Bison- messenger of nature in Fermilab
From Tia Sang (Vietnam), April 27, 2021: Fermilab keeps a strong connection with nature and history where he places modern accelerators, through her messenger of nature – the American bison.
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From Tia Sang (Vietnam), April 27, 2021: Fermilab keeps a strong connection with nature and history where he places modern accelerators, through her messenger of nature – the American bison.
From Marianne TV (France), April 21, 2021: An interview on the Muon g-2 experiment result with Laurent Lellouch, CNRS research director at the Theoretical Physics Center and the Universe Physics Institute.
From Physics Today, April 22, 2021: Shirley Ann Jackson, the renowned high-energy physicist, was the first Black woman to earn a doctorate from MIT and did her postdoctoral years at Fermilab and CERN.
From Pioneering Minds, April 20, 2021: Qubits will help advance the search for dark matter, as co-authored in a paper by Fermilab’s Aaron Chou.
From Northern Public Radio, April 20, 2021: Fermilab’s Rebecca Thompson, Head of the Office of Education and Public Outreach, talks on this segment about STEM and its impact on our society.
From WBEZ, April 20, 2021: Fermilab’s Brendan Casey is interviewed about the Muon g-2 experiment and the result announced earlier in April.
From The Daily Herald, April 20, 2021: Fermilab is celebrating Earth Day with events that you can join from wherever you are.
From IRIS-HEP, April 10, 2021: Allison Hall, Fermilab LHC Physics Center researcher, is quoted in this story on the hardware upgrade to CERN’s Large LHC that will significantly boost the proton beams’ intensity.
From the Observador (Portugal), April 18, 2021: The Muon g-2 experiment confirmed a small discrepancy previously detected between the measured values and those calculated by the most advanced theory we have with the probability that this measure is a statistical error is 1 in 100,000.
From Forbes, April 17, 2021: Fermilab scientist Don Lincoln explores the Muon g-2 result announcement about a new measurement that disagrees in a very significant way with predictions from the Standard Model.