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.
Don’t know your convolutional neural networks from your boosted decision trees? Symmetry is here to help.
Physicists work with computer scientists in academia and industry to advance 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.
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?
Scientists inside and outside of particle physics and astrophysics are leaning on AI for assistance with complex tasks.
Physics may seem like its own world, but different sectors using machine learning are all part of the same universe.
Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it?
