Speaker
Jubin Park
(Chonnam National University)
Description
Deep learning has been applied to many studies in high energy physics with substantial improvement over the traditional selection-cut methods. Based on deep-learning approaches, we perform a comprehensive signal-background analysis for Higgs-pair production in $HH \rightarrow b \bar{b} \gamma\gamma$ channel at the HL-LHC, with the goal of probing the self-coupling $\lambda_{3H}$ of the Higgs boson. We show that the multi-class classification using Deep Neural Network can indeed give better performance in disentangling signal and backgrounds.
Primary authors
Jung Chang
(National Center for Theoretical Sciences, Physics Division)
Prof.
Kingman Cheung
(National Tsing Hua University/Konkuk University)
Jae Sik Lee
(Chonnam National University)
Dr
Chih-Ting Lu
(National Tsing Hua University)
Jubin Park
(Chonnam National University)
