Conveners
Tagger Resilience
- Vinicius Mikuni
- Ariel Schwartzman
Despite the recent proliferation of symmetry-based machine learning methods in jet physics, the preference for smaller symmetry groups and highly custom architectures negatively impacts explainability and generalizability. In this work, we present an update to our own algorithm, which delivers both significant improvements in the top-tagging performance and the capability to perform full...
Discrepancies between real and simulated collider events are a significant source of uncertainty in LHC physics, especially in the context of training machine learning models, where even small differences in input variable distributions can degrade the performance of a simulation-trained model. In this work we present a deep learning implementation of “chained quantile morphing”: a technique...
