Fermilab scientists and engineers are developing a machine learning platform to help run Fermilab’s accelerator complex alongside a fast-response machine learning application for accelerating particle beams. The programs will work in tandem to boost efficiency and energy conservation in Fermilab accelerators.
From The New York Times, Nov. 23, 2020: It might be possible, physicists say, but not anytime soon. And there’s no guarantee that we humans will understand the result. Fermilab Deputy Director of Research Joe Lykken is quoted in this piece on using artificial intelligence to discover laws of physics.
Thursday Oct 15, 2020, 11:00 a.m. – 12:00 p.m. US/Central Speaker: Jan Offermann, University of Chicago Title: LGN: A Lorentz Group Equivariant Neural Network for Particle Physics Event page: https://indico.fnal.gov/event/46000/ Abstract: Machine learning has been used in high-energy physics for decades, and today we see plentiful examples of neural networks designed for detector operation and analysis tasks. However, as these tools find their ways into more and more physics analyses, questions about their functionality and interpretability remain. In this talk,…
From the Department of Energy, Aug. 17, 2020: Seven DOE national laboratories, including Fermilab, will lead a total of 14 projects aimed at both automating facility operations and managing data modeling, acquisition, mining, and analysis for the interpretation of experimental results. The projects involve large X-ray light sources, neutron scattering sources, particle accelerators and nanoscale science research centers.
From Gizmodo, May 5, 2020: Fermilab scientist Brian Nord weighs in on the question of how automated devices, such as an autonomously operating telescope, free from human biases and complications, could find the solutions to questions about dark matter and dark energy.
Join us for the inaugural Computational Science Seminar sponsored jointly by the University of Chicago, Argonne National Laboratory and Fermilab on Tuesday, Nov. 12, from 10:30 to 11:30 a.m. at the University of Chicago (Watch remotely at https://fnal.zoom.us/j/720265198, or watch the simulcast at the Racetrack-WH7X). SCD’s Nhan Tran is the speaker. https://indico.fnal.gov/event/22307/ Abstract: In the first of the joint seminar series to build connections between the University of Chicago, Argonne, and Fermilab, we will highlight current activities in…
Speaker: Nhan Tran (FNAL) Tuesday, November 12, 2019 from 10:30 to 11:30 (US/Central) Attend in person at University of Chicago, John Crerar Library, Kathleen A Zar Room (first floor) Watch simulcast at Fermilab, Racetrack (WH7X) Watch remotely with Zoom (link to be provided soon – See https://indico.fnal.gov/event/22307/) Abstract: In the first of the joint seminar series to build connections between the University of Chicago, Argonne, and Fermilab, we will highlight current activities in Artificial Intelligence (AI) at Fermilab. Machine…
From Inside HPC, Sept. 15, 2019: Argonne and the National Center for Supercomputing Applications use deep learning to analyze Dark Energy Survey data.
From MIT News, Aug. 19, 2019: A new prototype machine-learning technology co-developed by Fermilab and MIT scientists speeds Large Hadron Collider data processing by up to 175 times over traditional methods.
A new machine learning technology tested by Fermilab scientists and collaborators can spot specific particle signatures among an ocean of LHC data in the blink of an eye, much faster than standard methods. Sophisticated and swift, its performance gives a glimpse into the game-changing role machine learning will play in making future discoveries in particle physics as data sets get bigger and more complex.