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The prodigious amount of data produced at the Large Hadron Collider presents a major challenge for data analysis. Coffea, a Python package developed by Fermilab researchers, speeds up computation and helps scientists work more efficiently. Around a dozen international LHC research groups now use Coffea, which draws on big data techniques used outside physics.
A physicist making great advances in particle detector technology, Estrada is recognized by the American Physical Society Division of Particles and Fields for his creation and development of novel applications for CCD technology that probe wide-ranging areas of particle physics, including cosmology, dark matter searches, neutrino detection and quantum imaging.
Engineers and technicians in the UK have started production of key piece of equipment for a major international science experiment. The UK government has invested £65million in the international Deep Underground Neutrino Experiment. As part of the investment, the UK is delivering a series of vital detector components built at the Science and Technology Facilities Council’s Daresbury Laboratory.
Magnets play a key role in looking for the direct transformation of muons into electrons, a theorized phenomenon that Fermilab’s Mu2e experiment will hunt for when it comes online in 2023. In an important milestone, seven essential magnets have passed testing and been accepted for the construction of the experiment.
Fermilab plays a key role in the Quantum Science Center, led by Oak Ridge National Laboratory. The center unites that Oak Ridge’s powerhouse capabilities in supercomputing and materials science with Fermilab’s world-class high-energy physics instrumentation and measurement expertise and facilities. Drawing on their experience building and operating experiments in cosmology and particle physics and in quantum information science, the Fermilab team is engaging in QSC efforts to develop novel, advanced quantum technologies.
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.
The ArgoNeuT collaboration has published new measurements of the neutrino interaction channel critical for future experiments that seek to understand the difference between matter and antimatter in the world of neutrinos. Their paper presents new strategies for identifying electron neutrinos in liquid-argon neutrino detectors like ArgoNeuT.
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