ǂThe ǂtime evolution of the surface detector of the Pierre Auger Observatory
Abstract
The surface detector array of the Pierre Auger Observatory, consisting of 1660 water Cherenkov tanks, has been in operation for nearly 20 years. During this long period of data acquisition, ageing
effects in the detector response have been observed. The temporal evolution of the signals recorded by the surface detector is mostly compensated by continuous calibration with atmospheric muons;
however, effects persist in the signal rise time and in high-level data analysis using neural networks. We have implemented a detailed description of the time evolution of the detector response and of
the uptimes of individual stations in GEANT4-based detector simulations. These new simulations reproduce the observed time dependencies in the data. Using air-shower simulations that take
into account the evolution of individual stations, we show that the reconstructed energy is stable at the sub-percent level, and its resolution is affected by less than 5% in 15 years. For a few
specific stations, the collected light produced by muons has decreased to the point where it is difficult to distinguish it from the electromagnetic background in the calibration histograms. The
upgrade of the Observatory with scintillator detectors mitigates this problem: by requiring a coincidence between the water-Cherenkov and scintillator detectors, we can enhance the muon relative contribution to the calibration histogram. We present the impact and performance of this coincidence calibration method.
Keywords
surface detector;Pierre Auger Observatory;neural networks;air-shower simulations;
Data
| Language: | English |
|---|---|
| Year of publishing: | 2023 |
| Typology: | 1.08 - Published Scientific Conference Contribution |
| Organization: | UNG - University of Nova Gorica |
| UDC: | 52 |
| COBISS: |
182023683
|
| ISSN: | 1824-8039 |
| Views: | 311 |
| Downloads: | 4 |
| Average score: | 0 (0 votes) |
| Metadata: |
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Other data
| Pages: | str. 1-13 |
|---|---|
| Chronology: | 2023 |
| DOI: | 10.22323/1.444.0266 |
| ID: | 22530019 |
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