15th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements, and Searches at Colliders

Session

Tagger Resilience

1 Aug 2023, 16:00

Auditorium (50)

Conveners

Tagger Resilience

• Vinicius Mikuni
• Ariel Schwartzman

79. Dark Sector Showers in the Lund Jet Plane

Jennifer Roloff

01/08/2023, 16:00

In this talk, we will discuss the consequences of models where dark sector quarks could be produced at the LHC, which subsequently undergo a dark parton shower, generating jets of dark hadrons that ultimately decay back to Standard Model hadrons. This yields collider objects that can be nearly indistinguishable from Standard Model jets, motivating the reliance on substructure observables to...

84. Minimizing systematic uncertainties in jet tagging using contrastive learning

Jeffrey Krupa (MIT)

01/08/2023, 16:20

Jet taggers based on substructure are exploited in many areas of the LHC physics program. The ability to select jets stemming from different incident particles, so-called jet tagging, is afflicted with systematic uncertainties of both theoretical and experimental nature. This results in taggers that are ultimately less powerful than they could be. We propose an algorithm that creates a feature...

115. PELICAN: Equivariance and Explainability in Jet ML

Jan Offermann (University of Chicago), Timothy Hoffman (University of Chicago)

01/08/2023, 16:40

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...

98. Chained Quantile Morphing with Normalizing Flows

Sam Bright-Thonney (Cornell University)

01/08/2023, 17:00

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...