Regularity issues in the problem of fluid structure interaction

We investigate the evolution of rigid bodies in a viscous incompressible fluid. The flow is governed by the 2D Navier-Stokes equations, set in a bounded domain with Dirichlet boundary conditions. The boundaries of the solids and the domain have H\"older regularity $C^{1, \alpha}$, $0 < \alpha \le 1$. First, we show the existence and uniqueness of strong solutions up to collision. A key ingredient is a BMO bound on the velocity gradient, which substitutes to the standard $H^2$ estimate for smoother domains. Then, we study the asymptotic behaviour of one $C^{1, \alpha}$ body falling over a flat surface. We show that collision is possible in finite time if and only if $\alpha < 1/2$

Improvement in fast particle track reconstruction with robust statistics

The IceCube project has transformed 1 km3 of deep natural Antarctic ice into a Cherenkov detector. Muon neutrinos are detected and their direction is inferred by mapping the light produced by the secondary muon track inside the volume instrumented with photomultipliers. Reconstructing the muon track from the observed light is challenging due to noise, light scattering in the ice medium, and the possibility of simultaneously having multiple muons inside the detector, resulting from the large flux of cosmic ray muons.This paper describes work on two problems: (1) the track reconstruction problem, in which, given a set of observations, the goal is to recover the track of a muon; and (2) the coincident event problem, which is to determine how many muons are active in the detector during a time window. Rather than solving these problems by developing more complex physical models that are applied at later stages of the analysis, our approach is to augment the detector's early reconstruction with data filters and robust statistical techniques. These can be implemented at the level of on-line reconstruction and, therefore, improve all subsequent reconstructions. Using the metric of median angular resolution, a standard metric for track reconstruction, we improve the accuracy in the initial reconstruction direction by 13%. We also present improvements in measuring the number of muons in coincident events: we can accurately determine the number of muons 98% of the time

The IceProd Framework: Distributed Data Processing for the IceCube Neutrino Observatory

IceCube is a one-gigaton instrument located at the geographic South Pole, designed to detect cosmic neutrinos, iden- tify the particle nature of dark matter, and study high-energy neutrinos themselves. Simulation of the IceCube detector and processing of data require a significant amount of computational resources. IceProd is a distributed management system based on Python, XML-RPC and GridFTP. It is driven by a central database in order to coordinate and admin- ister production of simulations and processing of data produced by the IceCube detector. IceProd runs as a separate layer on top of other middleware and can take advantage of a variety of computing resources, including grids and batch systems such as CREAM, Condor, and PBS. This is accomplished by a set of dedicated daemons that process job submission in a coordinated fashion through the use of middleware plugins that serve to abstract the details of job submission and job management from the framework