Abstract: A theta (13) oscillation analysis based on the observed antineutrino rates at the Double Chooz far and near detectors for different reactor power conditions is presented. This approach provides a so far unique simultaneous determination of theta (13) and the total background rates without relying on any assumptions on the specific background contributions. The analysis comprises 865 days of data collected in both detectors with at least one reactor in operation. The oscillation results are enhanced by the use of 24.06 days (12.74 days) of reactor-off data in the far (near) detector. The analysis considers the nu <mml:mo stretchy=“true”><overbar></mml:mover>e interactions up to a visible energy of 8.5 MeV, using the events at higher energies to build a cosmogenic background model considering fast-neutrons interactions and Li-9 decays. The background-model-independent determination of the mixing angle yields sin(2)(2 theta (13)) = 0.094 0.017, being the best-fit total background rates fully consistent with the cosmogenic background model. A second oscillation analysis is also performed constraining the total background rates to the cosmogenic background estimates. While the central value is not significantly modified due to the consistency between the reactor-off data and the background estimates, the addition of the background model reduces the uncertainty on theta (13) to 0.015. Along with the oscillation results, the normalization of the anti-neutrino rate is measured with a precision of 0.86%, reducing the 1.43% uncertainty associated to the expectation.

Abstract: Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.

Abstract: We analyze the D (D) over bar mass distribution from a recent Belle experiment on the e(+)e(-) -> J/.D (D) over bar reaction, and show that the mass distribution divided by phase sp(c)e does not have a clear peak above the D (D) over bar threshold that justifies the experimental claim of chi(c0)(2P) state from those data. Then we use a unitary formalismwith coupled channels D+ D-, D-0 (D) over bar (0), D-s(D) over bar (s), and eta eta, with some of the interactions taken from a theoretical model, and use the data to fix other parameters. We then show that, given the poor quality of the data, we can get different fits leading to very different D (D) over bar amplitudes, some of them supporting a D (D) over bar bound state and others not. The main conclusion is that the claim for the chi(c0)(2P) state, already included in the PDG, is premature, but refined data can provide very valuable information on the D (D) over bar scattering amplitude. As side effects, we warn about the use of a Breit-Wigner amplitude parameterization close to threshold, and show that the D-s(D) over bar (s) channel plays an important role in this reaction.