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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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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measurement of Born cross section of $$ {e}^{+}{e}^{-}\to {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ at center-of-mass energies between 3.510 and 4.951 GeV

Abstract Using 24.1 fb −1 ofe + e − collision data collected with the BESIII detector at the BEPCII collider, the Born cross sections and effective form factors of the$$ {e}^{+}{e}^{-}\to {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ e + e − → Σ + Σ ¯ − reaction are measured. The measurements are performed at center-of-mass energies ranging from 3.510 to 4.951 GeV. No significant evidence for the decay of the charmonium(-like) states,ψ(3770),ψ(4040),ψ(4160),Y(4230),Y(4360),ψ(4415), andY(4660), into a$$ {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ Σ + Σ ¯ − final state is observed. Consequently, upper limits for the products of the branching fractions and the electronic partial widths at the 90% confidence level are reported for these decays.

Physics↗
DOE OSTI2023

Energy Optimization of Light and Heavy-Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data Sharing (Final Scientific and Technical Report)

Vehicle connectivity and automated driving technologies individually have the potential to decrease energy consumption and/or increase safety on light, medium or heavy duty vehicles to varying degrees depending on the traffic infrastructure and specific driving scenarios. Due to advances in sensing, perception and computing power, research and development emphasis in the mobility sector has shifted away from connectivity. Prior research has shown that driving automation with the absence of connectivity can in certain circumstances increase energy consumption. The effectiveness of synergizing connectivity and driving automation technologies is the focus of this work, specifically applied to vehicle cohorts of mixed composition, light and heavy duty, and powertrains ranging from all electric to conventional internal combustion engine. The project team is led by Michigan Technological University (MTU) and partnered with AVL Mobility Technologies Inc. (AVL), Borg Warner (BW), Traffic Technology Services (TTS), American Center for Mobility (ACM) and Navistar (NAV). The main thrusts for the team are to develop a micro-traffic simulation environment with specific VD&PT system attributes and CAV capabilities, 2) field a vehicle test fleet of mixed classification, propulsion and CAV capacity, 3) develop artificial intelligence (AI) and machine learning (ML) based multi-agent optimization methods for various traffic infrastructures, 4) integrate the virtual environment and the optimization methods then deploy the system as a CAV hardware in the loop (HiL) for the vehicle test fleet and 5) conduct closed track and public road testing to validate simulation and demonstrated energy and mobility improvements at multiple scales. For a cohort of mixed vehicles, the team will demonstrate a reduction of energy consumption of 10-50% at intersection, arterial roadway and limited access highway scenarios through connectivity and automation in simulation and at a closed test track. The energy reduction objectives of the project are summarized in Table 1, indicating the infrastructure and over what distances are relevant considered. Single scenario energy reductions are not relevant and thus, the research team took the approach to vary parameters associated with the infrastructure, vehicle cohort composition and dynamic behavior to generate energy consumption distributions for both unconnected and connected scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2011

Investigation of the Crust of the Pannonian Basin, Hungary Using Low-Altitude CHAMP Horizontal Gradient Magnetic Anomalies

