Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries?
For more than 20 years in experimental particle physics and astrophysics, machine learning has been accelerating the pace of science, helping scientists tackle problems of greater and greater complexity.
In the coming weeks, Symmetry will explore the ways scientists are using artificial intelligence to advance particle physics and astrophysics—in a series of articles written and illustrated entirely by humans.
With just its first year of data, DESI has surpassed all previous 3D spectroscopic maps combined and confirmed the basics of our best model of the universe.
Once set in place atop a telescope in Chile, the 3,200-megapixel LSST Camera will help researchers better understand dark matter, dark energy and other mysteries of our universe.
The Particle Physics Project Prioritization Panel recently recommended, among their top priorities for the next decade, moving forward with two experiments based at the South Pole.
Berkeley Lab researchers are developing an approach to avoid sudden, potentially destructive energy releases in a new generation of superconducting magnets.
Particle Mysteries, a podcast released by the Interactions Collaboration, illuminates the international search for dark matter through conversations with its inquirers.
A scientist tried using machine-learning techniques from particle physics to analyze data from astronomy—and in the process discovered a new exoplanet.
In India, scientists are building a new LIGO detector, enhancing the capabilities of the observatory that reported the first observation of gravitational waves.
