Neutrino oscillation physics at an upgraded CNGS with large next generation liquid Argon TPC detectors

The determination of the missing $U_{e3}$ element (magnitude and phase) of the PMNS neutrino mixing matrix is possible via the detection of \numu\to\nue oscillations at a baseline $L$ and energy $E$ given by the atmospheric observations, corresponding to a mass squared difference $E/L \sim \Delta m^2\simeq 2.5\times 10^{-3} eV^2$. While the current optimization of the CNGS beam provides limited sensitivity to this reaction, we discuss in this document the physics potential of an intensity upgraded and energy re-optimized CNGS neutrino beam coupled to an off-axis detector. We show that improvements in sensitivity to $\theta_{13}$ compared to that of T2K and NoVA are possible with a next generation large liquid Argon TPC detector located at an off-axis position (position rather distant from LNGS, possibly at shallow depth). We also address the possibility to discover CP-violation and disentangle the mass hierarchy via matter effects. The considered intensity enhancement of the CERN SPS has strong synergies with the upgrade/replacement of the elements of its injector chain (Linac, PSB, PS) and the refurbishing of its own elements, envisioned for an optimal and/or upgraded LHC luminosity programme.Comment: 37 pages, 20 figure

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte

Nucleon Decay Searches with large Liquid Argon TPC Detectors at Shallow Depths: atmospheric neutrinos and cosmogenic backgrounds

Grand Unification of the strong, weak and electromagnetic interactions into a single unified gauge group is an extremely appealing idea which has been vigorously pursued theoretically and experimentally for many years. The detection of proton or bound-neutron decays would represent its most direct experimental evidence. In this context, we studied the physics potentialities of very large underground Liquid Argon Time Projection Chambers (LAr TPC). We carried out a detailed simulation of signal efficiency and background sources, including atmospheric neutrinos and cosmogenic backgrounds. We point out that a liquid Argon TPC, offering good granularity and energy resolution, low particle detection threshold, and excellent background discrimination, should yield very good signal over background ratios in many possible decay modes, allowing to reach partial lifetime sensitivities in the range of 1034−1035 years with exposures up to 1000 kton×year, often in quasi-background-free conditions optimal for discoveries at the few events level, corresponding to atmospheric neutrino background rejections of the order of 105. Multi-prong decay modes like e.g. p→μ−π+K+ or p→e+π+π− and channels involving kaons like e.g. p→K+ν¯, p→e+K0 and p→μ+K0 are particularly suitable, since liquid Argon imaging (...)This work was in part supported by ETH and the Swiss National Foundation. AB, AJM and SN have been supported by CICYT Grants FPA-2002-01835 and FPA-2005-07605-C02-01. SN acknowledges support from the Ramon y Cajal Programme. We thank P. Sala for help with FLUKA while she was an ETH employee

Making 'informed choices' in antenatal care

In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this problem, so far none of them were particularly tight. In this paper, we show how to efficiently calculate a lower bound, which corresponds to the utility of a near-optimal memoryless policy for the decision problem, which is generally different from both the Bayes-optimal policy and the policy which is optimal for the expected MDP under the current belief. We then show how these can be applied to obtain robust exploration policies in a Bayesian reinforcement learning setting.Comment: Corrected version. 12 pages, 3 figures, 1 tabl