Applications of deep machine learning in collider physics

Cheng-Wei Chiang^1*, Yu-Chen Janice Chen¹, Giovanna Cottin¹, David Shih², Ting-Kuo Chen¹, Spencer Chang³

¹Physics, National Taiwan University, Taipei, Taiwan
²Physics, Rutgers University, Piscataway, New Jersey, USA
³Physics, University of Oregon, Eugene, Oregon, USA

* Presenter:Cheng-Wei Chiang, email:chengwei@phys.ntu.edu.tw

By way of examples, we discuss the applications of the modern deep machine learning technique in the study of collider physics phenomenology. More specifically, we construct taggers for boosted weak gauge bosons that decay hadronically. We also construct a neural network for studying properties of a new resonance in the s-channel Drell-Yan process. In such cases, we show the improvement or power that a neural network can achieve in comparison with traditional methods.

Keywords: deep machine learning, collider physics