22-30 July 2021
North-West University
Africa/Johannesburg timezone
More Information Coming Soon

Machine learning approach for the search of resonances with topological features at the Large Hadron Collider

30 Jul 2021, 12:30
15m
Potchefstroom Campus (North-West University)

Potchefstroom Campus

North-West University

Oral Presentation Track B - Nuclear, Particle and Radiation Physics Nuclear, Particle and Radiation Physics

Speaker

Salah-eddine Dahbi (University of Wits)

Description

We propose a new approach to search for new resonances beyond the Standard Model (SM) of particle physics in topological configurations using Machine Learning techniques. This involves a novel classification procedure based on a combination of weak-supervision and full-supervision in conjunction with Deep Neural Network algorithms. The performance of this strategy is evaluated on the production of SM Higgs boson decaying to a pair of photons inclusively and exclusive regions of phase space, for specific production modes at the Large Hadron Collider (LHC), namely through the gluon-gluon fusion, the fusion of weak vector bosons, in associated production with a weak vector boson, or in association with a pair of top quarks. After verifying the ability of the methodology to extract different Higgs signal mechanisms, a search for new phenomena in high-mass diphoton final states is setup for the LHC.

Apply to be considered for a student ; award (Yes / No)?

Yes

Level for award;(Hons, MSc, PhD, N/A)?

PhD

Primary author

Salah-eddine Dahbi (University of Wits)

Co-authors

Benjamin Lieberman (University of Witwatersrand) Bruce Mellado (University of the Witwatersrand) Gaogalalwe Mokgatitswane (University of the Witwatersrand (ZA)) Mr Joshua Choma (University of the Witwatersrand) XIFENG RUAN (University of the witwatersrand)

Presentation Materials

Peer reviewing

Paper