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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Design and Prototyping of the Electron Ion Collider Electron Storage Ring SRF cavity

Among the EIC?s numerous RF subsystems, the electron storage ring?s (ESR) 591 MHz fundamental RF system is one of the most challenging. Each cavity in the system will handle up to 2.5 A of beam current and supply up to 600 kW beam power under a wide range of voltage. The EIC R&D plan includes the design, fabrication and testing of such a cavity. In this paper, we will report the latest status and findings of the ongoing design and prototyping of this cavity, including the RF and mechanical/thermal design, fabrication design, and the progress of fabrication.

Guo, Jiquan↗
DOE OSTI2024

Pushing the Boundaries of Relativistic Fluid Dynamics: From Particle Colliders to Neutron Star Mergers

This project aimed at establishing connections between fluid dynamics, kinetic theory, and string theory to develop new tools to characterize the far-from-equilibrium properties of relativistic systems, which are instrumental in understanding the emergence of liquid-like behavior of the quark-gluon plasma formed in heavy-ion collisions. This project investigated the fundamental properties of viscous fluids in the full nonlinear regime to pave the way for the systematic inclusion of viscous effects in future numerical simulations of neutron star mergers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Blueprints for Training Information Bottlenecks for Collider Analyses

Dimensionality reduction is a crucial aspect of data analysis in high energy physics, even if accompanied by information loss. Several methods, including histogram- and kernel-based analyses, are only computationally feasible for low-dimensional data. Furthermore, simulation models used in HEP can often only be validated for low-dimensional data. We provide several blueprints for using machine learning to create low-dimensional data representations (continuous event variables and discrete classification labels) for use in signal discovery and parameter estimation tasks. We also describe how to design the learned representation to facilitate a) searches with unknown model parameters and b) validation of simulation models in data control regions.

43 PARTICLE ACCELERATORS↗