Publications (7)
Experimental signatures of the quantum-classical transition in a nanomechanical oscillator modeled as a damped driven double-well problem
Qi Li, Arie Kapulkin, Dustin Anderson +2
We demonstrate robust and reliable signatures for the transition from quantum to classical behavior in the position probability distribution of a damped double-well system using th…
Novel deep learning methods for track reconstruction
Steven Farrell, Paolo Calafiura, Mayur Mudigonda +11
For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew…
Dynamical complexity in the quantum to classical transition
Bibek Pokharel, Peter Duggins, Moses Misplon +5
We study the dynamical complexity of an open quantum driven double-well oscillator, mapping its dependence on effective Planck's constant and coupling to the…
Topology classification with deep learning to improve real-time event selection at the LHC
Thong Q. Nguyen, Daniel Weitekamp, Dustin Anderson +5
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consi…
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by a…
Multiscale Dynamics in Communities of Phase Oscillators
Dustin Anderson, Ari Tenzer, Gilad Barlev +3
We investigate the dynamics of systems of many coupled phase oscillators with het- erogeneous frequencies. We suppose that the oscillators occur in M groups. Each oscillator is con…