Learning to identify semi-visible jets

Journal of High Energy Physics

Regular Article - Theoretical Physics

Daniel Whiteson¹  Taylor Faucett¹  Shih-Chieh Hsu² 

[1] Department of Physics and Astronomy, University of California, Irvine, CA, USA;Department of Physics, University of Washington, Seattle, WA, USA;

关键词: Dark Matter at Colliders;    Jets and Jet Substructure;   

DOI  :  10.1007/JHEP12(2022)132

 received in 2022-08-23, accepted in 2022-11-21,  发布年份 2022

来源: Springer

【 摘 要 】

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-pT jet constituents.

【 授权许可】

Unknown   
© The Author(s) 2022

【 图 表 】

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