113 citations · 229 across the 8 of their papers we have counts for
9 papers
Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum
Keith R. Dienes, Jessica N. Howard, Fei Huang +2
One effective way of learning about the production and properties of dark matter in the early universe is by extracting information about the primordial dark-matter phase-space dis…
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predicti…
Dark Matter Freeze-out during Confinement
Jessica N. Howard, Seyda Ipek, Tim M. P. Tait +1
We explore the possibility that dark matter is a pair of vector-like fermionic doublets and propose a novel mechanism of dark matter production that procee…
Learning to Simulate High Energy Particle Collisions from Unlabeled Data
Jessica N. Howard, Stephan Mandt, Daniel Whiteson +1
In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model pre…
Learning to Identify Electrons
Julian Collado, Jessica N. Howard, Taylor Faucett +3
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable informa…
How to GAN Higher Jet Resolution
Pierre Baldi, Lukas Blecher, Anja Butter +6
QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the res…