Speaker
Description
Jet constituents provide a more detailed description of the radiation pattern within a jet compared to observables summarizing global jet properties. In Run 2 analyses at the LHC using the ATLAS detector, transformer-based taggers leveraging low-level variables outperformed traditional approaches based on high-level variables and conventional neural networks in distinguishing quark- and gluon-initiated jets. With the upcoming High-Luminosity LHC (HL-LHC) era, characterized by higher luminosity and increased center-of-mass energy, the ATLAS detector has undergone significant upgrades. This includes a new inner detector with extended coverage into the most forward region, previously unexplored with tracks. This study assesses how these advancements enhance jet tagger accuracy and robustness. These improvements are crucial for processes such as vector boson fusion, vector boson scattering, and supersymmetry, where precise jet identification enhances background discrimination.
| AI keywords | Transformers; Low-level feature learning; Neural network architectures; Jet tagging with AI |
|---|
