47 citations · 49 across the 4 of their papers we have counts for
4 papers
Know What You Don't Flow
Anja Butter, Sascha Diefenbacher, Tilman Plehn +1
Calibrated learned uncertainties are a key requirement also for generative neural networks in LHC physics. For a toy model with an explicit likelihood we show how a heteroscedastic…
NAE, Statistically
Ranit Das, Jonathan Ostertag-Henning, Tilman Plehn +1
Searches for new physics using neural anomaly scores have transformative potential, but suffer from a lack of statistical interpretability. The normalized autoencoder (NAE) provide…
Performance versus Resilience in Modern Quark-Gluon Tagging
Anja Butter, Barry M. Dillon, Tilman Plehn +1
Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show ho…
Symmetries, Safety, and Self-Supervision
Barry M. Dillon, Gregor Kasieczka, Hans Olischlager +3
Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and…