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.

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.

The LOw-Frequency ARray (LOFAR) is used to make precise measurements of radio emission from extensive air showers, yielding information about the primary cosmic ray. Interpreting the measured data requires an absolute and frequency-dependent calibration of the LOFAR system response. This is particularly important for spectral analyses, because the shape of the detected signal holds information about the shower development. We revisit the calibration of the LOFAR antennas in the range of 30-80 MHz. Using the Galactic emission and a detailed model of the LOFAR signal chain, we find an improved calibration that provides an absolute energy scale and allows for the study of frequency dependent features in measured signals. With the new calibration, systematic uncertainties of 13% are reached, and comparisons of the spectral shape of calibrated data with simulations show promising agreement. (C) 2019 Elsevier B.V. All rights reserved.

The LOFAR radio telescope is able to measure the radio emission from cosmic ray induced air showers with hundreds of individual antennas. This allows for precision testing of the emission mechanisms for the radio signal as well as determination of the depth of shower maximum X max , the shower observable most sensitive to the mass of the primary cosmic ray, to better than 20 g cm -2 . With a densely instrumented circular area of roughly 320 m 2 , LOFAR is targeting for cosmic ray astrophysics in the energy range 10 16 -10 18 eV. In this contribution we give an overview of the status, recent results, and future plans of cosmic ray detection with the LOFAR radio telescope. © Published under licence by IOP Publishing Ltd.

The LOw Frequency ARay (LOFAR) is a multi-purpose radio antenna array aimed to detect radio signals in the frequency range 10 - 240 MHz, covering a large surface in Northern Europe with a higher density in the Netherlands. Analytical calculations and simulation studies performed in the 2000s indicate a dependence of the radio frequency spectrum on cosmic-ray characteristics. The high number density of radio antennas at the LOFAR core allows to characterise the observed cascade in a detailed way. The radio signal emitted by air showers in the atmosphere has been studied accurately in the 30 - 80 MHz frequency range. The analysis has been conducted on simulated events and on real data detected by LOFAR since 2011. The final aim of this study is to find an independent method to infer information of primary cosmic rays for improving the reconstruction of primary particle parameters. Results show a strong dependence of the frequency spectrum on the distance to the shower axis for both real data and simulations. Furthermore, results show that this method is very sensitive to the precision in reconstructing the position of the shower axis at ground, and to different antenna calibration procedures. A correlation between the frequency spectrum and geometrical distance to the shower maximum development X-max has also been investigated.

The steep decrease of the flux of ultra-high energy cosmic rays (UHECR) provides a challenge to answer the long standing question about their origin and nature. A significant increase in detector volume may be achieved by employing Earth's moon as a detector that is read out using existing Earth-bound radio telescopes by searching for the radio pulses emitted by the particle shower in the lunar rock. In this contribution we will report on the properties of a corresponding detection mode currently under development for the LOFAR Radio telescope.

Cosmic particles hitting Earth's moon produce radio emission via the Askaryan effect. If the resulting radio ns-pulse can be detected by radio telescopes, this technique potentially increases the available collective area for ZeV scale particles by several orders of magnitude compared to current experiments. The LOw Frequency ARray (LOFAR) is the largest radio telescope operating in the optimum frequency regime for this technique. In this contribution, we report on the status of the implementation of the lunar detection mode at LOFAR. © Published under licence by IOP Publishing Ltd.

The radio signals emitted by Extensive Air Showers have been successfully used for the last decade by LOFAR to reconstruct the properties of the primary cosmic rays. Since an effective real-time recognition system for the very short radio pulses is lacking, cosmic-ray acquisition is currently triggered by an external array of particle detector, called LORA, limiting the LOFAR collecting area to the area covered by LORA. A new algorithm for the real-time cosmic-ray detection has been developed for the LOFAR Low Band Antenna, which are sensitive between 10 and 90 MHz, and is here presented together with the latest results.