Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles. The effectiveness of these approaches in enhancing sensitivity to various signals is studied and compared using data collected in proton-proton collisions at a center-of-mass energy of 13 TeV. In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework.
One of the classical ways to look for undiscovered particles at the LHC is to look for unexpected resonances in the jets coming from quarks and gluons. This CMSPaper compares the cutting edge of #machinelearnining #ai methods to see how well they do for top quark resonances arxiv.org/abs/2512.20395
@JensJot and here is one of today's posts on bsky... no broken links https://bsky.app/profile/freyablekman.bsky.social/post/3mgv7vf72an2u
but I will keep an eye out for fediverse posts, there have been a lot of problems it seems with links the last week, some links to youtube were also 'eaten' recently
Freya Blekman
Jens Jäger