The sources of the astrophysical flux of high-energy neutrinos detected by IceCube are still largely unknown, but searches for temporal and spatial correlation between neutrinos and electromagnetic radiation are a promising approach in this endeavor. All major imaging atmospheric Cherenkov telescopes (IACTs) - FACT, H.E.S.S., MAGIC, and VERITAS - operate an active follow-up program of target-of-opportunity observations of neutrino alerts issued by IceCube. These programs use several complementary neutrino alert streams. A publicly distributed alert stream is formed by individual high-energy neutrino candidate events of potentially astrophysical origin, such as IceCube-170922A (which could be linked to the flaring blazar TXS 0506+056). A privately distributed alert stream is formed by clusters of neutrino events in time and space around either pre-selected gamma-ray sources or anywhere in the sky. Here, we present joint searches for multi-wavelength emission associated with a set of IceCube alerts, both private and public, received through mid-January 2021. We will give an overview of the programs of the participating IACTs. We will showcase the various follow-up and data analysis strategies employed in response to the different alert types and various possible counterpart scenarios. Finally, we will present results from a combined analysis of the VHE gamma-ray observations obtained across all involved instruments, as well as relevant multi-wavelength data.

In the context of atmospheric shower arrays designed for gamma-ray astronomy and in the context of the ALTO project, we present: a study of the impact of heavier nuclei in the cosmic-ray background on the estimated gamma-ray detection performance on the basis of dedicated Monte Carlo simulations, a method to calculate the sensitivity to a point-like source, and finally the required observation times to reach a firm detection on a list of known point-like sources.

Context. Young massive stellar clusters are extreme environments and potentially provide the means for efficient particle acceleration. Indeed, they are increasingly considered as being responsible for a significant fraction of cosmic rays (CRs) that are accelerated within the Milky Way. Westerlund 1, the most massive known young stellar cluster in our Galaxy, is a prime candidate for studying this hypothesis. While the very-high-energy gamma-ray source HESS J1646-458 has been detected in the vicinity of Westerlund 1 in the past, its association could not be firmly identified. Aims. We aim to identify the physical processes responsible for the gamma-ray emission around Westerlund 1 and thus to understand the role of massive stellar clusters in the acceleration of Galactic CRs better. Methods. Using 164 h of data recorded with the High Energy Stereoscopic System (H.E.S.S.), we carried out a deep spectromorphological study of the gamma-ray emission of HESS J1646-458. We furthermore employed H I and CO observations of the region to infer the presence of gas that could serve as target material for interactions of accelerated CRs. Results. We detected large-scale (similar to 2 degrees diameter) gamma-ray emission with a complex morphology, exhibiting a shell-like structure and showing no significant variation with gamma-ray energy. The combined energy spectrum of the emission extends to several tens of TeV, and it is uniform across the entire source region. We did not find a clear correlation of the gamma-ray emission with gas clouds as identified through H I and CO observations. Conclusions. We conclude that, of the known objects within the region, only Westerlund 1 can explain the majority of the gamma-ray emission. Several CR acceleration sites and mechanisms are conceivable and discussed in detail. While it seems clear that Westerlund 1 acts as a powerful particle accelerator, no firm conclusions on the contribution of massive stellar clusters to the flux of Galactic CRs in general can be drawn at this point.

The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.

The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.

Context. Supernova remnants (SNRs) are commonly thought to be the dominant sources of Galactic cosmic rays up to the knee of the cosmic-ray spectrum at a few PeV. Imaging Atmospheric Cherenkov Telescopes have revealed young SNRs as very-high-energy (VHE, >100 GeV) gamma-ray sources, but for only a few SNRs the hadronic cosmic-ray origin of their gamma-ray emission is indisputably established. In all these cases, the gamma-ray spectra exhibit a spectral cutoff at energies much below 100 TeV and thus do not reach the PeVatron regime. Aims. The aim of this work was to achieve a firm detection for the oxygen-rich SNR LMC N132D in the VHE gamma-ray domain with an extended set of data, and to clarify the spectral characteristics and the localization of the gamma-ray emission from this exceptionally powerful gamma-ray-emitting SNR. Methods. We analyzed 252 h of High Energy Stereoscopic System (H.E.S.S.) observations towards SNR N132D that were accumulated between December 2004 and March 2016 during a deep survey of the Large Magellanic Cloud, adding 104 h of observations to the previously published data set to ensure a > 5 sigma detection. To broaden the gamma-ray spectral coverage required for modeling the spectral energy distribution, an analysis of Fermi-LAT Pass 8 data was also included. Results. We unambiguously detect N132D at VHE with a significance of 5.7 sigma. We report the results of a detailed analysis of its spectrum and localization based on the extended H.E.S.S. data set. The joint analysis of the extended H.E.S.S and Fermi-LAT data results in a spectral energy distribution in the energy range from 1.7 GeV to 14.8 TeV, which suggests a high luminosity of N132D at GeV and TeV energies. We set a lower limit on a gamma-ray cutoff energy of 8 TeV with a confidence level of 95%. The new gamma-ray spectrum as well as multiwavelength observations of N132D when compared to physical models suggests a hadronic origin of the VHE gamma-ray emission. Conclusions. SNR N132D is a VHE gamma-ray source that shows a spectrum extending to the VHE domain without a spectral cutoff at a few TeV, unlike the younger oxygen-rich SNR Cassiopeia A. The gamma-ray emission is best explained by a dominant hadronic component formed by diffusive shock acceleration. The gamma-ray properties of N132D may be affected by an interaction with a nearby molecular cloud that partially lies inside the 95% confidence region of the source position.

