4 papers
Potential of di-Higgs observation via a calibratable jet-free framework
Tianyi Yang, Congqiao Li
We present a calibratable, jet-free framework that enhances the search significance of the flagship LHC channel by more than a factor of five compared to existing appro…
Novel extraction method via boosted -tagging with in-situ calibration
Yuzhe Zhao, Congqiao Li, Antonios Agapitos +4
We present a novel method for measuring at the LHC using an advanced boosted-jet tagger to identify " signatures". By associating boosted signals…
Jet Tagging with More-Interaction Particle Transformer
Yifan Wu, Kun Wang, Congqiao Li +2
In this study, we introduce the More-Interaction Particle Transformer (MIParT), a novel deep learning neural network designed for jet tagging. This framework incorporates our own d…
Accelerating Resonance Searches via Signature-Oriented Pre-training
Congqiao Li, Antonios Agapitos, Jovin Drews +9
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging signifi…