The Pannonian Basin is a deep intra-continental basin that formed as part of the Alpine orogeny. It is some 600 by 500 km in area and centered on Hungary. This area was chosen since it has one of the thinnest continental crusts in Europe and is the region of complex tectonic structures. In order to study the nature of the crustal basement we used the long-wavelength magnetic anomalies acquired by the CHAMP satellite. The SWARM constellation, scheduled to be launched next year, will have two lower altitude satellites flying abreast, with a separation of between ca. 150 to 200 km. to record the horizontal magnetic gradient. Since the CHAMP satellite has been in orbit for eight years and has obtained an extensive range of data, both vertically and horizontally there is a large enough data base to compute the horizontal magnetic gradients over the Pannonian Basin region using these many CHAMP orbits. We recomputed a satellite magnetic anomaly map, using the spherical-cap method of Haines (1985), the technique of Alsdorf et al. (1994) and from spherical harmonic coefficients of MF6 (Maus et aI., 2008) employing the latest and lowest altitude CHAMP data. We then computed the horizontal magnetic anomaly gradients (Kis and Puszta, 2006) in order to determine how these component data will improve our interpretation and to preview what the SW ARM mission will reveal with reference to the horizontal gradient anomalies. The gradient amplitude of an 1000 km northeast-southwest profile through our horizontal component anomaly map varied from 0 to 0.025 nT/km with twin positive anomalies (0.025 and 0.023 nT/km) separated by a sharp anomaly negative at o nT/km. Horizontal gradient indicate major magnetization boundaries in the crust (Dole and Jordan, 1978 and Cordell and Grauch, 1985). Our gradient anomaly was modeled with a twodimensional body and the anomaly, of some 200 km, correlates with a 200 km area of crustal thinning in the southwestern Pannonian Basin.

Taylor, Patrick T.↗
DOE OSTI2024

First Measurement of the Decay Asymmetry in the Pure W -Boson-Exchange Decay Λ c + → Ξ 0 K +

Based on 4.4 fb − 1 of e + e − annihilation data collected at the center-of-mass energies between 4.60 and 4.70 GeV with the BESIII detector at the BEPCII collider, the pure W -boson-exchange decay Λ c + → Ξ 0 K + is studied with a full angular analysis. The corresponding decay asymmetry is measured for the first time to be α Ξ 0 K + = 0.01 ± 0.16 ( stat ) ± 0.03 ( syst ) . This result reflects the noninterference effect between the S - and P -wave amplitudes. The phase shift between S - and P -wave amplitudes has two solutions, which are δ p − δ s = − 1.55 ± 0.25 ( stat ) ± 0.05 ( syst ) rad or 1.59 ± 0.25 ( stat ) ± 0.05 ( syst ) rad . Published by the American Physical Society 2024

Physics↗
NASA NTRSDate not supplied

NASA’s Moon Trek Portal: New Capabilities Supporting Mission Planning and Engagement

