activity
20152025
most citedClassification without labels: Learning from mixed samples in high energy physics

297 citations · 2.4k across the 25 of their papers we have counts for

collaborators

26 papers

hep-ex2025

Unbinned measurement of thrust in collisions at = 91.2 GeV with ALEPH archived data

The Electron-Positron Alliance, :, Anthony Badea +17

The strong coupling constant () is a fundamental parameter of quantum chromodynamics (QCD), the theory of the strong force. Some of the earliest precise constraints on $α_{S…

hep-ex2025

Analysis note: measurement of thrust in collisions at = 91 GeV with archived ALEPH data

Anthony Badea, Austin Baty, Hannah Bossi +15

A measurement of the thrust distribution in collisions at GeV with archived data from the ALEPH experiment at the Large Electron-Positron Collider is…

hep-ph2023★ 2 cited

Anomaly Detection in Collider Physics via Factorized Observables

Eric M. Metodiev, Jesse Thaler, Raymond Wynne

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of mach…

hep-ph2023★ 8 cited

Pileup and Infrared Radiation Annihilation (PIRANHA): A Paradigm for Continuous Jet Grooming

Samuel Alipour-Fard, Patrick T. Komiske, Eric M. Metodiev +1

Jet grooming is an important strategy for analyzing relativistic particle collisions in the presence of contaminating radiation. Most jet grooming techniques introduce hard cutoffs…

stat.ML2021★ 20 cited

Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution

Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev +3

A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data. We pr…

hep-ph2021★ 42 cited

The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

Gregor Kasieczka, Benjamin Nachman, David Shih +44

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…