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Rare Supernova Discovery Ushers in New Era for Cosmology
Using an automated supernova-hunting pipeline based at NERSC, astronomers have captured multiple images of a gravitationally lensed Type Ia supernova. This detection is currently the only one of its kind, but astronomers believe that if they can find more, they may be able to measure the rate of the universe’s expansion within four percent accuracy. Fortunately, two Berkeley Lab researchers have a method for identifying more of these events using existing wide-field surveys. Read More »
Berkeley Lab Researchers Make NWChem’s Planewave “Purr” on Intel’s Knights Landing Architectures
Berkeley Lab researchers have successfully added thread-level parallelism on top of MPI-level parallelism in the planewave density functional theory method within the popular software suite NWChem. An important step to ensuring that computational chemists are prepared to compute efficiently on next-generation exascale machines. Read More »
Former Summer Student Jessica Hatcher Wins Research Award
Berkeley Lab Computing Sciences-sponsored summer student Jessica Hatcher won a first place award for her research poster “Quantitative Structure Activity Relationships (QSAR) for Biological Effects of Synthetic Cathinones” at the 74th Joint Annual Meeting of The National Institute of Science/ Beta Kappa Chi. Hatcher was mentored by CRD's Bert de Jong last summer. Read More »
A “VAST” Step Forward in Cyber Security
CRD network researcher Vern Paxson and postdoc Matthias Vallentin are developing VAST, a system that will help forensic security analysts pinpoint how much of an organization’s computer network has been compromised, and where. Read More »
Berkeley’s Lin Lin Awarded 2017 SIAG/CSE Early Career Prize
Berkeley Lab’s Lin Lin has ben honored with a 2017 SIAM Activity Group on Computational Science and Engineering Early Career Prize. The honor is bestowed to one outstanding early career researcher that has made distinguished contributions to the field within seven years of receiving a Ph.D. Read More »
Machine Learning Method Accurately Predicts Metallic Defects
For the first time, researchers at Berkeley Lab have built and trained machine learning algorithms to predict defect behavior in certain intermetallic compounds with high accuracy. This method will accelerate research of new advanced alloys and lightweight new materials for applications spanning automotive to aerospace and much more. Read More »
Simulations Reveal the Invisible Chaos of Superluminous Supernovae
To better understand the physical conditions that create superluminious supernova, astrophysicists are running 2D simulations of these events using supercomputers at NERSC and CRD developed CASTRO code.
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