Abstract: A translation of the penelope physics subroutines to C++, designed as an extension of the Geant4 toolkit, is presented. The Fortran code system penelope performs Monte Carlo simulation of coupled electron-photon transport in arbitrary materials for a wide energy range, nominally from 50 eV up to 1 GeV. Penelope implements the most reliable interaction models that are currently available, limited only by the required generality of the code. In addition, the transport of electrons and positrons is simulated by means of an elaborate class II scheme in which hard interactions (involving deflection angles or energy transfers larger than pre-defined cutoffs) are simulated from the associated restricted differential cross sections. After a brief description of the interaction models adopted for photons and electrons/positrons, we describe the details of the class-II algorithm used for tracking electrons and positrons. The C++ classes are adapted to the specific code structure of Geant4. They provide a complete description of the interactions and transport mechanics of electrons/positrons and photons in arbitrary materials, which can be activated from the G4ProcessManager to produce simulation results equivalent to those from the original penelope programs. The combined code, named PenG4, benefits from the multi-threading capabilities and advanced geometry and statistical tools of Geant4.

Abstract: Long-lived particles (LLPs) show up in many extensions of the Standard Model, but they are challenging to search for with current detectors, due to their very displaced vertices. This study evaluated the ability of the trigger algorithms used in the Large Hadron Collider beauty (LHCb) experiment to detect long-lived particles and attempted to adapt them to enhance the sensitivity of this experiment to undiscovered long-lived particles. A model with a Higgs portal to a dark sector is tested, and the sensitivity reach is discussed. In the LHCb tracking system, the farthest tracking station from the collision point is the scintillating fiber tracker, the SciFi detector. One of the challenges in the track reconstruction is to deal with the large amount of and combinatorics of hits in the LHCb detector. A dedicated algorithm has been developed to cope with the large data output. When fully implemented, this algorithm would greatly increase the available statistics for any long-lived particle search in the forward region and would additionally improve the sensitivity of analyses dealing with Standard Model particles of large lifetime, such as KS0 or Lambda (0) hadrons.

Abstract: Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures.

Abstract: GALTRACE is an array of segmented silicon detectors specifically built to work as an ancillary of the GALILEO gamma-ray spectrometer at Legnaro National Laboratory of INFN. GALTRACE consists of four telescopic Delta E-Edetectors which allow discriminating light charged particles also via pulse-shape analysis techniques. The good angular and energy resolutions, together with particle discrimination capabilities, make GALTRACE suitable for experiments where coincidences with specific emitted particles allow for the selection of reaction channels with very low cross section. The first in-beam experiment is reported here, aiming at identifying a narrow resonance, near-proton-threshold state in B-11, currently under discussion.

Abstract: This manuscript summarizes the results of radiative neutron capture cross-section measurements on two stable germanium isotopes, Ge-70 and Ge-73. Experiments were performed at the n_TOF facility at CERN via the time-of-flight technique, over a wide neutron energy range, for all stable germanium isotopes (70,72,73,74, and 76). Results for Ge-70 [Phys. Rev. C 100, 045804 (2019)] and Ge-73 [Phys. Lett. B 790, 458 (2019)] are already published. In the field of nuclear structure, such measurements allow to study excited levels close to the neutron binding energy and to obtain information on nuclear properties. In stellar nucleosynthesis research, neutron induced reactions on germanium are of importance for nucleosynthesis in the weak component of the slow neutron capture processes.