8 citations · 12 across the 4 of their papers we have counts for
4 papers
Explicit or Implicit? Encoding Physics at the Precision Frontier
Victor Breso-Pla, Kevin Greif, Vinicius Mikuni +4
High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the st…
Finding Unexpected Non-Helical Tracks
Levi Condren, Daniel Whiteson
Many theories of physics beyond the Standard Model predict particles with non-helical trajectories in a uniform magnetic field, but standard tracking algorithms assume helical path…
Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
Phiala Shanahan, Kazuhiro Terao, Daniel Whiteson
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of…
Limiting SUSY compressed spectra scenarios
A. Nelson, P. Tanedo, D. Whiteson
Typical searches for supersymmetry cannot test models in which the two lightest particles have a small ("compressed") mass splitting, due to the small momentum of the particles pro…