tracking of Targets by Cameras on a Mars Rover Sample Caching Subsystem Multistage Passive Cooler for Spaceborne Instruments GVIPS Models and Software Stowable Energy-Absorbing Rocker-Bogie Suspensions

Source record↗

Laboratory Studies of Particle Cementation and PV Module Soiling

Monitoring of photovoltaic (PV) systems can maintain efficient operations. However, extensive monitoring of large quantities of data can be a cumbersome process. The present work introduces a simple, inexpensive, yet effective data monitoring strategy for detecting faults and determining lost revenues automatically. This was achieved through the deployment of Raspberry Pi (RPI) device at a PV system's combiner box. The RPI was programmed to collect PV data through Modbus communications, and store the data locally in a MySQL database. Then, using a Gaussian Process Regression algorithm the RPI device was able to accurately estimate string level current, voltage, and power values. The device could also detect system faults using a Support Vector Novelty Detection algorithm. Finally, the RPI was programmed to output the potential lost revenue caused by the abnormal condition. The system analytics information was then displayed on a user interface. The interface could be accessed by operations personal to direct maintenance activity so that critical issues can be solved quickly.

14 SOLAR ENERGY↗

Three-dimensional organization of otolith-ocular reflexes in rhesus monkeys. I. Linear acceleration responses during off-vertical axis rotation

1. The dynamic properties of otolith-ocular reflexes elicited by sinusoidal linear acceleration along the three cardinal head axes were studied during off-vertical axis rotations in rhesus monkeys. As the head rotates in space at constant velocity about an off-vertical axis, otolith-ocular reflexes are elicited in response to the sinusoidally varying linear acceleration (gravity) components along the interaural, nasooccipital, or vertical head axis. Because the frequency of these sinusoidal stimuli is proportional to the velocity of rotation, rotation at low and moderately fast speeds allows the study of the mid-and low-frequency dynamics of these otolith-ocular reflexes. 2. Animals were rotated in complete darkness in the yaw, pitch, and roll planes at velocities ranging between 7.4 and 184 degrees/s. Accordingly, otolith-ocular reflexes (manifested as sinusoidal modulations in eye position and/or slow-phase eye velocity) were quantitatively studied for stimulus frequencies ranging between 0.02 and 0.51 Hz. During yaw and roll rotation, torsional, vertical, and horizontal slow-phase eye velocity was sinusoidally modulated as a function of head position. The amplitudes of these responses were symmetric for rotations in opposite directions. In contrast, mainly vertical slow-phase eye velocity was modulated during pitch rotation. This modulation was asymmetric for rotations in opposite direction. 3. Each of these response components in a given rotation plane could be associated with an otolith-ocular response vector whose sensitivity, temporal phase, and spatial orientation were estimated on the basis of the amplitude and phase of sinusoidal modulations during both directions of rotation. Based on this analysis, which was performed either for slow-phase eye velocity alone or for total eye excursion (including both slow and fast eye movements), two distinct response patterns were observed: 1) response vectors with pronounced dynamics and spatial/temporal properties that could be characterized as the low-frequency range of "translational" otolith-ocular reflexes; and 2) response vectors associated with an eye position modulation in phase with head position ("tilt" otolith-ocular reflexes). 4. The responses associated with two otolith-ocular vectors with pronounced dynamics consisted of horizontal eye movements evoked as a function of gravity along the interaural axis and vertical eye movements elicited as a function of gravity along the vertical head axis. Both responses were characterized by a slow-phase eye velocity sensitivity that increased three- to five-fold and large phase changes of approximately 100-180 degrees between 0.02 and 0.51 Hz. These dynamic properties could suggest nontraditional temporal processing in utriculoocular and sacculoocular pathways, possibly involving spatiotemporal otolith-ocular interactions. 5. The two otolith-ocular vectors associated with eye position responses in phase with head position (tilt otolith-ocular reflexes) consisted of torsional eye movements in response to gravity along the interaural axis, and vertical eye movements in response to gravity along the nasooccipital head axis. These otolith-ocular responses did not result from an otolithic effect on slow eye movements alone. Particularly at high frequencies (i.e., high speed rotations), saccades were responsible for most of the modulation of torsional and vertical eye position, which was relatively large (on average +/- 8-10 degrees/g) and remained independent of frequency. Such reflex dynamics can be simulated by a direct coupling of primary otolith afferent inputs to the oculomotor plant. (ABSTRACT TRUNCATED).

