Pierre Ager public data above 32 EeV

This service provides data analysis for publicly available data of Pierre Auger Observatory (PAO), see Pierre Auger collaboration, arXiv:2206.13492. The service extracts upper limits on the Ultra-High-Energy Cosmic Ray (UHECR) flux from different sky regions, using the "aperture photometry" (or, on occasion, "UHECRometry") method.

The data analysis can be launched either using the MMODA frontend interface or through a Python API from e.g. a Jupyter notebook on a user laptop. The main parameter panel generic for all analysis services allows selecting the source of interest, based on its name or coordinates:

The instrument specific parameter panel allows selecting the analysis parameters.

The flux estimates or upper limits will be extracted in a number of logarithmically spaced energy bins between minimal and maximal energy. The signal events will be collected from a circular region around the reference source sky direction (Cen A in the example considered in this help) of the radius given as the analysis parameter. The background will be estimated from a constant Declination strip spanning all Right Ascensions and with the width twice the radius of the source region, as shown on the illustration below:

The analysis can be launched by pressing the Submit button at the bottom of the parameter panel. The product display panel that appears upon the completion of data analysis shows the list of data products: the astropy table with flux upper limits or estimates and its visualisation as a png picture. Clicking on the View buttons next to the data product names provides a possibility to view and download the data product files. The "Query parameters" button provides the metadata with the analysis parameters. The "API code" button displays the Python API code that can be copy-pasted into a python code (e.g. on the user laptop) to request the data product.

Finally, clicking on the Jupyter icon one can see the workflow used for generation of the flux estimates and upper limits.

The python notebook used for the service can be found in the GitLab repository.