arXiv subjects
Publications and source records attributed to Katherine Holcomb.
We present and discuss a new method to extract parton distribution functions from hard scattering processes based on an alternative type of neural network, the Self-Organizing Map. Quantitative results including a detailed treatment of uncertainties are presented within a Next to Leading Order analysis of inclusive electron proton deep inelastic scattering data.
We describe a new method to extract parton distribution functions both in the unpolarized and the polarized case, based on a type of neural networks, the Self-Organizing Maps. Initial quantitative results of our Next to Leading Order analysis are presented for the unpolarized case.
We investigate the role of various structural nonidealities on the performance of armchair-edge graphene nanoribbon field effect transistors (GNRFETs). Our results show that edge roughness dilutes the chirality dependence often predicted by theory but absent experimentally. Instead, GNRs are classifiable into wide (semi-metallic) vs narrow (semiconducting) strips, defining thereby the building blocks for wide-narrow-wide all-graphene devices and interconnects. Small bandgaps limit drain bias at the expense of band-to-band tunneling in GNRFETs. We outline the relation between device performance metrics and non-idealities such as width modulation, width dislocations and surface step, and non-ideality parameters such as roughness amplitude and correlation length.