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Abstract: We report an estimation of the injected mass composition of ultrahigh energy cosmic rays (UHECRs) at energies higher than 10 EeV. The composition is inferred from an energy-dependent sky distribution of UHECR events observed by the Telescope Array surface detector by comparing it to the Large Scale Structure of the local Universe. In the case of negligible extragalactic magnetic fields (EGMFs), the results are consistent with a relatively heavy injected composition at E ∼ 10 EeV that becomes lighter up to E ∼ 100 EeV, while the composition at E > 100 EeV is very heavy. The latter is true even in the presence of highest experimentally allowed extragalactic magnetic fields, while the composition at lower energies can be light if a strong EGMF is present. The effect of the uncertainty in the galactic magnetic field on these results is subdominant.
Keywords: ultrahigh energy cosmic rays (UHECRs), Large Scale Structure, extragalactic magnetic fields, UHECR propagation, Telescope Array surface detector, UHECR mass composition, UHECR arrival directions
Published in RUNG: 23.04.2025; Views: 42; Downloads: 0
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Abstract: Optical emissions associated with Terrestrial Gamma ray Flashes (TGFs) have recently become important subjects in space‐based and ground‐based observations as they can help us understand how TGFs are produced during thunderstorms. In this paper, we present the first time‐resolved leader spectra of the optical component associated with a downward TGF. The TGF was observed by the Telescope Array Surface Detector (TASD) simultaneously with other lightning detectors, including a Lightning Mapping Array (LMA), an INTerFerometer (INTF), a Fast Antenna (FA), and a spectroscopic system. The spectroscopic system recorded leader spectra at 29,900 frames per second (33.44 s time resolution), covering a spectral range from 400 to 900 nm, with 2.1 nm per pixel. The recordings of the leader spectra began 11.7 ms before the kA return stroke and at a height of 2.37 km above the ground. These spectra reveal that optical emissions of singly ionized nitrogen and oxygen occur between 167 s before and 267 s after the TGF detection, while optical emissions of neutrals (H I, 656 nm; N I, 744 nm, and O I, 777 nm) occur right at the moment of the detection. The time‐dependent spectra reveal differences in the optical emissions of lightning leaders with and without downward TGFs.
Keywords: Telescope Array Surface Detector, terrestrial gamma‐ray flashes, time‐resolved tgf leader spectra
Published in RUNG: 22.04.2025; Views: 77; Downloads: 0
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Abstract: We report an investigation of the mass composition of cosmic rays with energies from 3 to 100 EeV (1 EeV = 10[sup]18 eV) using the distributions of the depth of shower maximum Xmax. The analysis relies on ∼50,000 events recorded by the surface detector of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm. Above energies of 5 EeV, the dataset offers a 10-fold increase in statistics with respect to fluorescence measurements at the Observatory. After cross-calibration using the fluorescence detector, this enables the first measurement of the evolution of the mean and the standard deviation of the Xmax distributions up to 100 EeV. Our findings are threefold: (i) The evolution of the mean logarithmic mass toward a heavier composition with increasing energy can be confirmed and is extended to 100 EeV. (ii) The evolution of the fluctuations of Xmax toward a heavier and purer composition with increasing energy can be confirmed with high statistics. We report a rather heavy composition and small fluctuations in Xmax at the highest energies. (iii) We find indications for a characteristic structure beyond a constant change in the mean logarithmic mass, featuring three breaks that are observed in proximity to the ankle, instep, and suppression features in the energy spectrum.
Keywords: ultra-high-energy cosmic rays, UHECRs, extensive air showers, Pierre Auger Observatory, UHECR mass composition, depth of shower maximum, fluorescence detector, surface detector, deep learning
Published in RUNG: 20.01.2025; Views: 689; Downloads: 8
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Abstract: To understand the physics of cosmic rays at the highest energies, it is mandatory to have an accurate knowledge of their mass composition. Since the mass of the primary particles cannot be measured directly, we have to rely on the analysis of mass-sensitive observables to gain insights into this composition. A promising observable for this purpose is the number of muons at the ground relative to that of an air shower induced by a proton primary of the same energy and inclination angle, commonly referred to as the relative muon number �μ. Due to the complexity of shower footprints, the extraction of �μ from measurements is a challenging task and intractable to solve using analytic approaches. We, therefore, reconstruct �μ by exploiting the spatial and temporal information of the signals induced by shower particles using neural networks. Using this data-driven approach permits us to tackle this task without the need of modeling the underlying physics and, simultaneously, gives us insights into the feasibility of such an approach. In this contribution, we summarize the progress of the deep-learning-based approach to estimate �μ using simulated surface detector data of the Pierre Auger Observatory. Instead of using single architecture, we present different network designs verifying that they reach similar results. Moreover, we demonstrate the potential for estimating �μ using the scintillator surface detector of the AugerPrime upgrade.
Keywords: ultra-high energy cosmic rays, Pierre Auger Observatory, AugerPrime, surface detector
Published in RUNG: 23.01.2024; Views: 2209; Downloads: 11
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Abstract: Exploring physics at energies beyond the reach of human-built accelerators by studying cosmic rays requires an accurate reconstruction of their energy. At the highest energies, cosmic rays are indirectly measured by observing a shower of secondary particles produced by their interaction in the atmosphere. At the Pierre Auger Observatory, the energy of the primary particle is either reconstructed from measurements of the emitted fluorescence light, produced when secondary particles travel through the atmosphere, or shower particles detected with the surface detector at the ground. The surface detector comprises a triangular grid of water-Cherenkov detectors that measure the shower footprint at the ground level. With deep learning, large simulation data sets can be used to train neural networks for reconstruction purposes. In this work, we present an application of a neural network to estimate the energy of the primary particle from the surface detector data by exploiting the time structure of the particle footprint. When evaluating the precision of the method on air shower simulations, we find the potential to significantly reduce the composition bias compared to methods based on fitting the lateral signal distribution. Furthermore, we investigate possible biases arising from systematic differences between simulations and data.
Keywords: ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, neural network
Published in RUNG: 22.01.2024; Views: 2209; Downloads: 5
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