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The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient muon tagging (and counting) can be achieved using a water Cherenkov detector with a reduced water volume and 4 PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis based on machine learning (ML). The developed analysis has been tested for different shower and array configurations. The output of the ML analysis, the probability of having a muon in the WCD station, has been used to notably discriminate between gamma and hadron induced showers with S/B∼4 for shower with energies E0∼1TeV. Finally, for proton-induced showers, an estimator of the number of muons was built by means of the sum of the probabilities of having a muon in the stations. Resolutions about 20% and a negligible bias are obtained for vertical showers with Nμ>10.

In this article, it is shown that the C k and LCm variables, recently introduced as an effective way to discriminate gamma and proton-induced showers in large wide-field gamma-ray observatories, can be generalised to be used in arrays of different detectors and variable fill factors. In particular, the C k profile discrimination capabilities are evaluated for scintillator and water Cherenkov detector arrays.

The concept of a small, single-layer water Cherenkov detector, with three photomultiplier tubes (PMTs), placed at its bottom in a 120∘ star configuration (Mercedes Water Cherenkov Detector) is presented. The PMTs are placed near the lateral walls of the stations with an adjustable inclination and may be installed inside or outside the water volume. To illustrate the technical viability of this concept and obtain a first-order estimation of its cost, an engineering design was elaborated. The sensitivity of these stations to low energy Extensive Air Shower (EAS) electrons, photons and muons is discussed, both in compact and sparse array configurations. It is shown that the analysis of the intensity and time patterns of the PMT signals, using machine learning techniques, enables the tagging of muons, achieving an excellent gamma/hadron discrimination for TeV showers. This concept minimises the station production and maintenance costs, allowing for a highly flexible and fast installation. Mercedes Water Cherenkov Detectors (WCDs) are thus well-suited for use in high-altitude large gamma-ray observatories covering an extended energy range from the low energies, closing the gap between satellite and ground-based measurements, to very high energy regions, beyond the PeV scale.

In this article, the potential of water Cherenkov detectors equipped with multi-PMT modules for background-free upward-going neutrino detection and improved direction reconstruction is demonstrated. By analyzing signal time traces with transformer-based models, significant improvements in angular resolution are achieved compared to previous designs with larger PMTs. These detectors enable the reconstruction of neutrino directions with resolutions of approximately 10 ∘ in azimuth and 7 ∘ in zenith for high-signal events, corresponding to an overall opening angle of approximately 10 ∘ . This design reduces saturation effects and enhances directional sensitivity, particularly for high-energy neutrinos. The results highlight the potential of WCD arrays as complementary tools for neutrino astronomy, particularly in the context of multimessenger observations of transient astrophysical sources. The nearly continuous operation and wide field of view of these detectors further enhance their suitability for real-time monitoring and alert generation.

This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs. Different perspectives of information representation are used, and new features are engineered using the specific domain knowledge. As results show, these new features, in combination with the convolutional layers, are able to achieve a good performance avoiding overfitting and being able to generalise properly for the test set. The results also prove that the combination of state-of-the-art machine learning analysis techniques and water Cherenkov detectors with low water depth can be used to efficiently identify muons, which may lead to huge investment savings due to the reduction of the amount of water needed at high altitudes. This achievement can be used in further research to be able to discriminate between gamma and hadron-induced showers using muons as discriminant.

The aim of this paper is to study the possibility of improving the gamma/hadron discrimination in extensive air showers. For this purpose, the identification of hadronic extensive air showers is carried out by means of the detection of muons in water Cherenkov detectors (WCDs). Machine learning algorithms have proven to be useful in a wide variety of fields, and due to their outstanding performance in problems involving complex data, Convolutional Neural Networks (CNNs) have been used in the analysis of the signals measured by the WCDs. Taking simulated events, different approaches were proposed attending to the balance of the classes in the training stage. The results obtained are promising and show that machine learning algorithms provide a powerful tool for muon detection and gamma/hadron discrimination to be considered in future gamma-rays detectors like The Southern Wide-field Gamma-ray Observatory (SWGO) to be built in South America.

Novel methods to reconstruct the slant depth of the maximum of the longitudinal profile (Xmax) of high-energy showers initiated by gamma-rays as well as their energy (E0) are presented. The methods were developed for gamma rays with energies ranging from a few hundred GeV to ∼10 TeV. An estimator of Xmax is obtained, event-by-event, from its correlation with the distribution of the arrival time of the particles at the ground, or the signal at the ground for lower energies. An estimator of E0 is obtained, event-by-event, using a parametrization that has as inputs the total measured energy at the ground, the amount of energy contained in a region near to the shower core and the estimated Xmax. Resolutions about 40(20)g/cm2 and about 30(20)% for, respectively, Xmax and E0 at 1(10)TeV energies are obtained, considering vertical showers. The obtained results are auspicious and can lead to the opening of new physics avenues for large wide field-of-view gamma-ray observatories. The dependence of the resolutions with experimental conditions is discussed.

Currently the detection of Very High Energy gamma-rays for astrophysics rely on the measurement of the Extensive Air Showers (EAS) either using Cherenkov detectors or EAS arrays with larger field of views but also larger energy thresholds. In this talk we present a novel hybrid detector concept for a EAS array with an improved sensitivity in the lower energies (~ 100 GeV). We discuss its main features, capabilities and present preliminary results on its expected perfomances and sensitivities.This wide field of view experiment is planned to be installed at high altitude in South America making it a complementary project to the planned Cherenkov telescope experiments and a powerful tool to trigger further observations of variable sources and to detect transients phenomena.

Introduction African–Americans present higher prevalence of left ventricular hypertrophy (LVH) when compared with white populations. However, there is a lack of information about the prevalence and determinants of LVH in black individuals living in Africa. Methods A cross-sectional study was performed with a non-probabilistic sample comprised of 609 University workers from Angola/Africa, describing the prevalence of LVH and the determinants of left ventricular mass. Echocardiographic measurements were performed and left ventricular mass was indexed to body surface area. Systolic and diastolic blood pressures were measured, along with anthropometric and clinical variables. Results Chamber diameter and wall thickness were higher in men compared to women. Additionally, LVM was higher in men (114.2 ± 36 vs 98.4 ± 31.9, P  < 0.001), and the overall prevalence of LVH in black Angolans was 41.1%, which tended to be higher in women (44.5 vs 37.4%, P  = 0.096). In men, systolic blood pressure and BMI were independently associated with LVM, while age, systolic blood pressure and waist circumference were associated with LVM in women. Conclusions In summary, blood pressure levels were the main determinants of LVH in black Africans, although different anthropometric variables showed mild influence in LVM. Our data suggests that LVH prevalence and determinants in black Africans are similar to that reported for African–Americans.

Using data collected at the Pierre Auger Observatory during the past 3.7 years, we demonstrated a correlation between the arrival directions of cosmic rays with energy above 6 × 10 19 electron volts and the positions of active galactic nuclei (AGN) lying within ∼75 megaparsecs. We rejected the hypothesis of an isotropic distribution of these cosmic rays with at least a 99% confidence level from a prescribed a priori test. The correlation we observed is compatible with the hypothesis that the highest-energy particles originate from nearby extragalactic sources whose flux has not been substantially reduced by interaction with the cosmic background radiation. AGN or objects having a similar spatial distribution are possible sources.