291 citations · 1.8k across the 21 of their papers we have counts for
3 papers · 1 filter
Energy Flow Networks: Deep Sets for Particle Jets
Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered…
An operational definition of quark and gluon jets
Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler
While "quark" and "gluon" jets are often treated as separate, well-defined objects in both theoretical and experimental contexts, no precise, practical, and hadron-level definition…
Learning to Classify from Impure Samples with High-Dimensional Data
Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman +1
A persistent challenge in practical classification tasks is that labeled training sets are not always available. In particle physics, this challenge is surmounted by the use of sim…