Over time, particle physics and astrophysics and computing have built upon one another’s successes. That coevolution continues today. New physics experiments require computing innovation, including cluster computing for the Tevatron, and more recently machine learning and quantum problem-solving.
Whether in Serbia or Chicago, Fermilab postdoctoral researcher Aleksandra Ćiprijanović is working to unlock the secrets of the night sky. As a member of the Deep Skies Lab, an international collaboration of physicists, she’s figuring out how to use artificial intelligence and machine learning to better handle the huge amounts of data needed for discovery science.
From AI Business, June 16, 2021: Fermilab was part of a consortium helping in the development of the TinyML benchmark from MLCommons to measure machine learning performance that will bring intelligence to devices like wearables, thermostats, and cameras.
To fully realize the potential of quantum computing, scientists must start with the basics: developing step-by-step procedures, or algorithms, for quantum computers to perform simple tasks. A Fermilab scientist has done just that, announcing two new algorithms that build upon existing work in the field to further diversify the types of problems quantum computers can solve.
U.S. CMS physicists from Fermilab and associated universities collaborating under the umbrella of the LPC make up a team that is the first to perform a new kind of search for “stealthy” supersymmetry that does not result in an obvious signature of large energy imbalance. Instead, the LPC team is looking for collisions that result in an unusually large number of particles in the detector. CMS recently published a briefing explaining their analysis.
Title: Measuring QCD Splittings with Invertible Networks Date and time: Thursday, Feb. 25, 11 a.m. – noon Central Speaker: Theo Heimel, Heidelberg University Abstract: QCD splittings are among the most fundamental theory concepts at the LHC. In this talk, I will present how conditional invertible neural networks, a realization of normalizing flows, can be used to extract posterior distributions for QCD theory parameters from low-level jet observables. This approach expands the LEP measurements of QCD Casimirs to a systematic test…
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.