machine learning

A new telescope will take a sequence of snapshots with the world’s largest digital camera, covering the entire visible night sky every few days — and repeating the process for an entire decade. What’s the best way to rapidly and automatically identify and categorize all of the stars, galaxies and other objects captured in these images? Data scientists trained have computers to pick out useful information from these hi-res snapshots of the universe.

Fermilab’s quantum program includes a number of leading-edge research initiatives that build on the lab’s unique capabilities as the U.S. center for high-energy physics and a leader in quantum physics research. On the tour, researchers discussed quantum technologies for communication, high-energy physics experiments, algorithms and theory, and superconducting qubits hosted in superconducting radio-frequency cavities.

From Spektrum, Nov. 2, 2018: Maschinelles Lernen hat bereits bei der Entdeckung des Higgs einen wesentlichen Beitrag geleistet. Teilchenphysiker setzen Verfahren aus diesem Bereich schon seit Jahrzehnten ein. Doch nun erwarten Experten durch lernende Software eine Revolution bei der Datenanalyse.

To keep up with an impending astronomical increase in data about our universe, astrophysicists turn to machine learning.

Recently, the MicroBooNE experiment published a paper describing how they used convolutional neural networks — a particular type of deep neural network — to sort individual pixels coming from images made by a particular type of detector.

A new review in Nature chronicles the many ways machine learning is popping up in particle physics research.