ML4Jets2023

Session

Theory & Understanding

7 Nov 2023, 09:00

Seminarraum 4a/b (DESY)

Conveners

Theory & Understanding

• David Shih

Presentation materials

Zoom link

110. Learning a Representation of New Physics Models

Tore von Schwartz

07/11/2023, 09:00

In the world of particle physics experiments, we often deal with data lying in high-dimensional spaces. Tasks like navigating and comparing these data points become challenging, but can be simplified with dimensionality reduction methods. In this work, we develop a method for mapping data originating from both Standard Model processes and various theories Beyond the Standard Model into a...

45. Anatomy of Jet classification using deep learning

Sung Hak Lim (Rutgers University)

07/11/2023, 09:15

State-of-the-art (SoTA) deep learning models have achieved tremendous improvements in jet classification performance while analyzing low-level inputs, but their decision-making processes have become increasingly opaque. We introduce an analysis model (AM) that combines several phenomenologically motivated neural networks to circumvent the interpretability issue while maintaining high...

7. Hyperbolic Machine Learning for Jet Physics

Nathaniel Sherlock Woodward (Massachusetts Inst. of Technology (US))

07/11/2023, 09:30

Particle jets exhibit tree-like structures through stochastic showering and hadronization. The hierarchical nature of these structures aligns naturally with hyperbolic space, a non-Euclidean geometry that captures hierarchy intrinsically. Drawing upon the foundations of geometric learning, we introduce hyperbolic transformer models tailored for tasks relevant to jet analyses, such as...