An international collaboration is upgrading the CMS detector at CERN to handle the increased number of collisions that the High-Luminosity Large Hadron Collider will produce.
Mary Magnuson
Accelerator experts at three national labs have advanced the next generation of cryomodules, the building blocks of particle accelerators. A prototype built for the high-energy upgrade of SLAC’s LCLS-II X-ray laser has advanced the state of the art, packing more acceleration into a smaller distance, and could dramatically improve future accelerators.
This summer, a team of scientists, engineers and technicians finished installing one of the main components that will create the strong electric field within the Short-Baseline Near Detector. Now they are getting ready to assemble the rest of the detector.
Back when it was theorized, scientists weren’t sure they would ever detect the neutrino. Now scientists, including some at Fermilab, are searching for a version of the particle that could be even more elusive.
Fermilab’s Pedro Machado has won the 2021 Universities Research Association Early Career Award for his theoretical work on neutrino science that helps experimentalists with novel search strategies and scientific questions worth exploring.
The annual Universities Research Association Thesis Award recognizes outstanding work for a thesis conducted at or in collaboration with Fermilab. Zhang’s winning Ph.D. dissertation included insights into both physics searches and equipment upgrades at the Large Hadron Collider’s CMS detector. Fermilab serves as the U.S. hub for CMS.
Construction crews will excavate around 800,000 tons of rock to make space for the international Deep Underground Neutrino Experiment. But first, teams must carve out a quarter-mile-high ventilation shaft.
A group of U.S. national laboratories, including Fermilab; publishers; journals and other organizations, is making it easier for researchers to update their names on past publications.
When the COVID-19 pandemic hit, travel bans and stay-at-home orders meant astrophysicists collaborating on the Dark Energy Survey needed to find a new way to conduct their observations using the Dark Energy Camera.
The U.S. Department of Energy awarded Farah Fahim an Early Career Research Award to investigate how deploying neural networks and machine learning on a particle detector can allow data processing at source. Her work could make data processing at detectors more efficient, improving fundamental research at physics facilities like the LHC at CERN.