15 citations · 22 across the 5 of their papers we have counts for
5 papers
Learnable cut flow for high energy physics
Jing Li, Hao Sun
Neural networks have emerged as a powerful paradigm for tasks in high energy physics, yet their opaque training process renders them as a black box. In contrast, the traditional cu…
HEP ML Lab: An end-to-end framework for applying machine learning into phenomenology studies
Jing Li, Hao Sun
Recent years have seen the development and growth of machine learning in high energy physics. There will be more effort to continue exploring its full potential. To make it easier…
Probing Heavy Neutrinos at the LHC from Fat-jet using Machine Learning
Wei Liu, Jing Li, Zixiang Chen +1
We explore the potential to use machine learning methods to search for heavy neutrinos, from their hadronic final states including a fat-jet signal, via the processes $pp \rightarr…
Testing the seesaw mechanisms via displaced right-handed neutrinos from a light scalar at the HL-LHC
Wei Liu, Jiale Li, Jing Li +1
We investigate the pair production of right-handed neutrinos from the decay of a light scalar in the model. The scalar mixes to the SM Higgs, and the physi…
An Attention Based Neural Network for Jet Tagging
Jing Li, Hao Sun
Convolutional neural networks are basic structures using jet images as input for the jet tagging problems. However, what they have learned during the training process is always dif…