5 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
Machine learning for the LHC physics program: a 2025-2026 stocktake
Jesse Thaler
The first sentence of this abstract--and the introduction to these proceedings--was authored by a human, but the bulk of this document was generated by an agentic AI system. In thi…
Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
Gregorio de la Fuente, Jesse Thaler
Providing a practical and hadron-level definition of multiple jet flavors has been a long-standing challenge in collider physics. Previous work has introduced a data-driven, operat…
Reweighting Adversarial Networks for Unbinned Unfolding
Umar Sohail Qureshi, Krish Desai, Jesse Thaler +1
Differential cross sections are the currency of scientific exchange in particle and nuclear physics. Recently, machine learning methods have enabled unbinned and high-dimensional c…
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler
We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning the structural transparency of…
Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference
Ezequiel Alvarez, Sean Benevedes, Manuel Szewc +1
In particle physics, as in many areas of science, parameter inference relies on simulations to bridge the gap between theory and experiment. Recent developments in simulation-based…
Improving parton shower predictions via precision moments of energy flow polynomials
Benoît Assi, Kyle Lee, Jesse Thaler
We study conceptual and practical aspects of using maximum-entropy reweighting to upgrade parton-shower event samples with higher-accuracy theoretical constraints. Our approach pro…