23 citations · 56 across the 5 of their papers we have counts for
5 papers
A High- and Variable-Dimensional Measurement of the +jets Differential Cross Section with the ATLAS Experiment and Artificial Intelligence
Kevin Greif
Proton-proton collisions at the Large Hadron Collider (LHC) offer the opportunity to observe the interactions of fundamental particles at very high energy scales. The high collisio…
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…
The Landscape of Unfolding with Machine Learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov +9
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new…
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
Alexander Shmakov, Kevin Greif, Michael James Fenton +3
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, stati…
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
Alexander Shmakov, Kevin Greif, Michael Fenton +3
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before meas…