Ryan Sheppard | Searcharxiv

arXiv2022

A Neural Network Model of a Quasi-Periodic Elliptically Polarizing Undulator in Universal Mode

Machine learning has recently been applied and deployed at several light source facilities in the domain of Accelerator Physics. We introduce an approach based on machine learning to produce a fast-executing model that predicts the polarization and energy of the radiated light produced at an insertion device. This paper demonstrates how a machine learning model can be trained on simulated data and later calibrated to a smaller, limited measured data set, a technique referred to as transfer learning. This result will enable users to efficiently determine the insertion device settings for achieving arbitrary beam characteristics.

physics.acc-ph↗

arXiv2018

Strange metallicity in the doped Hubbard model

Strange or bad metallic transport, defined by its incompatibility with conventional quasiparticle pictures, is a theme common to strongly correlated materials and ubiquitous in many high temperature superconductors. The Hubbard model represents a minimal starting point for modeling strongly correlated systems. Here we demonstrate strange metallic transport in the doped two-dimensional Hubbard model using determinantal quantum Monte Carlo calculations. Over a wide range of doping, we observe resistivities exceeding the Mott-Ioffe-Regel limit with linear temperature dependence. The temperatures of our calculations extend to as low as 1/40 the non-interacting bandwidth, placing our findings in the degenerate regime relevant to experimental observations of strange metallicity. Our results provide a foundation for connecting theories of strange metals to models of strongly correlated materials.

cond-mat.str-el↗