1.2k citations · 4.5k across the 78 of their papers we have counts for
20 papers · 1 filter
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…
Deep-Learning-Based Kinematic Reconstruction for DUNE
Junze Liu, Jordan Ott, Julian Collado +4
In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering, and the octant of remain unknown. The Deep Underground Neutrino Expe…
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…
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging
Stephen McAleer, Alex Fast, Yuntian Xue +5
Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to about one millimeter in thickness. Typically, reducing fluorescence excitation exposure reduces the quality…
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
Griffin Mooers, Mike Pritchard, Tom Beucler +4
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Par…
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Michael James Fenton, Alexander Shmakov, Ta-Wei Ho +3
Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques. The most common decay mode, the…