Observation of radio signals from air showers at the Pierre Auger Observatory

In order to get the primary energy of cosmic rays from their extensive air showers using the fluorescence detection technique, the invisible energy should be added to the measured calorimetric energy. The invisible energy is the energy carried away by particles that do not deposit all their energy in the atmosphere. It has traditionally been calculated using Monte Carlo simulations that are dependent on the assumed primary particle mass and on model predictions for neutrino and muon production. In this work the invisible energy is obtained directly from events detected by the Pierre Auger Observatory. The method applied is based on the correlation of the measurements of the muon number at the ground with the invisible energy of the showers. By using it, the systematic uncertainties related to the unknown mass composition and to the high energy hadronic interaction models are significantly reduced, improving in this way the estimation of the energy scale of the Observatory.

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Towards online triggering for the radio detection of air showers using deep neural networks

EPJ Web of Conferences ◽

10.1051/epjconf/201921603004 ◽

2019 ◽

Vol 216 ◽

pp. 03004 ◽

Cited By ~ 1

Author(s):

Florian Föhrer ◽

Tom Charnock ◽

Anne Zilles ◽

Matias Tueros

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Air Showers ◽

Air Shower ◽

Single Antenna ◽

Radio Detection ◽

Radio Signals ◽

Trigger Algorithm ◽

Background Events ◽

Online Reconstruction

The detection of air-shower events via radio signals requires the development of a trigger algorithm for clean discrimination between signal and background events in order to reduce the data stream coming from false triggers. In this contribution we will describe an approach to trigger air-shower events on a single-antenna level aswell as performing an online reconstruction of the shower parameters using neural networks.

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Classification and recovery of radio signals from cosmic ray induced air showers with deep learning

Journal of Instrumentation ◽

10.1088/1748-0221/14/04/p04005 ◽

2019 ◽

Vol 14 (04) ◽

pp. P04005-P04005 ◽

Cited By ~ 3

Author(s):

M. Erdmann ◽

F. Schlüter ◽

R. Šmída

Keyword(s):

Deep Learning ◽

Cosmic Ray ◽

Air Showers ◽

Radio Signals

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Atmospheric aerosol attenuation effect on FD data analysis at the Pierre Auger Observatory

EPJ Web of Conferences ◽

10.1051/epjconf/201921005008 ◽

2019 ◽

Vol 210 ◽

pp. 05008

Author(s):

Laura Valore ◽

Keyword(s):

Atmospheric Aerosol ◽

Uv Light ◽

Pierre Auger Observatory ◽

Event Analysis ◽

Air Showers ◽

Fluorescence Detector ◽

Attenuation Effect ◽

Laser Facility ◽

Shower Maximum ◽

Light Profiles

The atmospheric aerosol monitoring system of the Pierre Auger Observatory has been operating smoothly since 2004. Two laser facilities (Central Laser Facility, CLF and eXtreme Laser Facility, XLF) fire sets of 50 shots four times per hour during FD shifts to measure the highly variable hourly aerosol attenuation to correct the longitudinal UV light profiles of the Extensive Air Showers detected by the Fluorescence Detector. Hourly aerosol attenuation loads (Vertical Aerosol Optical Depth) are used to correct the measured profiles. Two techniques are used to determine the aerosol profiles, which have been proven to be fully compatible. The uncertainty in the VAOD profiles measured consequently leads to an uncertainty on the energy and on the estimation of the depth at the maximum development of a shower (Xmax) of the event in analysis. To prove the validity of the aerosol attenuation measurements used in FD event analysis, the flatness of the ratio of reconstructed SD to FD energy as a function of the aerosol transmission to the depth of shower maximum has been verified.

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