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Fermilab scientists have implemented a cloud-based machine learning framework to handle data from the CMS experiment at the Large Hadron Collider. Now they can begin to use graph neural networks to boost their pattern recognition abilities in the search for new particles.
Scientists are testing the components and systems for the international Deep Underground Neutrino Experiment, hosted by Fermilab, with other liquid-argon particle detectors. One such detector is ICEBERG, which is over 10,000 times smaller than DUNE will be. ICEBERG’s measurements are providing insight for future neutrino experiments.
Particle accelerators like the LHC require intricate beam dump systems to safely dispose of high-energy particles after each run.
Particle physics is driven by surprise. Researchers in the 1960s studying tiny but ubiquitous particles called neutrinos found only a fraction of what they expected to be in their detector. That unexpected result eventually led to the discovery that neutrinos are shape-shifters, oscillating between three types as they travel. In this stop-motion video, Symmetry writer Zack Savitsky imagines a painter discovering a similar surprise among his art supplies.
Cornell University postdoc David Sweigart has won the 2020 URA Thesis Award for his dissertation analyzing the first year’s data from Fermilab’s Muon g-2 experiment. His efforts in analyzing the anomalous precession frequency of the muon could help confirm or challenge the Standard Model of particle physics.
Laura Fields has won an Early Career Research Award from the Department of Energy to help physicists better understand the composition of neutrino beams used by Fermilab experiments. Her work will help gather and validate results that could shed light on why the universe consists of something rather than nothing.
From fixing up furniture as a kid to testing particle accelerator components at the lab and woodworking in his garage, Dave Burk has a knack for solving problems with his hands.