30 citations · 179 across the 19 of their papers we have counts for
6 papers · 1 filter
Normalizing Flows for High-Dimensional Detector Simulations
Florian Ernst, Luigi Favaro, Claudius Krause +2
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimete…
Semi-visible jets, energy-based models, and self-supervision
Luigi Favaro, Michael Krämer, Tanmoy Modak +2
We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive-learning approach to create observables that are approx…
Kicking it Off(-shell) with Direct Diffusion
Anja Butter, Tomas Jezo, Michael Klasen +3
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensi…
The MadNIS Reloaded
Theo Heimel, Nathan Huetsch, Fabio Maltoni +3
In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS…
Precision-Machine Learning for the Matrix Element Method
Theo Heimel, Nathan Huetsch, Ramon Winterhalder +2
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integratio…
Anomalies, Representations, and Self-Supervision
Barry M. Dillon, Luigi Favaro, Friedrich Feiden +2
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR tech…