Abstract: The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.

Abstract: Decays of B-s(0) and (B) over bar (0)(s) mesons into J/psi pi(+)pi(-) final states are studied in a data sample corresponding to 1.9 fb(-1) of integrated luminosity collected with the LHCb detector in 13 TeV pp collisions. A time-dependent amplitude analysis is used to determine the final-state resonance contributions, the CP-violating phase phi(s) = -0.057 +/- 0.060 +/- 0.011 rad, the decay-width difference between the heavier mass B-s(0) eigenstate and the B-0 meson of -0.050 +/- 0.004 +/- 0.004 ps(-1), and the CP-violating parameter vertical bar lambda vertical bar = 1.01(-0.06)(+0.08) +/- 0.03, where the first uncertainty is statistical and the second systematic. These results are combined with previous LHCb measurements in the same decay channel using 7 TeV and 8 TeV pp collisions obtaining phi(s) = 0.002 +/- 0.044 +/- 0.012 rad, and vertical bar lambda vertical bar = 0.949 +/- 0.036 +/- 0.019.

Abstract: Planck data robustly exclude the simple lambda phi(4) scenario for inflation. This is also the case for models of “Axion Inflation” in which the inflaton field is the radial part of the Peccei-Quinn complex scalar field. In this letter we show that for the KSVZ model it is possible to match the data taking into account radiative corrections to the tree level potential. After writing down the 1-loop Coleman-Weinberg potential, we show that a radiative plateau is easily generated thanks to the fact that the heavy quarks are charged under SU(3)(c) in order to solve the strong CP problem. We also give a numerical example for which the inflationary observables are computed and the heavy quarks are predicted to have a mass m(Q) greater than or similar to 10(2) TeV.