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The recently constructed H4-VLE beam line, a tertiary extension branch of the existing H4 beam line in the CERN North Area, was commissioned in October 2018. The beam line was designed with the purpose of providing very low energy (VLE) hadrons and positrons to the NP-04 experiment, in the momentum range of1–7GeV/c. The production of these low-energy particles is achieved with a mixed hadron (pions, kaons, protons),80GeV/c secondary beam impinging on a thick target. The H4-VLE beam line has been instrumented with prototype scintillating fiber detectors providing the beam profile, intensity, and time-of-flight measurement of the beam particles, that, together with Cherenkov threshold counters, permit an event-by-event particle identification over the entire momentum spectrum. In this paper, we present detailed results of the beam line performance and the measured beam composition, as well as the comparison of these measurements with simulations performed during the design phase using fluka and geant-4-based Monte-Carlo codes.

Muon tomography is a potential non-invasive technique for internal structure scanning. It has already interesting applications in geophysics and can be used for archaeological purposes. Muon tomography is based on the measurement of the muon flux after crossing the structure studied. Differences on the mean density of these structures imply differences on the detected muon rate for a given direction. Based on this principle, Monte Carlo simulations represent a useful tool to provide a model of the expected muon rate and angular distribution depending on the composition of the studied object, being useful to estimate the expected detected muons and to better understand the experimental results. These simulations are mainly dependent on the geometry and composition of the studied object and on the modelling of the initial muon flux at surface. In this work, the potential of muon tomography in archaeology is presented and evaluated with Monte Carlo simulations by estimating the differences on the muon rate due to the presence of internal structures and its composition. The influence of the chosen muon model at surface in terms of energy and angular distributions in the final result has been also studied.

For many decades the CERN North Area facility at the Super Proton Synchrotron (SPS) has delivered secondary beams to various fixed target experiments and test beams. In 2018, two new tertiary extensions of the existing beam lines, designated \"H2-VLE\" and \"H4-VLE\", have been constructed and successfully commissioned. These beam lines have been designed to provide charged particles of both polarities in the momentum range from 0.3GeV/c to 12GeV/c. During the design phase, multiple simulation tools and techniques have been employed to optimize the tertiary beam line layout in terms of particle production, transverse beam dynamics and particle identification on an event-by-event basis. In this paper, a comparison of the simulated performance and the first measurement results obtained during the commissioning phase are presented.

In the framework of the CERN Neutrino Platform project, extensions to the existing SPS North Area secondary beam lines \"H2\" and \"H4\", able to provide low-energy charged particles in the momentum range from 0.4 to 12 GeV/c, have been designed. The parameters of these \"very low energy\" beam lines, the expected beam composition as seen by the experiments as well as an outlook on their expected performance are summarized in this paper. Results from Monte-Carlo simulations, important for the optimization of the future instrumentation of the beam lines (serving both the purpose of beam tuning and the experiments' needs for particle identification and momentum measurements), are also presented.

The recent progress in R&D of the Micromegas detectors for hadronic calorimetry including new engineering-technical solutions, electronics development, and accompanying simulation studies with emphasis on the comparison of the physics performance of the analog and digital readout is described. The developed prototypes are with 2 bit digital readout to exploit the Micromegas proportional mode and thus improve the calorimeter linearity. In addition, measurements of detection efficiency, hit multiplicity, and energy shower profiles obtained during the exposure of small size prototypes to radioactive source quanta, cosmic particles and accelerator beams are reported. Eventually, the status of a large scale chamber (1 × 1 m2) are also presented with prospective towards the construction of a 1 m3 digital calorimeter consisting of 40 such chambers.

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/\\(c\\) charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1\\(\\pm0.6\\)% and 84.1\\(\\pm0.6\\)%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.

Muon imaging is one of the most promising non-invasive techniques for density structure scanning, specially for large objects reaching the kilometre scale. It has already interesting applications in different fields like geophysics or nuclear safety and has been proposed for some others like engineering or archaeology. One of the approaches of this technique is based on the well-known radiography principle, by reconstructing the incident direction of the detected muons after crossing the studied objects. In this case, muons detected after a previous forward scattering on the object surface represent an irreducible background noise, leading to a bias on the measurement and consequently on the reconstruction of the object mean density. Therefore, a prior characterization of this effect represents valuable information to conveniently correct the obtained results. Although the muon scattering process has been already theoretically described, a general study of this process has been carried out based on Monte Carlo simulations, resulting in a versatile tool to evaluate this effect for different object geometries and compositions. As an example, these simulations have been used to evaluate the impact of forward scattered muons on two different applications of muon imaging: archaeology and volcanology, revealing a significant impact on the latter case. The general way in which all the tools used have been developed can allow to make equivalent studies in the future for other muon imaging applications following the same procedure.

The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is determined, based on a full simulation, reconstruction, and event selection of the far detector and a full simulation and parameterized analysis of the near detector. Detailed uncertainties due to the flux prediction, neutrino interaction model, and detector effects are included. DUNE will resolve the neutrino mass ordering to a precision of 5\\(\\sigma\\), for all \\(\\delta_{\\mathrm{CP}}\\) values, after 2 years of running with the nominal detector design and beam configuration. It has the potential to observe charge-parity violation in the neutrino sector to a precision of 3\\(\\sigma\\) (5\\(\\sigma\\)) after an exposure of 5 (10) years, for 50\\% of all \\(\\delta_{\\mathrm{CP}}\\) values. It will also make precise measurements of other parameters governing long-baseline neutrino oscillation, and after an exposure of 15 years will achieve a similar sensitivity to \\(\\sin^{2} 2\\theta_{13}\\) to current reactor experiments.

The observation of 236 MeV muon neutrinos from kaon-decay-at-rest (KDAR) originating in the core of the Sun would provide a unique signature of dark matter annihilation. Since excellent angle and energy reconstruction are necessary to detect this monoenergetic, directional neutrino flux, DUNE with its vast volume and reconstruction capabilities, is a promising candidate for a KDAR neutrino search. In this work, we evaluate the proposed KDAR neutrino search strategies by realistically modeling both neutrino-nucleus interactions and the response of DUNE. We find that, although reconstruction of the neutrino energy and direction is difficult with current techniques in the relevant energy range, the superb energy resolution, angular resolution, and particle identification offered by DUNE can still permit great signal/background discrimination. Moreover, there are non-standard scenarios in which searches at DUNE for KDAR in the Sun can probe dark matter interactions.