Julia Gonski explains the long-established use of artificial intelligence and machine learning (AI/ML) in high-energy physics research and explores the exciting potential these technologies hold for the field.

In a new paper submitted to Phys. Rev. Lett., the ATLAS Collaboration pioneers the use of unsupervised machine learning to search for anomalous collision events which could be from new physics phenomena.

This week, ATLAS physicists presented four exciting new results about jet tagging using AI algorithms at the BOOST 2023 conference held at Lawrence Berkeley National Lab (USA).

The ATLAS Collaboration has released two new results explaining how detector timing measurements and calorimeter signal calibration using artificial intelligence (AI) are being used to further improve the quality of data recorded by the experiment.

On 31 March 2022 at 8pm CEST, Dr. David Rousseau will give a live public talk on the ATLAS Youtube Channel on the role artificial intelligence plays in particle physics research.

The ATLAS Collaboration is exploring novel ways to search for new phenomena. Alongside an extensive research programme often inspired by specific theoretical models – ranging from quantum black holes to supersymmetry – physicists are applying new model-independent methods to broaden their searches. ATLAS has just released the first model-independent search for new particles using a novel technique called “weak supervision”.

Cirta is a new machine-learning challenge for high-energy physics on Zindi, the Africa-based data-science challenge platform. Launched this autumn at the International Conference on High Energy and Astroparticle Physics (TIC-HEAP), Constantine, Algeria, Cirta challenges participants to provide machine-learning solutions for identifying particles in LHC experiment data.

Physicists from the ATLAS, CMS and LHCb collaborations have just launched the TrackML challenge – your chance to develop new machine learning solutions for the next generation of particles detectors.

The dataset from the ATLAS Higgs Machine Learning Challenge has been released on the CERN Open Data Portal.

The winner of the four-month long Higgs Machine Learning Challenge, launched on 12 May, is Gábor Melis from Hungary, followed closely by Tim Salimans from The Netherlands and Pierre Courtiol from France. They will receive cash prizes, sponsored by Paris-Saclay Centre for Data Science and Google, of $7000, $4000, and $2000 respectively. The three winners have been invited to participate at the Neural Information Processing Systems conference on 13 December in Canada.

It's been four weeks since the four-month long Higgs Machine Learning Challenge was announced. Almost 700 teams have signed up and more than 200 have beaten the in-house benchmark already.