NASA Discipline Neuroscience↗

Vectorized Rebinning Algorithm for Fast Data Down-Sampling

A vectorized rebinning (down-sampling) algorithm, applicable to N-dimensional data sets, has been developed that offers a significant reduction in computer run time when compared to conventional rebinning algorithms. For clarity, a two-dimensional version of the algorithm is discussed to illustrate some specific details of the algorithm content, and using the language of image processing, 2D data will be referred to as "images," and each value in an image as a "pixel." The new approach is fully vectorized, i.e., the down-sampling procedure is done as a single step over all image rows, and then as a single step over all image columns. Data rebinning (or down-sampling) is a procedure that uses a discretely sampled N-dimensional data set to create a representation of the same data, but with fewer discrete samples. Such data down-sampling is fundamental to digital signal processing, e.g., for data compression applications.

Dean, Bruce↗

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗

Kernelized approaches to streaming compression of scientific data

In this paper three algorithms are developed for the streaming compression of scientific data. The algorithms presented are reliant on the theory of vector-valued reproducing kernel Hilbert spaces and operator valued kernel. Further, the scientific data is modeled as a snapshot of time dependent vector field F(x, t) over a manifold M and the recovery of the data is framed as a learning problem. These processes are then appropriately modified and ana lyzed for the streaming scenario in which data is generated without the ability to revisit past entries.

97 MATHEMATICS AND COMPUTING↗

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗

Artificial Intelligence for Digital Security and Protections

Proper functioning of nuclear power plants relies on a mix of well-regulated human and machine-driven workflows. This regulation supports nuclear safety through a series of processes and many of the tasks that support these processes have a repetitive nature that make artificial intelligence (AI) informed by machine learning (ML) a potential aid in a variety of tasks. AI is being evaluated for activities that include inspections, fuel processing, monitoring, and other activities. The introduction of any new technology presents a potential new attack vector. In the case of AI/ML, there are many attacks that have already been discovered and over time the attacks can be expected to follow the growth pattern observed in cyber security. While future planning is necessary, current efforts need to be established now to predict the threat emergence over the next year 10 years and mitigate potential threats. Based on these observations, AI/ML will need to become trustworthy, which corresponds to techniques and procedures that emphasize AI explainability along with resilience techniques to data, algorithms, models, and systems. This kind of system robustness is the foundation for defenses against AI/ML-specific attacks. Attempting to look forward and take a broad view of capabilities provides input to research roadmaps and the ability to distill vulnerabilities into specific use cases may provide greater assistance in understanding the technology benefits while introducing new risks. The impact of current and future AI in three areas—capabilities, challenges, and recovery strategies—represents an initial attempt at balancing both.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Simulation of complex three-dimensional flows

The concept of splitting is used extensively to simulate complex three dimensional flows on modern computer architectures. Used in all aspects, from initial grid generation to the determination of the final converged solution, splitting is used to enhance code vectorization, to permit solution driven grid adaption and grid enrichment, to permit the use of concurrent processing, and to enhance data flow through hierarchal memory systems. Three examples are used to illustrate these concepts to complex three dimensional flow fields: (1) interactive flow over a bump; (2) supersonic flow past a blunt based conical afterbody at incidence to a free stream and containing a centered propulsive jet; and (3) supersonic flow past a sharp leading edge delta wing at incidence to the free stream.

Diewert, G. S.↗

Polynomial approximation of functions of matrices and its application to the solution of a general system of linear equations

During the process of solving a mathematical model numerically, there is often a need to operate on a vector v by an operator which can be expressed as f(A) while A is NxN matrix (ex: exp(A), sin(A), A sup -1). Except for very simple matrices, it is impractical to construct the matrix f(A) explicitly. Usually an approximation to it is used. In the present research, an algorithm is developed which uses a polynomial approximation to f(A). It is reduced to a problem of approximating f(z) by a polynomial in z while z belongs to the domain D in the complex plane which includes all the eigenvalues of A. This problem of approximation is approached by interpolating the function f(z) in a certain set of points which is known to have some maximal properties. The approximation thus achieved is almost best. Implementing the algorithm to some practical problem is described. Since a solution to a linear system Ax = b is x= A sup -1 b, an iterative solution to it can be regarded as a polynomial approximation to f(A) = A sup -1. Implementing the algorithm in this case is also described.

Tal-Ezer, Hillel↗

Simplified Digital Subband Coders And Decoders

Simplified digital subband coders and decoders developed for use in converting digitized samples of source signals into compressed and encoded forms that maintain integrity of source signals while enabling transmission at low data rates. Examples of coding methods used in subbands include coarse quantization in high-frequency subbands, differential coding, predictive coding, vector quantization, and entropy or statistical coding. Encoders simpler, less expensive and operate rapidly enough to process video signals.

