Gamma/hadron discrimination in ground-based gamma-ray observatories at the sub-TeV energy range is challenging as traditional muon-based methods become less effective at lower energies. This work explores a novel gamma/hadron discrimination method for Extensive Air Shower arrays that analyzes the shower signal footprint patterns using a state-of-the-art pre-trained Vision Transformer (ViT). The resilience of the method to background noise, such as atmospheric muons and low-energy proton showers, along with its adaptability to different zenith angles and array configurations, demonstrates its potential for application in current and future ground-based gamma-ray observatories.

We highlight the capacity of current and forthcoming air shower arrays using water-Cherenkov stations to detect neutrino events spanning energies from 10 GeV to 100 TeV. This detection approach leverages individual stations equipped with both bottom and top photosensors, making use of features of the signal time trace and machine-learning techniques. Our findings demonstrate the complementary of this method to established and future neutrino-detection experiments, including IceCube and the upcoming Hyper-Kamiokande experiment.

The recent discovery of PeV gamma-ray emission especially from the LHAASO observatory, located in the Northern hemisphere, boosted the relevance of observing the Southern sky at such energies. SWGO (SouthernWide-Field Gamma-Ray Observatory) is the largest proposed detector with sensitivity in the 100 TeV-1 PeV energy range. The baseline SWGO idea is a km^2 array of water tanks to be placed above 4,400ma.s.l. in the Andes, South America. In this contribution, we have studied the particle content and the morphology of Extensive Air Showers (EAS) generated by photons and protons in the 0.1 to 10 PeV energy range. We have simulated over 106 gamma-rays and proton induced showers respectively with primary energy in the 0.1-10 PeV energy range. We also show the particle distribution at ground, the lateral profile, the muon content and the average particle properties at ground.

Water-Cherenkov detectors (WCD) have been manufactured in Australia by the company AQUA-MATE as part of the RD activities for SWGO. They consist of a steel tank frame with a bladder on its interior satisfying the SWGO double-layer tank design. Tanks and bladders have been custom designed to optimally accommodate the bladder inside the tank and with minimal material usage. They are delivered in compact boxes that are easy to transport. These boxes are designed to fit 24 tanks in a 20-foot container. The double-layer tank design has introduced new features to improve the discrimination between gamma-rays and cosmic rays. Some of these features created challenges for the manufacturing. Some units have been delivered to one of Peru’s candidate sites at 4800 m and to Mexico (the HAWC Observatory, 4100 m) for prototype tests in real conditions. In this contribution we will describe manufacturing and construction details of the first SWGO prototype WCD. These details were envisaged to facilitate: the transport of the units, the assembly, the deployment and maintenance activities of the detectors. Furthermore, the units need to be resistant to strong winds, rain, snow and earthquakes.The costs are scalable with the detector volume. This information could be of interest to other Observatories that are in RD phase, such as the Global Cosmic Ray Observatory (GCOS) for the study of the highest-energy particles in the Universe and the Tau Air Shower Mountain-Based Observatory (TAMBO) for the search of PeV neutrinos.

The SWGO Collaboration is evaluating the possibility of deploying Water Cherenkov Detectors (WCD) in a high-altitude natural lake. For that, the first challenge is to build a bladder strong enough that could be used as a WCD inside a natural lake. A prototype bladder has been designed for SWGO and two bladders, made of different films, have been deployed for testing at Sibinacocha lake, in Peru, at 5000 masl. In order to monitor the wave intensity in the lake, a low-cost oceanographic buoy was developed using an acceleration sensor MPU6050 and a liquid sensor DS18B20. The development platform used was the Arduino Mega 2560 with some out-of-the shelf modules to achieve a functional and autonomous prototype. A code was developed in Python to process the data and convert the acceleration values into position, allowing estimation of height variations, as a function of time, less than 1 cm. To reduce the environmental impact of the floating structure, the use of metallic materials was minimized and mostly wood, cotton, and PVC pipes were used. This buoy has been installed next to SWGO prototype bladders at the Sibinacocha lake in Peru. In this contribution we will present the details of a low-cost oceanographic buoy built to monitor lake wave intensity.

Current cosmic ray anisotropy experiments have shown a significant swing in both the direction and the amplitude of the dipole at energies around tens of TeV. Due to the charged nature of these particles, and the presence of magnetic fields in our galaxy, an underlying composition-dependent dipole swing is expected. For this reason, combining measurements of the composition and the distribution of arrival directions is essential for unveiling the astrophysical origin of this structure. In this work, we study the potential of the upcoming Southern Wide-field Gamma-ray Observatory (SWGO) in contributing to these anisotropy studies. We present a template-based method developed for reconstructing the number of muons and separating primary cosmic rays. Preliminary resolutions of $5-30\%$ in the number of muons and an accuracy of $70-90\%$ in the species separation are found. A clear improvement is seen by considering a dedicated muon-counter layer in a detector, highlighting the future potential of SWGO.

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance -- from the data-generating processes to their reconstruction and the extraction of inference on the parameters of interest of a measuring instrument -- along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism.

