From Lawrence Berkeley National Laboratory, June 17, 2020: While COVID-19 risks had led to a temporary halt in fabrication work on high-power superconducting magnets built by a collaboration of three national labs for an upgrade of the world’s largest particle collider at CERN in Europe, researchers at Berkeley Lab are still carrying out some project tasks. Fermilab scientist Giorgio Apollinari, head of the U.S.-based magnet effort for the HL-LHC, is quoted in this piece.
The cryostat for Berkeley Lab’s LUX-ZEPLIN experiment — the largest direct-detection dark matter experiment in the U.S. — is successfully moved to its research cavern. This final journey of LZ’s central detector on Oct. 21 to its resting place in a custom-built research cavern required extensive planning and involved two test moves of a “dummy” detector to ensure its safe delivery.
Berkeley Lab’s Dark Energy Spectroscopic Instrument aimed its robotic array of 5,000 fiber-optic “eyes” at the night sky Oct. 22 to capture the first images showing its unique view of galaxy light. It was the first test DESI with its nearly complete complement of components. Fermilab contributed key elements to DESI, including the corrector barrel, hexapod, cage and CCDs. Fermilab also provided the online databases used for data acquisition and the software for the instrument’s robotic positioners.
Scientists are working on a pixelated detector capable of clearly and quickly capturing neutrino interactions — a crucial component for the near detector of the Deep Underground Neutrino Experiment. Using technological solutions developed at University of Bern and Berkeley Lab, a prototype detector called ArgonCube is under construction in Bern and will arrive at Fermilab next year.
Scientists are redoubling their efforts to find dark matter by designing new and nimble experiments that can look for dark matter in previously unexplored ranges of particle mass and energy, using previously untested methods. Dark matter could be much lower in mass and slighter in energy than previously thought.
A new telescope will take a sequence of snapshots with the world’s largest digital camera, covering the entire visible night sky every few days — and repeating the process for an entire decade. What’s the best way to rapidly and automatically identify and categorize all of the stars, galaxies and other objects captured in these images? Data scientists trained have computers to pick out useful information from these hi-res snapshots of the universe.