Limits on Electron Neutrino Disappearance from the KARMEN and LSND electron neutrino - Carbon Cross Section Data

This paper presents a combined analysis of the KARMEN and LSND nu_e-carbon cross section measurements within the context of a search for nu_e disappearance at high Delta m^2. KARMEN and LSND were located at 17.7 m and 29.8 m respectively from the neutrino source, so the consistency of the two measurements, as a function of antineutrino energy, sets strong limits on neutrino oscillations. Most of the allowed region from the nu_e disappearance analysis of the Gallium calibration data is excluded at >95% CL and the best fit point is excluded at 3.6$\sigma$. Assuming CPT conservation, comparisons are also made to the oscillation analyses of reactor antineutrino data.Comment: Published versio

Using Reactors to Measure θ13\theta_{13}

A next-generation neutrino oscillation experiment using reactor neutrinos could give important information on the size of mixing angle $\theta_{13}$. The motivation and goals for a new reactor measurement are discussed in the context of other measurements using off-axis accelerator neutrino beams. The reactor measurements give a clean measure of the mixing angle without ambiguities associated with the size of the other mixing angles, matter effects, and effects due to CP violation. The key question is whether a next-generation experiment can reach the needed sensitivity goals to make a measurement for $\sin^{2}2\theta_{13}$ at the 0.01 level. The limiting factors associated with a reactor disappearance measurement are described with some ideas of how sensitivities can be improved. Examples of possible experimental setups are presented and compared with respect to cost and sensitivity

Predictability and hierarchy in Drosophila behavior

Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent amongst the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested sub-clusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states