70 citations · 115 across the 7 of their papers we have counts for
5 papers · 1 filter
Bridging the divide: axion searches and axino phenomenology at colliders
Gabe Hoshino, Kristin Dona, Keisuke Harigaya +4
We discuss a phenomenological model that extends the minimal supersymmetric standard model to contain axions and their supersymmetric partner, the axino. In the supersymmetric DFSZ…
19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
Alexander Bogatskiy, Timothy Hoffman, Jan T. Offermann
As particle accelerators increase their collision rates, and deep learning solutions prove their viability, there is a growing need for lightweight and fast neural network architec…
Explainable Equivariant Neural Networks for Particle Physics: PELICAN
Alexander Bogatskiy, Timothy Hoffman, David W. Miller +2
PELICAN is a novel permutation equivariant and Lorentz invariant or covariant aggregator network designed to overcome common limitations found in architectures applied to particle…
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
Alexander Bogatskiy, Timothy Hoffman, David W. Miller +1
Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limi…
Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann +3
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The…