Introduction: NASA’s Moon Trek (https://trek.nasa.gov/moon/) is one of a growing number of interactive, browser-based, online portals for planetary data visualization and analysis produced by NASA’s Solar System Treks Project (SSTP). Moon Trek continues to be enhanced with new data and new capabilities enabling it to facilitate the planning and conducting of upcoming lunar missions by NASA, its commercial partners, and its international partners, as well as advancing its role as a valuable outreach tool. A Comprehensive Online Web Portal: Developed at NASA’s Jet Propulsion Laboratory (JPL) and managed as a project of NASA’s Solar System Exploration Research Virtual Institute (SSERVI) at NASA Ames Research Center, Moon Trek is a browser-based web portal. The portal provides easy-to-use tools for browsing, data layering, data product blending, and feature search among thousands of data products covering topography, mineralogy, elemental abundance, geology, and much more. Visualizations are provided in var-ious map projections, interactive 3D viewing, and in virtual reality. Using an in-house stereo workflow, SSTP is able to produce new NAC-based high-resolution mosaics and DEMs. Diverse Applications for Lunar Exploration: Baseline analytic tools available to all users include dis-tance measurement, elevation profiling, sun angle calculation, and 3D print file generation. More advanced account-level tools allow users to perform more computationally intensive analyses. These include ray-traced lighting analysis for user-specified areas over user-specified time/date ranges and time intervals, electro-static surface potential analysis, subsetting of large data products, slope analysis, and Lunar Laser Ranging geometry calculation. Artificial intelligence (AI) and ma-chine learning (ML) based tools have been implemented for crater detection and hazard analysis, boulder detection and hazard analysis, and rockfall detection. New Tools Facilitating Exploration: Additional, new tools have recently been added and others are in development, offering even greater functionality in con-ducting analyses of potential landing sites and areas of surface operations. The new Line-of-Sight tool facilitates communications planning between locations on the lunar surface, between any given site on the lunar surface and a specified ground station on the Earth, and between a site on the lunar surface and a relay asset in lunar orbit, all taking into account local lunar topography. The new Data Plotter tool provides both tabular and graphical representations of pixel values along a user specified path for a growing number of data products. The new NAC Finder tool will identify and pro-vide access to NAC images that intersect a user-defined path or bounded area. The SSTP development team is looking to leverage the capabilities of its existing AI and ML crater, boulder, and rockfall detection and analysis tools, and extend that technology to a generalized feature detector that can be trained on instances of specific types of landforms and then search the lunar surface for more examples of such features. New traverse planning tools are being developed with use cases in generalized concept studies and specific mission planning in mind. These will facilitate finding optimal traverse paths based on constraints such as slope, lighting, hazard avoidance, and communications. These will be complemented by new traverse visualization capabilities. Users will be able to interactively ride along with a rover, examining 3D views of the terrain while adjusting camera height and viewing angle along with selecting different data layer overlays to drape across the terrain. Engaging the Public: The capabilities being developed for mission planning are being leveraged to further enhance Moon Trek’s proven utility as a valuable public outreach resource. This includes providing multiple lev-els of engagement with different points of entry. At its simplest level, promoting understanding through visualization, media and the public will be able to easily visualize and conduct their own exploration of lunar sites targeted by NASA and its partners. For a more in-depth experience, we are working with our stakeholders to promote understanding through interaction by extend-ing our current landing site and traverse analysis capabilities, making simplified access to these tools available to those who want to explore more deeply key factors in planning a mission through interactive and possibly even gamified experiences. The highest degree of public outreach, focusing on engagement through scientific participation, could be achieved through our work with missions and the NASA Office of the Chief Scientist on Moon Trek’s extension as a tool with specialized capabilities for facilitating citizen science. In such scenarios, participants become members of extended mis-sion science teams, using dedicated and integrated interfaces to analyze mission data to help answer questions key to lunar science and exploration. We are work-ing with NASA’s Office of Communications, museums, planetariums, and the media to help them easily integrate accurate, detailed visualizations of NASA’s lunar destinations and exploration into their content/productions and to engage diverse audiences in diverse venues.

Moon Trek↗
DOE OSTI2024

Amplitude analysis of the decays and *

Abstract Usingannihilation data corresponding to an integrated luminosity of 2.93taken at the center-of-mass energyGeV with the BESIII detector, a joint amplitude analysis is performed on the decaysand(non-η). The fit fractions of individual components are obtained, and large interferences among the dominant components of the decays,,, andare observed in both channels. With the obtained amplitude model, the-even fractions ofand(non-η) are determined to be% and%, respectively. The branching fractions ofand(non-η) are measured to be% and%, respectively. The amplitude analysis provides an important model for the binning strategy in measuring the strong phase parameters ofwhen used to determine the CKM anglevia thedecay.

Physics↗
DOE OSTI2024

Study of D + → K S 0 K * ( 892 ) + in D + → K S 0 K S 0 π +

Using a data sample of e + e − collisions corresponding to an integrated luminosity of 7.93 fb − 1 collected with the BESIII detector at the center-of-mass energy 3.773 GeV, we perform the first amplitude analysis of the decay D + → K S 0 K S 0 π + . The absolute branching fraction of D + → K S 0 K S 0 π + is measured to be ( 2.97 ± 0.0 9 stat ± 0.0 5 syst ) × 10 − 3 . The dominant intermediate process is D + → K S 0 K * ( 892 ) + , whose branching fraction is determined to be ( 8.72 ± 0.2 8 stat ± 0.1 5 syst ) × 10 − 3 , including all the K * ( 892 ) + decays. Published by the American Physical Society 2024