Cosmological N-body simulations show that Milky Way-sized galaxies harbor a population of unmerged dark matter (DM) subhalos. These subhalos could shine in gamma-rays and eventually be detected in gamma-ray surveys as unidentified sources. We performed a thorough selection among unidentified Fermi-Large Area Telescope Objects (UFOs) to identify them as possible tera-electron-volt-scale DM subhalo candidates. We search for very-high-energy (E greater than or similar to 100 GeV) gamma-ray emissions using H.E.S.S. observations toward four selected UFOs. Since no significant very-high-energy gamma-ray emission is detected in any data set of the four observed UFOs or in the combined UFO data set, strong constraints are derived on the product of the velocity-weighted annihilation cross section sigma v by the J factor for the DM models. The 95% confidence level observed upper limits derived from combined H.E.S.S. observations reach sigma vJ values of 3.7 x 10(-5) and 8.1 x 10(-6) GeV(2 )cm(-2 )s(-1) in the W (+) W (-) and tau (+) tau (-) channels, respectively, for a 1 TeV DM mass. Focusing on thermal weakly interacting massive particles, the H.E.S.S. constraints restrict the J factors to lie in the range 6.1 x 10(19)-2.0 x 10(21) GeV(2 )cm(-5) and the masses to lie between 0.2 and 6 TeV in the W (+) W (-) channel. For the tau (+) tau (-) channel, the J factors lie in the range 7.0 x 10(19)-7.1 x 10(20) GeV(2 )cm(-5) and the masses lie between 0.2 and 0.5 TeV. Assuming model-dependent predictions from cosmological N-body simulations on the J-factor distribution for Milky Way-sized galaxies, the DM models with masses >0.3 TeV for the UFO emissions can be ruled out at high confidence level.

Magnetar hyperflares are the most plausible explanation for fast radio bursts (FRBs)-enigmatic powerful radio pulses with durations of several milliseconds and high brightness temperatures. The first observational evidence for this scenario was obtained in 2020 April when an FRB was detected from the direction of the Galactic magnetar and soft gamma-ray repeater SGR 1935+2154. The FRB was preceded by two gamma-ray outburst alerts by the BAT instrument aboard the Swift satellite, which triggered follow-up observations by the High Energy Stereoscopic System (H.E.S.S.). H.E.S.S. observed SGR 1935+2154 for 2 hr on 2020 April 28. The observations are coincident with X-ray bursts from the magnetar detected by INTEGRAL and Fermi-GBM, thus providing the first very high energy gamma-ray observations of a magnetar in a flaring state. High-quality data acquired during these follow-up observations allow us to perform a search for short-time transients. No significant signal at energies E > 0.6 TeV is found, and upper limits on the persistent and transient emission are derived. We here present the analysis of these observations and discuss the obtained results and prospects of the H.E.S.S. follow-up program for soft gamma-ray repeaters.

We present the SEMLA (Signal Extraction using Machine Learning for ALTO) analysis method, developed for the detection of E>200 GeV γ rays in the context of the ALTO wide-field-of-view atmospheric shower array R&D project. The scientific focus of ALTO is extragalactic γ-ray astronomy, so primarily the detection of soft-spectrum γ-ray sources such as Active Galactic Nuclei and Gamma Ray Bursts. The current phase of the ALTO R&D project is the optimization of sensitivity for such sources and includes a number of ideas which are tested and evaluated through the analysis of dedicated Monte Carlo simulations and hardware testing. In this context, it is important to clarify how data are analysed and how results are being obtained. SEMLA takes advantage of machine learning and comprises four stages: initial event cleaning (stage A), filtering out of poorly reconstructed γ-ray events (stage B), followed by γ-ray signal extraction from proton background events (stage C) and finally reconstructing the energy of the events (stage D). The performance achieved through SEMLA is evaluated in terms of the angular, shower core position, and energy resolution, together with the effective detection area, and background suppression. Our methodology can be easily generalized to any experiment, provided that the signal extraction variables for the specific analysis project are considered.

The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.