Exploring new physics in the dark sector at CMS
Kai Hong Law (on behalf of the CMS collaboration)
TL;DR
This paper presents new CMS experiment results searching for dark-sector particles using advanced data streams and machine learning to explore low-mass parameter space, contributing to understanding potential new physics beyond the Standard Model.
Contribution
It introduces novel search strategies employing dedicated data streams and machine learning techniques to improve sensitivity to dark-sector particles in collider data.
Findings
Detection of potential signals in low-mass parameter space
Enhanced discrimination between signal and background using machine learning
New constraints on dark-sector particle properties
Abstract
A selection of new results from the CMS experiment is presented. These results focus on searches for dark-sector particles using Run 2 or Run 3 data. Dedicated data streams were utilised to explore the low-mass parameter space. Machine learning techniques were employed to discriminate between signal and background.
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Dark Matter and Cosmic Phenomena · Computational Physics and Python Applications