44 citations · 90 across the 20 of their papers we have counts for
5 papers · 1 filter
Residual ANODE
Ranit Das, Gregor Kasieczka, David Shih
We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability. The key to R-ANODE is to…
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
Joschka Birk, Erik Buhmann, Cedric Ewen +2
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizi…
Full Phase Space Resonant Anomaly Detection
Erik Buhmann, Cedric Ewen, Gregor Kasieczka +3
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strateg…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…
Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection
Thorben Finke, Marie Hein, Gregor Kasieczka +6
Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable perform…