6,573 research outputs found

    SoLid : Search for Oscillations with Lithium-6 Detector at the SCK-CEN BR2 reactor

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    Sterile neutrinos have been considered as a possible explanation for the recent reactor and Gallium anomalies arising from reanalysis of reactor flux and calibration data of previous neutrino experiments. A way to test this hypothesis is to look for distortions of the anti-neutrino energy caused by oscillation from active to sterile neutrino at close stand-off (similar to 6-8m) of a compact reactor core. Due to the low rate of anti-neutrino interactions the main challenge in such measurement is to control the high level of gamma rays and neutron background. The SoLid experiment is a proposal to search for active-to-sterile anti-neutrino oscillation at very short baseline of the SCK center dot CEN BR2 research reactor. This experiment uses a novel approach to detect anti-neutrino with a highly segmented detector based on Lithium-6. With the combination of high granularity, high neutron-gamma discrimination using 6LiF:ZnS(Ag) and precise localization of the Inverse Beta Decay products, a better experimental sensitivity can be achieved compared to other state-of-the-art technology. This compact system requires minimum passive shielding allowing for very close stand off to the reactor. The experimental set up of the SoLid experiment and the BR2 reactor will be presented. The new principle of neutrino detection and the detector design with expected performance will be described. The expected sensitivity to new oscillations of the SoLid detector as well as the first measurements made with the 8 kg prototype detector deployed at the BR2 reactor in 2013-2014 will be reported

    Validating module network learning algorithms using simulated data

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    In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio
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