machine learning

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.

From Wired, July 16, 2023: The use of a machine learning tool known as sparse convolutional neural network is now being used by researchers to accelerate real-time data analysis. SCNNs have been used in simulations of the data expected from DUNE and analyzed the simulated data faster than ordinary methods while requiring significantly less computational power.

Illustration of four scientists in white lab coats, two of whom are typing, two of whom are looking at and drawing on a screen with equations and 3D images.

Over time, particle physics and astrophysics and computing have built upon one another’s successes. That coevolution continues today. New physics experiments require computing innovation, including cluster computing for the Tevatron, and more recently machine learning and quantum problem-solving.

A photo of a woman with long, bright-orange hair, wearing sunglasses on top of her head and a light green T-shirt, smiling. Behind her, greenery.

Whether in Serbia or Chicago, Fermilab postdoctoral researcher Aleksandra Ćiprijanović is working to unlock the secrets of the night sky. As a member of the Deep Skies Lab, an international collaboration of physicists, she’s figuring out how to use artificial intelligence and machine learning to better handle the huge amounts of data needed for discovery science.

New amplification algorithms expand the utility of quantum computers to handle non-Boolean scenarios, allowing for an extended range of values to characterize individual records, such as the scores assigned to each disk in the output superposition above. Illustration: Prasanth Shyamsundar

To fully realize the potential of quantum computing, scientists must start with the basics: developing step-by-step procedures, or algorithms, for quantum computers to perform simple tasks. A Fermilab scientist has done just that, announcing two new algorithms that build upon existing work in the field to further diversify the types of problems quantum computers can solve.

These physicists comprise the LPC team that contributed to the supersymmetry analysis.

U.S. CMS physicists from Fermilab and associated universities collaborating under the umbrella of the LPC make up a team that is the first to perform a new kind of search for “stealthy” supersymmetry that does not result in an obvious signature of large energy imbalance. Instead, the LPC team is looking for collisions that result in an unusually large number of particles in the detector. CMS recently published a briefing explaining their analysis.