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Future particle colliders will need strong magnets to steer high-energy particle beams as they travel close to the speed of light on their circular path. A group at Fermilab has achieved a record field strength of 14.1 teslas for a particle accelerator steering magnet, breaking the 11-year record.

The Johannes Gutenberg University Mainz, Germany, has taken a significant step to participate in the international Deep Underground Neutrino Experiment, hosted by Fermilab. Fermilab and the university have signed an agreement to jointly appoint an internationally renowned researcher who will strengthen the experimental particle physics research program at JGU Mainz and advance a German contribution to DUNE. This is the first Fermilab joint agreement with a university in Germany.

Engineers at Fermilab have shown that sometimes, to reshape the metal heart of a particle accelerator, what you need is a balloon. The new, patented technique is a novel solution to a problem that affects an essential component of accelerators: superconducting cavities.

For years, U.S. institutions have been working to upgrade the hardware in the behemoth CMS particle detector at the Large Hadron Collider, enabling it to profit fully from the LHC’s increasing collision energy and intensity. With CD-4 approval, the Department of Energy formally recognized that the USCMS collaboration, managed by Fermilab, met every stated goal of the upgrade program — on time and under budget.

The first major superconducting section of the PIP-II accelerator has come to Fermilab: the first of 23 cryomodules for the future accelerator. The cryomodules’ job is to get the lab’s powerful proton beam up and moving, sending it to higher and higher energies, approaching the speed of light. This first cryomodule also represents a successful joint effort between Argonne National Laboratory and Fermilab to design and produce a critical accelerator component for the future heart of Fermilab.

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.