Analysis of a diffusive effective mass model for nanowires

We propose in this paper to derive and analyze a self-consistent model describing the diffusive transport in a nanowire. From a physical point of view, it describes the electron transport in an ultra-scaled confined structure, taking in account the interactions of charged particles with phonons. The transport direction is assumed to be large compared to the wire section and is described by a drift-diffusion equation including effective quantities computed from a Bloch problem in the crystal lattice. The electrostatic potential solves a Poisson equation where the particle density couples on each energy band a two dimensional confinement density with the monodimensional transport density given by the Boltzmann statistics. On the one hand, we study the derivation of this Nanowire Drift-Diffusion Poisson model from a kinetic level description. On the other hand, we present an existence result for this model in a bounded domain

Optimal operation of cryogenic calorimeters through deep reinforcement learning

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to light dark matter-nucleus scattering in current direct detection dark matter searches. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three CRESST detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.Comment: 23 pages, 14 figures, 2 table

ANISOTROPIC MESH ADAPTION GOVERNED BY A HESSIAN MATRIX METRIC

ABSTRACT An essential task for any finite element method is to provide appropriate resolution of the mesh to resolve the initial solution. We present a computational method for anisotropic tetrahedral mesh refinement according to an adjustable discretization error. The initial attribute profile is given by an analytical function which is twice continously differentiable. Anisotropy is taken into account to reduce the amount of elements compared to pure isotropic meshes. By the proposed method the spatial resolution in three-dimensional unstructured tetrahedral meshes used for diffusion simulation is locally increased and the accuracy of the discretization improved