The Southern Wide-field Gamma-ray Observatory (SWGO) is an international collaboration working on a ground-based gamma-ray observatory that will be located in the southern hemisphere. A crucial step in the analysis is to identify the showers produced by gamma rays and separate them from the abundant background of hadronic showers. In this work, we propose to adapt the observable $S_b$ used successfully to composition studies in the Pierre Auger Observatory to the SWGO detector. This observable takes into account the signal and the position of each triggered detector. It characterizes the shape of the lateral distribution of the signal, which depends on the nature of the primary particle. The value of $S_b$ is therefore suited to identify gamma induced showers and to reject the more frequent hadronic showers. This analysis has been performed using a scaling factor to link the observable $S_b$ between the central and outrigger array. Also we explore how this new observable improves the separation of primary proton and gamma-induced air showers in terms of the merit factor.

We present the concept of a shallow, single-layer, multi-PMT water Cherenkov detector (WCD) unit for ground-based gamma-ray astronomy. The design was developed as part of the R&D activities for SWGO, as a candidate WCD unit solution for the future Observatory. The main objective of the proposal is to achieve a low-volume single-layer surface detection unit with good calorimetry and timing of the shower front, and excellent gamma-hadron discrimination capability. The unit name, Mercedes, is derived from its configuration, where 3 PMTs are positioned equidistant from the center of the unit in a 120-deg symmetric azimuthal arrangement. Background rejection, in the TeV range, is based on the asymmetric illumination of the PMTs by energetic muons traversing the water volume. The mechanical implementation is based on rotomolded tanks similar to those used in the Pierre Auger Observatory. For easy deployment, the PMTs may be installed through openings in the lateral walls of the tank, and placed outside water, observing the calorimetric volume through transparent windows in the tank bladders. The highly innovative tank proposal, whose production technique is already patented, and at the commercial stage, has a jacketed- wall structure that houses all electronics and cabling, and a layer of insulating foam to avoid freezing at high-altitude. The mechanical concept was conceived for inexpensive production, easy deployment and maintenance, and to provide a cost-effective solution for high-altitude large array designs with several thousand units, such as in SWGO.

The Southern Wide-field Gamma-ray Observatory (SWGO) is a proposed facility for ground-based gamma-ray astronomy. It will consist of an array of water Cherenkov detectors to detect astrophysical gamma-rays mainly in the range of hundreds of GeV up to the PeV scale. To be constructed in South America, it will feature an approximately two-steradian field of view and a duty cycle close to 100%, which will complement the current generation of instruments by extending survey coverage to the Southern Hemisphere. As part of the development of the SWGO standard reconstruction framework, we employ a Monte Carlo template-based method to estimate the properties of the primary gamma-ray (such as core position and energy). In this method, an observed lateral amplitude distribution of a gamma-ray-induced air shower is fitted to the expected probability distribution stored in the templates. We validate this approach by estimating the energy performance for one of the currently investigated test array and detector unit configurations for SWGO.

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.

In this letter, a new strategy to enhance the discrimination of high energy gamma rays from the huge charged cosmic rays background in large cosmic rays ground arrays is presented. This strategy is based on the introduction of a new simple variable, Pγhα, which combines the probability of tagging muons and/or very energetic particles in each single array station. The discrimination power of this new variable, particularly important for and above multi-TeV energies, is illustrated for a few specific examples in the case of a hypothetical water Cherenkov detector cosmic ray array, both in the case of low and high particle stations occupancy. The results are very encouraging and hopefully will be demonstrated in the present and future gamma-ray Observatories.

The Southern Wide-field Gamma-ray Observatory (SWGO) is the next-generation gamma-ray observatory, currently in an R&D phase. The experiment is expected to have a large array of water Cherenkov detectors (WCD) placed at a high elevation (> 4.4 km a.s.l.) in South America. Here we present a WCD concept with reduced surface area and height of stations comprising four PMTs at the bottom. We show that it is possible to reach an excellent gamma/hadron discrimination by analysing the data gathered by this station with machine learning techniques. Such performance can be achieved by analysing the shower patterns at the ground or through the PMTs signal time structure to tag muons. Moreover, it is shown that the station's performance does not depend on the array configuration (dense or sparse) nor on the shower inclination (theta < 40 degrees). Such a concept reduces the cost associated with the transport of massive amounts of water to high elevation sites while keeping a high physics performance. Therefore, it could be a good candidate station for SWGO, enabling to reach good sensitivities from low energies (similar to 100 GeV) up to the PeV region, covering large ground surface areas (few square km).

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 letter, a new strategy to enhance the discrimination of high-energy gamma rays from the huge charged cosmic rays background in large cosmic rays ground arrays is presented. This strategy is based on the introduction of a new simple variable, $P_{\gamma h}^{\alpha}$, which combines the probability of tagging muons and/or very energetic particles in each single array station. The discrimination power of this new variable is illustrated for a few specific examples in the case of a hypothetical water Cherenkov detector cosmic ray array, both in the case of low and high particle stations occupancy. The results are very encouraging and hopefully will be demonstrated in the present and future gamma-ray Observatories.

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.