Nov. 4, 2022
The weak nuclear force is currently not entirely understood, despite being one of the four fundamental forces of nature. In a pair of articles, a multi-institutional team, including theorists and experimentalists from Louisiana State University, Lawrence Livermore National Laboratory, Argonne National Laboratory and other institutions worked closely together to test physics beyond the "Standard Model" through high-precision measurements of nuclear beta decay.
By loading lithium-8 ions, an exotic heavy isotope of lithium with a less than one second half-life, in an ion trap, the experimental team was able to detect the energy and directions of the particles emitted in the beta decay of lithium-8 produced with the ATLAS accelerator at Argonne National Laboratory and held in an ion trap. Different underlying mechanisms for the weak nuclear force would give rise to distinct energy and angular distributions, which the team determined to unrivaled precision.
State-of-the-art calculations with the ab initio symmetry-adapted no-core shell model had to be performed to precisely account for typically neglected effects that are 100 times smaller than the dominant decay contributions. However, since the experiments have achieved remarkable precision, it is now required to confront the systematic uncertainties of such corrections that are difficult to be measured.
Two Lawrence Livermore led teams received SciVis Test of Time awards at the 2022 IEEE VIS conference, for papers that have achieved lasting relevancy in the field of scientific visualization.
Published in 2008, an LLNL-led paper that — for the first time — allowed Digital Morse Theory to be applied to large scale and three-dimensional data, won the 14-year Test of Time award for making a lasting impact to the decades-long application of computational topology to data analysis and visualization at scale.
The SciVis 25-year Test of Time award went to a paper co-authored by former LLNL senior scientist Mark Duchaineau and current LLNL computer scientist Mark Miller, who has helped develop numerous scientific database, visualization and data modeling technologies at LLNL. Several Los Alamos National Laboratory (LANL) researchers also appear as co-authors. Published in 1997, the team’s paper describes a method for terrain visualization called Real-time Optimally Adapting Meshes (ROAM), in which triangles are split and then merged to optimally add or remove detail, allowing for meshes to be rendered at high frame rates and maintain high performance for terrain visualization.
Kern's future as a carbon-management capital may hinge on a cooperative effort coming together locally to compete against other parts of the country for a share of $3.5 billion approved under last year's federal Infrastructure Investment and Jobs Act.
A consortium of oil companies and other organizations is being finalized to prepare a bid for $800 million or more to help make the county home to one of four U.S. hubs that, by no later than 2029, would pull carbon dioxide out of the atmosphere — "direct air capture," or DAC — to begin to slow global warming.
Scientists at Lawrence Livermore National Laboratory, partner to Cal State Bakersfield and the Kern Community College District and now a member of the consortium, see DAC — a form of carbon capture and sequestration, or CCS — as an opportunity for California to make strong progress toward its goal of carbon neutrality by 2045. For the county, they say, it's also a chance to create safe, quality jobs.
LLNL Director Kimberly Budil said any project arising from a federal grant award could be just the start. Additional companies will be attracted to Kern if the workforce is available locally, along with other local assets, she added. "It will become self-reinforcing over time," she said, adding to efforts to make the local economy more diverse.
Researchers from Lawrence Livermore National Laboratory (LLNL) are expanding the creation of vertically aligned single-walled carbon nanotubes (SWCNT) that could transform various commercial products ranging from automotive parts, rechargeable batteries and sporting goods to water filters and boat hulls.
The majority of CNT production at present is utilized in thin films and mass composite materials, which depend on disorganized CNT architectures. For several uses, organized CNT architectures, such as vertically aligned forests, offer significant benefits for using the properties of separate CNTs in macroscopic systems.
“Robust synthesis of vertically-aligned carbon nanotubes at large scale is required to accelerate deployment of numerous cutting-edge devices to emerging commercial application,” said LLNL scientist Francesco Fornasiero.
Lawrence Livermore researchers are starting work on a three-year project aimed at improving methods for visual analysis of large heterogeneous data sets as part of a recent Department of Energy funding opportunity.
The joint project, entitled “Neural Field Processing for Visual Analysis,” will be led at LLNL by co-principal investigator (PI) Andrew Gillette. Gillette is joined by lead PI Matthew Berger at Vanderbilt University and co-PI Joshua Levine at the University of Arizona.
While visualization is essential to understanding results of numerical simulations, modern data sets can be large in size and heterogeneous in type, making direct processing computationally challenging, Gillette said.
The newly funded project will explore methods for processing “implicit neural representations” (INRs) — datasets that incorporate coordinate-based neural networks to represent scientific data sets efficiently and compactly. Currently, traditional processing algorithms and visual analysis techniques cannot be applied to INRs directly, Gillette explained.