Ablikim, M.↗
DOE OSTI2026

Predicting Adaptively Chosen Observables in Quantum Systems

Recent advances have demonstrated that 𝒪⁡(log 𝑀) measurements suffice to predict 𝑀 properties of arbitrarily large quantum many-body systems. However, these remarkable findings assume that the properties to be predicted are chosen independently of the data. This assumption can be violated in practice, where scientists adaptively select properties after looking at previous predictions. This work investigates the adaptive setting for three classes of observables: local, Pauli, and bounded-Frobenius-norm observables. We prove that Ω⁡(√𝑀) samples of an arbitrarily large unknown quantum state are necessary to predict expectation values of 𝑀 adaptively chosen local and Pauli observables, where the system size scales exponentially and polynomially in 𝑀, respectively. We also present computationally efficient algorithms that achieve this information-theoretic lower bound. In contrast, for bounded-Frobenius-norm observables, we devise an algorithm requiring only 𝒪⁡(log 𝑀) samples, independent of system size. These results highlight the potential pitfalls of adaptivity in analyzing data from quantum experiments and provide algorithmic tools to safeguard against erroneous predictions in quantum experiments.

Machine learning↗
DOE OSTI2024

Precise Measurement of Born Cross Sections for e + e − → D D ¯ at s = 3.80 − 4.95 GeV

Using data samples collected with the BESIII detector at the BEPCII collider at center-of-mass energies ranging from 3.80 to 4.95 GeV, corresponding to an integrated luminosity of 20 fb − 1 , a measurement of Born cross sections for the e + e − → D 0 D ¯ 0 and D + D − processes is presented with unprecedented precision. Many clear peaks in the line shape of e + e − → D 0 D ¯ 0 and D + D − around the mass range of G ( 3900 ) , ψ ( 4040 ) , ψ ( 4160 ) , Y ( 4260 ) , and ψ ( 4415 ) , etc., are foreseen. These results offer crucial experimental insights into the nature of hadron production in the open-charm region. Published by the American Physical Society 2024

Physics↗
DOE OSTI2024

Observation of the singly Cabibbo-suppressed decay Λ c + → Σ − K + π +

The singly Cabibbo-suppressed decay Λ c + → Σ − K + π + is observed for the first time with a statistical significance of 5.4 σ by using 4.5 fb − 1 of e + e − collision data collected at center-of-mass energies between 4.600 and 4.699 GeV with the BESIII detector at BEPCII. The absolute branching fraction of Λ c + → Σ − K + π + is measured to be ( 3.8 ± 1.2 stat ± 0.2 syst ) × 10 − 4 in a model-independent approach. This is the first observation of a Cabibbo-suppressed Λ c + decay involving Σ − in the final state. The ratio of branching fractions between Λ c + → Σ − K + π + and the Cabibbo-favored decay Λ c + → Σ − π + π + is observed to be ( 0.4 ± 0.1 ) s c 2 , where s c ≡ sin θ c = 0.2248 with θ c the Cabibbo mixing angle. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2020

Development of the Intelligent, Preventive Infrared (IR) Inspection System Housed in Hybrid Robotic Platforms

