University of Cambridge > Talks.cam > Data Intensive Science Seminar Series > Searching for new physics with machine learning at the Large Hadron Collider

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Searching for new physics with machine learning at the Large Hadron Collider

Download to your calendar using vCal

If you have a question about this talk, please contact James Fergusson .

The Standard Model of particle physics has been incredibly successful at predicting the properties and interactions of the known fundamental particles. However, there are several major flaws with the model, one being the lack of explanation for dark matter. We know there is more to be discovered, and this is why the Large Hadron Collider – the world’s most powerful particle collider – was built at CERN . The ATLAS experiment is one of the main detectors at the LHC and collects petabytes of data each year. In this talk I will describe recent attempts to build automatic “structure-finding” models using tensor-attention networks, which will help tease out hints of rare signals of new particles from the deluge of background data.

This talk is part of the Data Intensive Science Seminar Series series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Fitzwilliam College | Auditorium Room E, West Hub Centre for Molecular Science Informatics

Other talks

Summer Cactus & Succulent Show Talk TBC Title to be confirmed Shape modes of CP^1 vortices Topology of the Ritz energy landscape Mergers all the way down: simulating the origin of globular clusters in a galactic context
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity