KM3NeT Collaboration(Adrian-Martinez, S. et al), Aguilar, J. A., Bigongiari, C., Calvo Diaz-Aldagalan, D., Emanuele, U., Gomez-Gonzalez, J. P., et al. (2013). Expansion cone for the 3-inch PMTs of the KM3NeT optical modules. J. Instrum., 8, T03006–20pp.
Abstract: Detection of high-energy neutrinos from distant astrophysical sources will open a new window on the Universe. The detection principle exploits the measurement of Cherenkov light emitted by charged particles resulting from neutrino interactions in the matter containing the telescope. A novel multi-PMT digital optical module (DOM) was developed to contain 31 3-inch photomultiplier tubes (PMTs). In order to maximize the detector sensitivity, each PMT will be surrounded by an expansion cone which collects photons that would otherwise miss the photocathode. Results for various angles of incidence with respect to the PMT surface indicate an increase in collection efficiency by 30% on average for angles up to 45 degrees with respect to the perpendicular. Ray-tracing calculations could reproduce the measurements, allowing to estimate an increase in the overall photocathode sensitivity, integrated over all angles of incidence, by 27% (for a single PMT). Prototype DOMs, being built by the KM3NeT consortium, will be equipped with these expansion cones.
|
KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum., 15(10), P10005–39pp.
Abstract: The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.
|
Oliveira, C. A. B., Sorel, M., Martin-Albo, J., Gomez-Cadenas, J. J., Ferreira, A. L., & Veloso, J. F. C. A. (2011). Energy resolution studies for NEXT. J. Instrum., 6, P05007–13pp.
Abstract: This work aims to present the current state of simulations of electroluminescence (EL) produced in gas-based detectors with special interest for NEXT – Neutrino Experiment with a Xenon TPC. NEXT is a neutrinoless double beta decay experiment, thus needs outstanding energy resolution which can be achieved by using electroluminescence. The process of light production is reviewed and properties such as EL yield and associated fluctuations, excitation and electroluminescence efficiencies, and energy resolution, are calculated. An EL production region with a 5 mm width gap between two infinite parallel planes is considered, where a uniform electric field is produced. The pressure and temperature considered are 10 bar and 293 K, respectively. The results show that, even for low values of VUV photon detection efficiency, good energy resolution can be achieved: below 0.4% (FWHM) at Q(beta beta) = 2.458 MeV.
|
Pierre Auger Collaboration(Abreu, P. et al), & Pastor, S. (2013). Constraints on the origin of cosmic rays above 10^18 eV from large-scale anisotropy searches in data of the Pierre Auger Observatory. Astrophys. J. Lett., 762(1), L13–8pp.
Abstract: A thorough search for large-scale anisotropies in the distribution of arrival directions of cosmic rays detected above 10(18) eV at the Pierre Auger Observatory is reported. For the first time, these large-scale anisotropy searches are performed as a function of both the right ascension and the declination and expressed in terms of dipole and quadrupole moments. Within the systematic uncertainties, no significant deviation from isotropy is revealed. Upper limits on dipole and quadrupole amplitudes are derived under the hypothesis that any cosmic ray anisotropy is dominated by such moments in this energy range. These upper limits provide constraints on the production of cosmic rays above 10(18) eV, since they allow us to challenge an origin from stationary galactic sources densely distributed in the galactic disk and emitting predominantly light particles in all directions.
|
Trotta, R., Johannesson, G., Moskalenko, I. V., Porter, T. A., Ruiz de Austri, R., & Strong, A. W. (2011). Constraints on Cosmic-Ray Propagation Models from a Global Bayesian Analysis. Astrophys. J., 729(2), 106–16pp.
Abstract: Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.
|