Robots and robotic systems that are designed for inspection, environmental study, and health and safety aid are becoming an increasing necessity. However, there is a number of challenges that accompany robots that are designed for these specific applications. These challenges include: navigating compact, enclosed spaces, travelling over multiple terrains and large obstacles, using the proper sensing and detection methods to assess an environment, and the use of lightweight and durable materials. The robotic platforms currently in development look at all of these challenges and attempt to overcome them. These designs specific use of hybrid robotic platforms, or platform that utilizes soft and rigid materials, allows for a more flexible platform and makes environments more navigable. To further improve the navigation of the platforms and environmental assessment, a novel infrared detection system is housed in the platforms to create a robot that can be used for the applications listed above and more. Objectives: Further develop two types of robotic platforms that utilize additive manufacturing, soft materials, and rigid materials. Continue the development of an intelligent and inhibitory infrared detection system based on an artificial intelligence (AI) algorithm. Continued study and fabrication of active soft materials designed for both sensing and actuation in hybrid robotic systems. Improve additive manufacturing fabrication to design rigid and semi-rigid components for hybrid robotic platforms. Transformable Wheel Robotic Platform: The chassis, wheels, tires and inspection system housing use different additive manufacturing techniques for fabrication. Continued work with additive manufacturing has lead to studies in metal-based printing and modular design and manufacturing. The new platform design with integrated electrical component printed. This will allow integration of the sensor housing onto the platform. Electrical components are being tested for battery life and performance. To improve this performance, such as integration of Lithium Polymer (LiPo) batteries. Snake Robotic Platform: The main focus of the development has centered around liquid-based soft actuators. That act on the principles of electrostatic and hydraulic actuation. A liquid dielectric sits between two compliant electrodes, contained by a flexible polymer shell. The electrodes and film gradually collapse toward each other from one corner of the electrode to the other. When the electrodes and film close together, a majority of the fluid is pushed into the area not covered by an electrode. A thin layer of the liquid dielectric remains between the electrode. The actuators will be stacked to cause large displacement, and move the linkages. The chassis of this platform uses purely additively manufactured linkages. Intelligent, Preventive IR Inspection System: Development of the AI for the system has lead to using a Scikit-Learn which assists in creating predictive models based on Regression, clustering, classification etc. To improve the infrared thermometry for low emissivity sources, work on the fabrication of a tandem photoconductive infrared thermometer was a main focus. Distance-Voltage-Temperature response data has been collected in the range of 7 cm - 100 cm and 200-400 deg. C. Modifications were made to the existing test bench to have a better control over the data. Automated data collection was realized using a Python code and an Arduino controlled stepper motor to increase the sample rate. A protective enclosure has been built around the setup to minimize the effect of the environment on the measurements. To better predict temperature, different AI models are being optimized. The regression model, LARS showed a high accuracy but had convergence issues and only works for the current test set-up. Results: The Transformable Wheel Robot has developed into a more flexible and modular platform. With the improvements to the current work, effort on the tire or soft gripper design been a large focus. The soft grippers will be interchange able to allow for increased performance in identified terrain types. The development of the liquid-based actuators allows the snake robotic platform to achieve the goals of being flexible and able to navigate confined spaces. However, there is room for improvement. Optimization work is currently being done in COMSOL Multiphysics. With the current IR system set-up the LARS model perfectly predicts the data; however, considering the mobility aspect of the project other models will allow for an optimized system. Testing of other model types is currently being done.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Search for the radiative transition χ c 1 ( 3872 ) → γ ψ 2 ( 3823 )

Using 9.0 f b − 1 of e + e − collision data collected at center-of-mass energies from 4.178 to 4.278 GeV with the BESIII detector at the BEPCII collider, we perform the first search for the radiative transition χ c 1 ( 3872 ) → γ ψ 2 ( 3823 ) . No signal is observed and the upper limit on the ratio of branching fractions B ( χ c 1 ( 3872 ) → γ ψ 2 ( 3823 ) , ψ 2 ( 3823 ) → γ χ c 1 ) / B ( χ c 1 ( 3872 ) → π + π − J / ψ ) is set at 0.075 at the 90% confidence level. Our result contradicts theoretical predictions under the assumption that the χ c 1 ( 3872 ) is the pure charmonium state χ c 1 ( 2 P ) . Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Study of the decay D 0 → ρ ( 770 ) − e + ν e

