9 papers
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…
Vistas: A Visualization Interface for Particle Collision Simulations
Benoit Assi, Christan Bierlich, Rikab Gambhir +7
We introduce Vistas, a tool for visualizing high-energy particle physics collisions simulated by the Pythia Monte-Carlo event generator. Vistas utilizes the browser-based event dis…
HDSense: An efficient method for ranking observable sensitivity
Benoît Assi, Christian Bierlich, Rikab Gambhir +6
Identifying which observables most effectively constrain model parameters can be computationally prohibitive when considering full likelihoods of many correlated observables. This…
The Pareto Frontier of Resilient Jet Tagging
Rikab Gambhir, Matt LeBlanc, Yuanchen Zhou
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting th…
Resummed Distribution Functions: Making Perturbation Theory Positive and Normalized
Rikab Gambhir, Radha Mastandrea
Fixed-order perturbative calculations for differential cross sections can suffer from non-physical artifacts: they can be non-positive, non-normalizable, and non-finite, none of wh…
A Search for "New Physics'' "Beyond the Standard Model'' in Open Data with Machine Learning
Rikab Gambhir
In this new era of large data, it is important to make sure we do not miss any signs of new physics. Using the publicly-available open data collected by the arXiv.org experiment in…