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The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors (WCDs), LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the atmosphere of Earth. This flux can be grouped into three distinct basic constituents: electromagnetic, muonic, and hadronic components. When a particle enters a WCD, it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, these data are insufficient to effectively distinguish between the contributions of different secondary particles. In this work, we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. This dataset, which was created through the combination of the outputs of the ARTI and Meiga simulation frameworks, simulated the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 m above sea level. This was achieved by analyzing the real-time magnetospheric and local atmospheric conditions for February and March of 2012, where the resultant atmospheric secondary-particle flux was integrated into a specific Meiga application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The final output was modified for effective integration into our machine-learning pipeline. With an implementation of Ordering Points to Identify the Clustering Structure (OPTICS), a density-based clustering algorithm used to identify patterns in data collected by a single WCD, we have further refined our approach to implement a method that categorizes particle groups using advanced unsupervised machine learning techniques. This allowed for the differentiation among particle types and utilized the detector’s nuanced response to each, thus pinpointing the principal contributors within each group. Our analysis has demonstrated that applying our enhanced methodology can accurately identify the originating particles with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of future implementations of machine-leaning-based models throughout LAGO’s distributed detection network and other astroparticle observatories for semi-automated, onboard and real-time data analysis.

Searching for very-high-energy photons arising from dark matter interactions in selected astrophysical environments is a promising strategy to probe the existence and particle nature of dark matter. Among the many particle candidates, motivated by the extensions of the Standard Model, Weakly Interacting Massive Particles (WIMPs) are considered the most compelling candidate for the elusive dark matter in the universe. In this contribution, we report an overview of the important developments in the field of indirect searching for dark matter through cosmic gamma-ray observations. We mainly focus on the role of atmospheric Cherenkov telescopes in probing the dark matter. Finally, we emphasize the opportunities for the Major Atmospheric Cherenkov Experiment (MACE) situated in Hanle, India, to explore WIMPs in the mass range of 200 GeV to 10 TeV for Segue1 and Draco dwarf–spheroidal galaxies.

Angular resolution is crucial for the detailed study of gamma-ray sources and current Cherenkov telescopes (e.g., HESS, MAGIC, and VERITAS) that operate below tens of TeV. Several gamma-ray sources with a photon energy larger than 100 TeV have been revealed by the LHAASO in recent years; the angular resolution of the LHAASO is around 0.3∘. A gamma-ray detector with an angular resolution of less than 0.1∘ operating beyond 100 TeV is needed to study the detailed morphology of ultra-high-energy gamma-ray sources further. The cost-effectiveness is crucial for such large-area detectors. In this paper, the impact of telescope aperture, field of view, pixel size, optical point spread function, and signal integration time window on angular resolution is studied. These results can provide essential elements for the design of telescope arrays.

This work presents the development of a 64-channel application-specific integrated circuit (ASIC), implemented to detect the optical Cherenkov light from sub-orbital and orbital altitudes. These kinds of signals are generated by ultra-high energy cosmic rays (UHECRs) and cosmic neutrinos (CNs). The purpose of this front-end electronics is to provide a readout unit for a matrix of silicon photo-multipliers (SiPMs) to identify extensive air showers (EASs). Each event can be stored into a configurable array of 256 cells where the on-board digitization can take place with a programmable 12-bits Wilkinson analog-to-digital converter (ADC). The sampling, the conversion process, and the main digital logic of the ASIC run at 200 MHz, while the readout is managed by dedicated serializers operating at 400 MHz in double data rate (DDR). The chip is designed in a commercial 65 nm CMOS technology, ensuring a high configurability by selecting the partition of the channels, the resolution in the interval 8–12 bits, and the source of its trigger. The production and testing of the ASIC is planned for the forthcoming months.

Ground-based observations of Very High Energy (VHE) gamma rays from extreme astrophysical sources are significantly influenced by atmospheric conditions. This is due to the atmosphere being an integral part of the detector when utilizing Imaging Atmospheric Cherenkov Telescopes (IACTs). Clouds and dust particles diminish atmospheric transmission of Cherenkov light, thereby impacting the reconstruction of the air showers and consequently the reconstructed gamma-ray spectra. Precise measurements of atmospheric transmission above Cherenkov observatories play a pivotal role in the accuracy of the analysed data, among which the corrections of the reconstructed energies and fluxes of incoming gamma rays, and in establishing observation strategies for different types of gamma-ray emitting sources. The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes and the Cherenkov Telescope Array Observatory (CTAO), both located on the Observatorio del Roque de los Muchachos (ORM), La Palma, Canary Islands, use different sets of auxiliary instruments for real-time characterisation of the atmosphere. In this paper, historical data taken by MAGIC LIDAR (LIght Detection And Ranging) and CTAO FRAM (F/Photometric Robotic Telescope) are presented. From the atmospheric aerosol transmission profiles measured by the MAGIC LIDAR and CTAO FRAM aerosol optical depth maps, we obtain the characterisation of the clouds above the ORM at La Palma needed for data correction and optimal observation scheduling.

