The examples provided in this page include two basic tutorials to introduce Auger Open Data, as well as science analysis examples. The two tutorials show how to read the CSV summary files and the JSON pseudo-raw data, while the analysis codes are simplified reimplementation of parts of published Auger analyses:
The analysis examples use the most-updated version of the Auger data sets, which differ slightly from those used for the publications because of improvements to the reconstruction and calibration. Also, since, according to the Auger Open Data policy, the fraction of data which is public is currently only 10% of the actual Auger data samples, the statistical significances are reduced with respect to what can be achieved with the full dataset.
These example codes recall the spirit of the original analysis, but, for the sake of simplicity, the more advanced analysis methods of the original papers are omitted. Nevertheless, the examples provide a qualitative insight about how the original results were obtained, and the more complex analysis methods can totally be implemented on the released data.
This notebook is a collection of examples that allows the user to explore the content of the summary file and to apply some basic analysis methods.
In particular, the examples explain how to:
In addition to standard python libraries (numpy, pandas and matplotlib) the notebook uses some libraries useful to generate interactive plots, and imports the auxiliary files ‘sdMap.csv’ and ‘fdPixelMap.csv’.
This notebook is a collection of examples that allows the user to explore the content of a json file and to produce some plots using pseudo-raw and higher level data.
Since the structure of files changes according to the event type (SD reconstructed, hybrid), different plots are produced according to the JSON file chosen.
For a JSON file including an SD-reconstructed event, the examples show how to plot the pmt-signals of each station, the shower footprint at ground, and the lateral distribution function.
For a file including an hybrid event, the photons-trace in the FD telescopes and the profile of the energy deposited in the atmosphere are plotted.
The notebook uses only standard python libraries (numpy, pandas and matplotlib) and imports the auxiliary files ‘sdMap.csv’ and ‘fdPixelMap.csv’.
The search for anisotropies on large angular scales in the distribution of the arrival directions of vertical cosmic rays (zenith < 60 degrees) detected with the Pierre Auger Surface Detector (SD) is made by looking for nonuniformities in the distribution of the observed showers in right ascension. This is because for arrays of detectors that operate with close to 100% efficiency, the total exposure as a function of this angle is almost constant. A search for the first harmonic modulation in right ascension is performed in two energy bins (between 4 and 8×1018 eV and above 8×1018 eV ) by applying the classical Rayleigh formalism, slightly modified to account for small nonuniformities in the exposure of the array. Details are given in Science 357 (2017) 1266 (arxiv).
The estimation of the energy spectrum of the vertical cosmic-rays (zenith < 60 degrees) detected with the Pierre Auger Surface Detector (SD) is made by counting the number of observed showers in differential bins and dividing by the exposure. The bin sizes are selected to be equal in the logarithm of the energy, such that the width corresponds approximately to the energy resolution in the lowest energy bin. The latter is chosen to start at 2.5×1018 eV, as this is the energy above which the SD acceptance becomes independent of the mass and energy of the primary cosmic ray. Details are given in Physical Review D 102 (2020) 062005 (arxiv).
The estimation of the atmospheric depth, Xmax, at which the longitudinal development of a cosmic-ray shower reaches its maximum, relies on the reconstruction of the longitudinal profile of events measured by the Pierre Auger Fluorescence Detector (FD), and at least one coincident Surface Detector (SD) station (so-called hybrid events). By building the Xmax distributions in differential energy bins above 1018 eV, the energy dependence of their mean and standard deviation is derived and compared to those obtained from simulations of showers produced by proton and iron primaries. Details are given in Physical Review D 90 (2014) 122005 (arxiv).
The estimation of the proton-air cross section for particle production at the center-of-mass energy per nucleon of 57 TeV is achieved by analyzing the shape of the distribution of the atmospheric depth, Xmax, at which the longitudinal development of a cosmic-ray shower reaches its maximum. The tail of the Xmax distribution is sensitive to the proton-air cross section, because the depth at which proton-induced showers maximize is deeper in the atmosphere than for showers from heavier nuclei. Xmax is estimated by reconstructing the longitudinal profile of events measured by the Pierre Auger Fluorescence Detector (FD), and at least one coincident Surface Detector (SD) station (so-called hybrid events). Details are given in Physical Review Letters 109 (2012) 062002 (arxiv).
The energy estimation for the vertical events (zenith < 60 degrees) recorded by the Pierre Auger Surface Detector (SD) relies on the calibration of its energy estimator, the shower size S(1000), i.e. the signal at 1000 m from the shower impact at the ground, or shower core, in the plane of the shower front. To calibrate the shower size we profit from the measurement of the shower calorimetric energy performed with the Fluorescence Detector (FD) for a subsample of high quality events simultaneously recorded by both the SD and FD, the so-called golden hybrid events. Details are given in Physical Review D 102 (2020) 062005 (arxiv).
Atmospheric effects on the development of extensive air showers can be understood in terms of local changes in atmospheric parameters. The impact on the energy reconstruction is described in JINST 12 P02006 (2017) , (arXiv). In this notebook we show how the energy estimator can be corrected to account for such effects.