CMS

From CERN, Oct. 15, 2019: A new result by the CMS Collaboration narrows down the mass of the Higgs boson to a precision of 0.1%. After reporting the observation of the Higgs boson at the CERN LHC in 2012, scientists the ATLAS and CMS collaborations have been busy understanding exactly its place within the standard model of particle physics. Any straying from expectations could be an indication of new physics.

From CERN, Oct. 7, 2019: The CMS collaboration has measured for the first time the variation, or “running,” of the top quark mass. The theory of quantum chromodynamics predicts this energy-scale variation for the masses of all quarks and for the strong force acting between them. Observing the running masses of quarks can therefore provide a way of testing quantum chromodynamics and the Standard Model.

The Big Bang Science Fair brings science communication and outreach to an arts festival in Rhode Island. The event is filled with presentations, workshops and hands-on activities covering a wide range of scientific disciplines. It makes its second appearance later this month.

Working on hardware doesn’t come easily to all physicists, but Francesca Ricci-Tam has learned that what matters most is a willingness to put in the practice.

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.

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.

From Texas Tech Today, Aug. 5, 2019: Texas Tech physicists have been looking for dark matter at the CMS experiment at CERN and studying neutrinos.

Tran’s $2.5 million award will fund the development of new ways to handle the massive amounts of data that particle physics produces and to expand investigations of Higgs boson physics.

A pioneer in particle physics and high-performance computing, Fermilab has launched HEPCloud, a cloud computing service that will enable the lab’s demanding experiments to make the best, most efficient use of computing resources. This flagship project lets experiments rent computing resources from external sources during peak demand, reducing the costs of providing for local resources while also providing failsafe redundancy.