Monte Carlo simulations of induced extensive air showers (EASs) by ultra-high-energy cosmic rays are widely used in comparison with measured events at experiments to estimate the main cosmic ray characteristics, such as mass, energy, and arrival direction. However, these simulations are computationally expensive, with running time scaling proportionally with the number of radio antennas included. The AugerPrime upgrade of the Pierre Auger Observatory will feature an array of 1660 radio antennas. As a result, simulating a single EAS using the full detector array will take weeks on a single CPU thread. To reduce the simulation time, detectors are commonly pre-selected based on their proximity to the shower core, using a selection ellipse based on the Cherenkov radiation footprint scaled by a fixed constant factor. While effective, this approach often includes many noisy antennas at high zenith angles, reducing computational efficiency. In this paper, we introduce an optimal method for selecting candidate detectors with high predicted signal-to-noise ratio for proton and iron primary cosmic rays, replacing the constant scaling factor with a function of the zenith angle. This approach significantly reduces simulation time—by more than 50% per CPU thread for the heaviest, most inclined showers—without compromising signal quality.

To apply deep learning to estimate the three-dimensional interaction position of a Cherenkov detector, an experimental measurement of the true depth of interaction is needed. This requires significant time and effort. Therefore, in this study, we propose a direct annihilation position classification method based on deep learning using paired Cherenkov detectors. The proposed method does not explicitly estimate the interaction position or time-of-flight information and instead directly estimates the annihilation position from the raw data of photon information measured by paired Cherenkov detectors. We validated the feasibility of the proposed method using Monte Carlo simulation data of point sources. A total of 125 point sources were arranged three-dimensionally with 5 mm intervals, and two Cherenkov detectors were placed face-to-face, 50 mm apart. The Cherenkov detector consisted of a monolithic PbF2 crystal with a size of 40 × 40 × 10 mm3 and a photodetector with a single photon time resolution (SPTR) of 0 to 100 picosecond (ps) and readout pitch of 0 to 10 mm. The proposed method obtained a classification accuracy of 80% and spatial resolution with a root mean square error of less than 1.5 mm when the SPTR was 10 ps and the readout pitch was 3 mm.

The MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov) Florian Goebel telescopes are a system of two Cherenkov telescopes located on the Canary island of La Palma (Spain), at the Roque de Los Muchachos Observatory, which have been operating in stereo mode since 2009. Their low energy threshold (down to 15 GeV) allows the investigation of Active Galactic Nuclei (AGNs) in the very-high-energy (VHE, E > 100 GeV) gamma-ray range with a sensitivity up to the redshift limit of the existing IACT (Imaging Atmospheric Cherenkov Telescopes) systems. The MAGIC telescopes discovered 36 extragalactic objects emitting VHE gamma-rays and performed comprehensive studies of galaxies and their AGNs, also in a multi-wavelength (MWL) and multi-messenger (MM) context, expanding the knowledge of our Universe. Here, we report on the highlights achieved by the MAGIC collaboration since the beginning of their operations.

Bioassay is the preferred individual monitoring technique for radiation workers handling phosphorus-32 (32 P), a pure beta emitter (βmax = 1.71 MeV) with 14.3 day half-life. The method standardized at Bioassay Laboratory, Trombay and in use for this purpose includes estimation of 32 P in urine by coprecipitation with ammonium phosphomolybdate followed by gross beta counting. In this study, the feasibility of Cerenkov counting for detection of 32 P in bioassay samples was explored, and the results obtained were compared with the conventional gross beta technique.

Gas Cherenkov detectors have been used to convert fusion gammas into photons to record gamma reaction history measurements. These gas detectors include a converter, pressurized gas volume, relay collection optics, and a photon detector. A novel design for the National Ignition Facility (NIF) using 90° off-axis parabolic mirrors efficiently collects signal from fusion gammas with 8-ps time dispersion. Fusion gammas are converted to Compton electrons, which generate broadband Cherenkov light (response is from 250 to 700 nm) in a pressurized gas cell. This light is relayed into a high-speed detector using three parabolic mirrors. The relay optics collect light from a 125-mm-diameter by 600-mm-long interchangeable gas (CO2 or SF6) volume. The parabolic mirrors were electroformed instead of diamond turned to reduce scattering of the UV light. All mirrors are bare aluminum coated for maximum reflectivity. This design incorporates a 4.2-ns time delay that allows the detector to recover from prompt radiation before it records the gamma signal. At NIF, a cluster of four channels will allow for increased dynamic range, as well as different gamma energy thresholds.