8 papers
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…
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…
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…
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, Benoît Assi, Kyle Lee +1
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…
Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
Rikab Gambhir, Radha Mastandrea, Benjamin Nachman +1
We present the first study of anti-isolated Upsilon decays to two muons () in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-…