20 citations · 40 across the 3 of their papers we have counts for
7 papers · 1 filter
Parameter Estimation using Neural Networks in the Presence of Detector Effects
Anders Andreassen, Shih-Chieh Hsu, Benjamin Nachman +2
Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators. Parametrized neural network reweighting can be us…
Simulation Assisted Likelihood-free Anomaly Detection
Anders Andreassen, Benjamin Nachman, David Shih
Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches ha…
OmniFold: A Method to Simultaneously Unfold All Observables
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev +2
Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments. Unfolding is traditiona…
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Anders Andreassen, Benjamin Nachman
Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. The significan…
Binary JUNIPR: an interpretable probabilistic model for discrimination
Anders Andreassen, Ilya Feige, Christopher Frye +1
JUNIPR is an approach to unsupervised learning in particle physics that scaffolds a probabilistic model for jets around their representation as binary trees. Separate JUNIPR models…
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
Anders Andreassen, Ilya Feige, Christopher Frye +1
In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics. To this end, a powerful to…