by Mr Xuhui JIANG (Hong Kong University of Science and Technology)

Asia/Shanghai
Meeting Room N602 (Tsung-Dao Lee Institute)

Meeting Room N602

Tsung-Dao Lee Institute

Description

Abstract:

Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. We develop an analysis scheme that exploits the complementarity between isolation-based and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a tt¯γγtt¯γγ final state: tt¯htt¯h, giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, resulting in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for detecting both signals are found to be encouraging.

Biography:

Mr. Xuhui JIANG obtained his bachelor degree of physics in Nanjing University in 2017 and now is a final year PhD candidate affiliated to the Hong Kong University of Science and Technology. His research interests include collider phenomenology, machine learning, flavor physics and etc.

Online Meeting Room: https://meeting.tencent.com/dm/ZTrJiEvCpvlq

ID: 513-359-776​  Password:123456