In a machine learning challenge dubbed the 2020 Large Hadron Collider Olympics, a team of cosmologists from Berkeley Lab developed a code that best identified a mock signal hidden in simulated particle-collision data.
There wasn’t as much buzz about the particle physics applications of quantum computing when Amitabh Yadav began working on his master’s thesis in the field at Delft University of Technology in the Netherlands a couple of years ago, he recalled.
If you study the detector readout shortly after a particle collision at CERN’s Large Hadron Collider (LHC), “It looks like somebody fired a shotgun at a target,” said Eric Rohm, a physics researcher from the University of South Carolina who spent August 2019 to December 2019 working on a quantum-computing project at Berkeley Lab. With the planned upgrade of the LHC, this seemingly scattershot picture will only become more complicated.
Giant-scale physics experiments are increasingly reliant on big data and complex algorithms fed into powerful computers, and managing this multiplying mass of data presents its own unique challenges. To better prepare for this data deluge posed by next-generation upgrades and new experiments, physicists are turning to the fledgling field of quantum computing.
Lucy Linder grew up near CERN, the largest high-energy physics laboratory in the world, but during her youth she didn’t pay much attention to the science taking place there. Her academic pursuits, though, would steer her on a circuitous path that brought her close to home – and to the wide world of particle physics research at CERN.
The largest set of data yet from an underground experiment called CUORE sets more stringent limits on a theoretical ultra-rare particle process known as neutrinoless double-beta decay that could help to explain the abundance of matter over antimatter in the universe.
Quentin Riffard, a project scientist for the LUX-ZEPLIN dark matter detection experiment that is now being installed at the Sanford Underground Research Facility in Lead, South Dakota, shares his experiences in researching dark matter in this Q&A.
Last week, crews at the Sanford Underground Research Facility in South Dakota strapped the central component of LUX-ZEPLIN – the largest direct-detection dark matter experiment in the U.S. – below an elevator and s-l-o-w-l-y lowered it 4,850 feet down a shaft formerly used in gold-mining operations.
As reported in Nature Physics, a Berkeley Lab-led team of physicists and materials scientists was the first to unambiguously observe and document the unique optical phenomena that occur in certain types of synthetic materials called moiré superlattices. The new findings will help researchers understand how to better manipulate materials into light emitters with controllable quantum
This video and accompanying article highlight the decades of discoveries, achievements and progress in particle accelerator R&D at Berkeley Lab. These accelerators have enabled new explorations of the atomic nucleus; the production and discovery of new elements and isotopes, and of subatomic particles and their properties; created new types of medical imaging and treatments; and provided new insight into the nature of matter and energy, and new methods to advance industry and security, among other wide-ranging applications.