Abstract: The high-spin states of the neutron-rich Ga-81, with three valence protons outside a Ni-78 core, were measured. The measurement involved prompt gamma-ray spectroscopy of fission fragments isotopically identified using the combination of the variable mode spectrometer (VAMOS++) and the advanced gamma tracking array (AGATA). The new gamma-ray transitions, observed in coincidence with Ga-81 ions, and the corresponding level scheme do not confirm the high-spin levels reported earlier. The newly observed high-spin states in Ga-81 are interpreted using the results of state-of-the-art large-scale shell model (LSSM) calculations. The lower excitation energy levels are understood as resulting from the recoupling of three valence protons to the closed doubly magic core, while the highest excitation energy levels correspond to excitations of the magic N = 50 neutron core. These results support the doubly magic character of Ni-78 and the persistence of the N = 50 shell closure but also highlight the presence of strong proton-neutron correlations associated with the promotion of neutrons across the magic N = 50 shell gap, only few nucleons away from Ni-78.

Abstract: A discrete-time Quantum Walk (QW) is an operator driving the evolution of a single particle on the lattice, through local unitaries. In a previous paper, we showed that QWs over the honeycomb and triangular lattices can be used to simulate the Dirac equation. We apply a spacetime coordinate transformation upon the lattice of this QW, and show that it is equivalent to introducing spacetime-dependent local unitaries-whilst keeping the lattice fixed. By exploiting this duality between changes in geometry, and changes in local unitaries, we show that the spacetime-dependent QW simulates the Dirac equation in (2 + 1)-dimensional curved spacetime. Interestingly, the duality crucially relies on the non linear-independence of the three preferred directions of the honeycomb and triangular lattices: The same construction would fail for the square lattice. At the practical level, this result opens the possibility to simulate field theories on curved manifolds, via the quantum walk on different kinds of lattices.

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.