Glover, Daniel R.↗

CFD Research, Parallel Computation and Aerodynamic Optimization

During the last five years, CFD has matured substantially. Pure CFD research remains to be done, but much of the focus has shifted to integration of CFD into the design process. The work under these cooperative agreements reflects this trend. The recent work, and work which is planned, is designed to enhance the competitiveness of the US aerospace industry. CFD and optimization approaches are being developed and tested, so that the industry can better choose which methods to adopt in their design processes. The range of computer architectures has been dramatically broadened, as the assumption that only huge vector supercomputers could be useful has faded. Today, researchers and industry can trade off time, cost, and availability, choosing vector supercomputers, scalable parallel architectures, networked workstations, or heterogenous combinations of these to complete required computations efficiently.

Ryan, James S.↗

Simulation of Aerosols and Chemistry with a Unified Global Model

This project is to continue the development of the global simulation capabilities of tropospheric and stratospheric chemistry and aerosols in a unified global model. This is a part of our overall investigation of aerosol-chemistry-climate interaction. In the past year, we have enabled the tropospheric chemistry simulations based on the GEOS-CHEM model, and added stratospheric chemical reactions into the GEOS-CHEM such that a globally unified troposphere-stratosphere chemistry and transport can be simulated consistently without any simplifications. The tropospheric chemical mechanism in the GEOS-CHEM includes 80 species and 150 reactions. 24 tracers are transported, including O3, NOx, total nitrogen (NOy), H2O2, CO, and several types of hydrocarbon. The chemical solver used in the GEOS-CHEM model is a highly accurate sparse-matrix vectorized Gear solver (SMVGEAR). The stratospheric chemical mechanism includes an additional approximately 100 reactions and photolysis processes. Because of the large number of total chemical reactions and photolysis processes and very different photochemical regimes involved in the unified simulation, the model demands significant computer resources that are currently not practical. Therefore, several improvements will be taken, such as massive parallelization, code optimization, or selecting a faster solver. We have also continued aerosol simulation (including sulfate, dust, black carbon, organic carbon, and sea-salt) in the global model to cover most of year 2002. These results have been made available to many groups worldwide and accessible from the website http://code916.gsfc.nasa.gov/People/Chin/aot.html.

Chin, Mian↗

QCD+QED PDF implications for the Higgs sector

In this work, we examine the implications of electroweak corrections beyond leading order for processes of special interest in the Higgs sector. We especially explore the role of these corrections given the introduction of an explicit parton distribution function (PDF) for the photon in the proton, an object which emerges necessarily in global PDF fits which include QED effects (i.e., ‘QCD+QED PDFs’). We concentrate on several representative cases, including total Higgs-production cross sections through gluon fusion, gg → H, vector-boson fusion (VBFH), and associated production, pp → V H; we also examine differential distributions, taking a representative Higgs-strahlung process, pp → W + H. We find that the recently developed LUX formalism for the photon PDF significantly stabilizes the PDF dependence of both QED-PDF and electroweak corrections in the Higgs sector, while leaving overall ~ 3–4% cross-section-level variations, depending on the chosen QCD+QED PDF. We illustrate this QCD+QED PDF dependence by exploring predictions based upon recent analyses of the CTEQ-TEA, MSHT, and NNPDF analysis groups, fitted either at NNLO or approximate N3LO in QCD.

Higgs production↗

Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H →$ \textrm{b}\overline{\textrm{b}} $ decay mode using LHC proton-proton collision data at $ \sqrt{s} $ = 13 TeV

A measurement is performed of Higgs bosons produced with high transverse momentum (p$_{T}$) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV collected in 2016–2018 with the CMS detector at the LHC and corresponds to an integrated luminosity of 138 fb$^{−1}$. The decay of a high-p$_{T}$ Higgs boson to a boosted bottom quark-antiquark pair is selected using large-radius jets and employing jet substructure and heavy-flavor taggers based on machine learning techniques. Independent regions targeting the vector boson and gluon fusion mechanisms are defined based on the topology of two quark-initiated jets with large pseudorapidity separation. The signal strengths for both processes are extracted simultaneously by performing a maximum likelihood fit to data in the large-radius jet mass distribution. The observed signal strengths relative to the standard model expectation are $ {4.9}_{-1.6}^{+1.9} $ and $ {1.6}_{-1.5}^{+1.7} $ for the vector boson and gluon fusion mechanisms, respectively. A differential cross section measurement is also reported in the simplified template cross section framework.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