We present a study of the semileptonic decay D 0 → π − π 0 e + ν e using an e + e − annihilation data sample of 7.93 fb − 1 collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The branching fraction of D 0 → ρ ( 770 ) − e + ν e is measured to be ( 1.439 ± 0.033 ( stat ) ± 0.027 ( syst ) ) × 10 − 3 , which is a factor 1.6 more precise than previous measurements. By performing an amplitude analysis, we measure the hadronic form-factor ratios of D 0 → ρ ( 770 ) − e + ν e at q 2 = 0 assuming the single-pole-dominance parametrization: r V = V ( 0 ) / A 1 ( 0 ) = 1.548 ± 0.079 ( stat ) ± 0.041 ( syst ) and r 2 = A 2 ( 0 ) / A 1 ( 0 ) = 0.823 ± 0.056 ( stat ) ± 0.026 ( syst ) . Published by the American Physical Society 2024

Ablikim, M.↗
DOE OSTI2025

Measurement of the inclusive cross sections of prompt J / ψ and ψ ( 3686 ) production in e + e − annihilation from s = 3.808 to 4.951 GeV

The inclusive cross sections of prompt J / ψ and ψ ( 3686 ) production are measured at center-of-mass energies from 3.808 to 4.951 GeV. The dataset used is 22 fb − 1 of e + e − annihilation data collected with the BESIII detector operating at the BEPCII storage ring. The results obtained are in agreement with the previous BESIII measurements of exclusive J / ψ and ψ ( 3686 ) production. The average values obtained for the cross sections measured in the center-of-mass energy ranges from 4.527 to 4.951 GeV for J / ψ and from 4.843 to 4.951 GeV for ψ ( 3686 ) , where the impact of known resonances is negligible, are 14.0 ± 1.7 ± 3.1 pb and 15.3 ± 3.0 pb , respectively. For J / ψ , the first and the second uncertainties are statistical and systematic, respectively. For ψ ( 3686 ) , the uncertainty is total. These values are useful for testing charmonium production models. Published by the American Physical Society 2025

Ablikim, M.↗
DOE OSTI2024

Measurement of branching fractions for Λ c + → n K S 0 π + and Λ c + → n K S 0 K +

Based on 4.5 fb − 1 of e + e − collision data accumulated at center-of-mass energies between 4599.53 and 4698.82 MeV with the BESIII detector, we measure the absolute branching fraction of the Cabibbo-favored decay Λ c + → n K S 0 π + with the precision improved by a factor of 2.8 and report the first evidence for the singly-Cabibbo-suppressed decay Λ c + → n K S 0 K + . The branching fractions for Λ c + → n K S 0 π + and Λ c + → n K S 0 K + are determined to be ( 1.86 ± 0.08 ± 0.04 ) × 10 − 2 and ( 3. 9 − 1.4 + 1.7 ± 0.3 ) × 1 0 − 4 , respectively, where the first uncertainties are statistical and the second ones are systematic. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Measurement of the branching fractions of D + → K + K − π + π + π − , ϕ π + π + π − , K S 0 K + π + π − π 0 , K S 0 K + η , and K S 0 K + ω decays

Using 20.3 fb − 1 of e + e − collision data collected at a center-of-mass energy of 3.773 GeV with the BESIII detector operating at the BEPCII collider, the branching fractions of three hadronic charm meson decays, D + → ϕ π + π + π − , D + → K S 0 K + π + π − π 0 , and D + → K S 0 K + ω , are measured for the first time to be ( 0.54 ± 0.19 ± 0.02 ) × 10 − 4 , ( 2.51 ± 0.34 ± 0.14 ) × 10 − 4 , and ( 2.02 ± 0.35 ± 0.10 ) × 10 − 4 , respectively. Futhermore, the branching fractions of D + → K + K − π + π + π − and D + → K S 0 K + η are measured with improved precision, yielding values of ( 0.66 ± 0.11 ± 0.03 ) × 10 − 4 and ( 2.27 ± 0.22 ± 0.05 ) × 10 − 4 , respectively. Published by the American Physical Society 2025

Ablikim, M.↗
DOE OSTI2026

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.

Tran, Hoang [Pacific Northwest National Laboratory↗