Global Optimization Of Quasi-Monoenergetic Electron Beams From Laser Wakefield Accelerators

We globally optimize a terawatt-laser-driven wakefield accelerator by systematically varying laser and target parameters to achieve 100 MeV electrons, 10% energy spread, 100 pC charge, 4 mrad divergence and 10 mrad pointing fluctuation with similar to 100% reproducibility, thereby meeting conditions for producing similar to 10(6) 200 keV X-ray photons/pulse by inverse Compton scatter.Physic

Zero Temperature Insulator-Metal Transition in Doped Manganites

We study the transition at T=0 from a ferromagnetic insulating to a ferromagnetic metallic phase in manganites as a function of hole doping using an effective low-energy model Hamiltonian proposed by us recently. The model incorporates the quantum nature of the dynamic Jahn-Teller(JT) phonons strongly coupled to orbitally degenerate electrons as well as strong Coulomb correlation effects and leads naturally to the coexistence of localized (JT polaronic) and band-like electronic states. We study the insulator-metal transition as a function of doping as well as of the correlation strength U and JT gain in energy E_{JT}, and find, for realistic values of parameters, a ground state phase diagram in agreement with experiments. We also discuss how several other features of manganites as well as differences in behaviour among manganites can be understood in terms of our model.Comment: To be published in Europhysics Letter

Modeling programming knowledge for mentoring at scale

In large programming classes, MOOCs or online communities, it is challenging to find peers and mentors to help with learning specific programming concepts. In this paper we present first steps towards an automated, scalable system for matching learners with Python programmers who have expertise in different areas. The learner matching system builds a knowledge model for each programmer by analyzing their authored code and extracting features that capture domain knowledge and style. We demonstrate the feasibility of a simple model that counts the references to modules from the standard library and Python Package Index in a programmers' code. We also show that programmers exhibit self-selection using which we can extract the modules a programmer is best at, even though we may not have all of their code. In our future work we aim to extend the model to encapsulate more features, and apply it for skill matching in a programming class as well as personalizing answers on StackOverflow.Massachusetts Institute of Technology. Undergraduate Research Opportunities Progra