Ultra-high energy cosmic rays impinge on the atmosphere and induce air shower cascades, in which huge numbers of particles are produced. By travelling through the Earth’s atmosphere and magnetic field, these particles create a noticeable effect on the electric field at the surface. In this article, we propose a method to map the time dependence of the recorded electric field to the emitted one as a function of slant depth along the shower axis. We derive calibration curves relating the obtained field mapping atmospheric profile to the depth and width of the particle shower longitudinal development. Assuming perfect knowledge of the electric field up to 500MHz in a noise-free scenario, we demonstrate that both the depth of shower maximum, Xmax, and the shower width can be inferred with resolutions of the order of 10g/cm2, depending on observer position with respect to the air shower geometry. In addition, imposing realistic frequency ranges and adding white Gaussian noise, we show that the shower maximum can still be obtained with reasonable resolution. Even in its early stages, the proposed technique provides a way to probe the air shower longitudinal profile with radio-wave measurements and very few antennas, ideal for detection of very inclined events in sparse arrays.
Accurate reconstruction of the electromagnetic shower axis in the AMS-02 Electromagnetic Calorimeter (ECAL) is important for electron and gamma-ray analyses, including calorimeter-based tracking, track-to-cluster matching, and source-oriented studies that rely on precise shower pointing. We present an improved shower-axis reconstruction method based on a deep residual network adapted to the 18 × 72 imaging structure of the AMS-02 ECAL. Using the ECAL energy-deposition image together with the total deposited energy, the network reconstructs both the shower inclination angle and the incident position on the ECAL front surface. The model is trained with electron Monte Carlo samples from 2GeV to 4TeV and validated with independent positron Monte Carlo samples and high-purity cosmic-ray electron data collected by AMS-02 on the International Space Station. Compared with conventional AMS-02 ECAL reconstruction methods, the deep-residual-network approach improves the angular resolution significantly over a broad energy range. For example, relative to the three-dimensional fit method, the angular resolution improves by more than a factor of two at 10GeV, from 2.229° to 0.994°, and by about 31% at 100GeV, from 0.533° to 0.370°. A dedicated gamma-ray Monte Carlo study further shows comparable performance for neutral electromagnetic showers. These results demonstrate that deep residual networks can improve ECAL shower reconstruction and enhance the physics reach of AMS-02 gamma-ray and electron analyses.
The analysis traditionally employed by Imaging Atmospheric Cherenkov Telescopes involves optimizing quality cuts to select a sub-sample of high-quality events. These events are used for the scientific interpretation of the data, employing a single set of Instrument Response Functions (IRFs). All selected events are treated equally and assumed to be well represented by these IRFs, while the rest are discarded. An alternative approach, successfully applied in experiments such as Fermi-LAT, is an event-type-based analysis. This method divides datasets into subsamples, each containing events of a given expected reconstruction quality. IRFs are computed for each subsample independently, improving the accuracy with which IRFs represent the reconstruction quality of each event. The high-level analysis of these subsamples is performed treating them as independent observations, each with their own set of IRFs, and analyzed jointly.In this work we present a proof-of-concept implementation of an event-type-based analysis for the future Cherenkov Telescope Array Observatory (CTAO) using simulated data. A neural network (specifically a multi-layer perceptron) is trained to predict the direction reconstruction error of each event, and the simulated dataset is divided into event types based on this predicted variable. We compute IRFs for each event type and compare them with those from the standard analysis (without event types). Finally, we simulate observations using these event-type-wise IRFs and analyze them with high-level analysis tools to test the performance of both approaches. This implementation demonstrates notable improvements: 25% to 50% boost in spatial resolving power and ∼25% in sensitivity. This boost in performance will have strong implications in the scientific exploitation of the CTAO data, especially in crowded regions such as the Galactic Plane or searching for spectral signatures like Dark Matter annihilation lines.
Multimessenger observations suggest that Seyfert galaxies are promising sources of high-energy neutrinos, but their dense inner environments can strongly suppress the emerging very-high-energy gamma-ray emission. Active galactic nuclei (AGN) undergoing intense accretion episodes can enter a super-Eddington state, in which the accretion flow becomes geometrically and optically thick within a critical radius and develops strong magnetic fields in its innermost region. At the same time, large amounts of matter are expelled from the disk surface in the form of powerful, radiation-driven winds. In this work, we explore a scenario in which the cores of super-Eddington AGN provide suitable conditions for the acceleration of relativistic particles, including hadrons, via magnetic reconnection in a magnetically confined region close to the supermassive black hole. The accelerated hadronic component interacts with the intense photon field of the disk, leading to a copious neutrino flux peaking at 10–100 TeV that may be detectable with current observatories such as IceCube and KM3NeT, while the surrounding outflow efficiently absorbs the accompanying gamma-ray emission from the inner core. We also apply the model to the nearby super-Eddington Seyfert 1 NGC 7469 as a representative case with two reported neutrino events. In this framework, super-Eddington AGN, in particular Seyfert galaxies undergoing transient intense accretion episodes, emerge as plausible hidden neutrino sources, offering a natural explanation for the coexistence of efficient neutrino production and a strongly attenuated gamma-ray counterpart.
We propose that early Super Massive Black Holes (SMBHs) grow at an enhanced Eddington rate facilitated by rotation. Recent theoretical work suggests that rotating Black Holes (BHs) can produce winds or jets carrying electric currents. These currents are driven by a pE-2 momentum spectrum of protons and anti-protons, which can reach EeV energies for near-maximal rotation - independent of the BHs mass. This electric current generated in the ergo-region leads to rapid collisions producing anti-particles and lots of pions. The associated electron population suffers extreme losses in the magnetic field observed, so that the protons, anti-protons, neutrons, anti-neutrons and pions altogether, carry a much larger mass/energy per electron than a single proton, while charge is conserved. This factor can be estimated, depends on the spin of the BH, and allows a significant enhancement of Eddington limited accretion. This effect is proposed to explain the rapid growth of early SMBHs observed by James Webb Space Telescope (JWST). The prediction is that these SMBHs rotate rapidly. When compared with cosmological simulations of structure evolution with SMBH formation and evolution without allowing for BH rotation, a second testable prediction is that the gravitational waves produced in ensuing SMBH mergers are enhanced due to high rotation, with ensuing increased total mass.
