I may being studying mostly math in college, but that doesn’t mean I don’t like other fields of science; on the contrary I love physics because it’s so interesting how mathematics (which at times can seem very detached from reality) can model complex real life processes.

Last Thursday I went to a colloquium about CERN and particle physics at UCLA (very last minute idea, was informed about it by my physics professor literally 3 minutes before the event: link here). It was AMAZING, and after talking to Thomas Müller and the other awesome professors about the discoveries made by CERN over the last 70 years and how computing power has accelerated discoveries, I wanted to share a little from that event.

A (not so) Brief History of CERN

Organisation européenne pour la recherche nucléaire, or CERN for short, is a particle physics laboratory located on the France-Switzerland border. It originally was intended to study atomic nuclei, but over the years has been focused on particle physics, including discovering particles in the Standard Model, like the Higgs boson in 2012.

Standard Model of Elementary Particles

CERN is most known for their accelerator complex, which can accelerate particles near light speed and force them to collide with each other, allowing for particle physics experiments to be conducted.

CERN accelerator complex

CERN also helped propel technologies used in the modern internet, like TCP/IP, and the World Wide Web, which wanted to use hypertext to facilitate information sharing.

The Uses of Artificial Intelligence in Particle Physics Research

CERN’s Large Hadron Collider (LHC) generates 40 million particle collisions per second, producing over 1 petabyte of data daily. This has often required the use of various supercomputers, but as to handle this deluge, researchers now also deploy AI and edge computing systems that make real-time decisions about which collisions to analyze-akin to finding needles in a cosmic haystack.

AI’s transformative role

  • Pattern recognition: Machine learning algorithms detect rare Higgs boson interactions 100x faster than classical methods, enabling studies of its self-coupling-a key to understanding why particles have mass.

  • Edge AI: Custom chips developed with Ceva reduce energy use by 90% while processing collision data locally in microseconds, avoiding cloud latency.

  • Assisting Scientists in Open-ended Discovery: Unlike traditional hypothesis-driven approaches, AI systems at CERN can now scour data for anomalies that could reveal dark matter or extra dimensions.

Extra Credit: Quantum Computing in Particle Physics Research

With LHC upgrades planned for the 2030s, quantum computing is being tested to tackle calculations like collision cross-sections. Early experiments show quantum support vector machines can solve integration problems 15x faster than classical supercomputers. While still experimental, these methods could eventually reduce annual CPU usage by billions of hours.

Conclusion

CERN exemplifies how abstract mathematics becomes concrete through experimental validation. From uncovering the Higgs mechanism to pioneering distributed computing, this institution continues pushing boundaries in both physics and technology. As AI and quantum systems mature, they’ll likely reveal deeper connections between mathematical models and physical reality – perhaps even answering why our universe has mass at all.