DUNE

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Cryomodules of five different types, one of which is the SSR1 pictured here, boost the energy of the beam. cryomodule, beam, PIP-II, superconducting technology, accelerator Photo: Tom Nicol, Fermilab

Testing wraps up for first Fermilab-designed cryomodule for PIP-II accelerator

A Fermilab team has completed tests for a crucial superconducting segment for the PIP-II particle accelerator, the future heart of the Fermilab accelerator chain. The segment, called a cryomodule, will be one of many, but this is the first to be fully designed, assembled and tested at Fermilab. It represents a journey of technical challenges and opportunities for innovation in superconducting accelerator technology.

Tackling big data challenges for next-generation experiments

    From UKRI, Feb. 22, 2021: UKRI scientists are developing vital software to exploit the large data sets collected by the next-generation experiments in high-energy physics. The new software will have the capability to crunch the masses of data that the LHC at CERN and next-generation neutrino experiments, such as the Fermilab-hosted Deep Underground Neutrino Experiment, will produce this decade.

    The perplexing question of missing cosmic antimatter

      From Forbes, Feb. 10, 2021: Fermilab scientist Don Lincoln explains why there should be equal amounts of matter and antimatter in the universe. There aren’t. He discusses several current theories that try to explain the discrepancy. Better understanding this imbalance is an aim of ongoing experiments, such as DUNE, which is being built at Fermilab.

      UK scientists build core components of global neutrino experiment

      Engineers and technicians in the UK have started production of key piece of equipment for a major international science experiment. The UK government has invested $89 million in the international Deep Underground Neutrino Experiment. As part of the investment, the UK is delivering a series of vital detector components built at the Science and Technology Facilities Council’s Daresbury Laboratory.

      Engineers and scientists at Fermilab are designing machine learning programs for the lab’s accelerator complex. These algorithms will enable the laboratory to save energy, give accelerator operators better guidance on maintenance and system performance, and better inform the research timelines of scientists who use the accelerators. The pilot system will used on the Main Injector and Recycler, pictured here. It will eventually be extended to the entire accelerator chain. Photo: Reidar Hahn, Fermilab

      Fermilab receives DOE funding to develop machine learning for particle accelerators

      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.