Speaker
Description
The acceleration mechanisms powering the emission of high-energy gamma-rays within the jets of active galactic nuclei are continuously the target of investigations by the astroparticle physics community. While not yet fully understood, the community has advanced its knowledge of the classification of the acceleration processes, among other things, through the analysis of data from Imaging Air Cherenkov Telescopes (IACT). Notably, the analysis of data from highly luminous BL Lac sources, which form a subclass of blazars, allows for insights into the spectra and acceleration process classification in the high-energy regime between 100 Gev and 30 TeV.
To dive deeper into the astrophysical understanding of these objects, it becomes necessary to analyze larger amounts of data, corresponding to timescales much longer than a typical analysis. However, the data of the MAGIC Telescopes that is used within this analysis is continuously influenced by the variability of the night-sky background, weather, and zenith of the source, since IACTs utilize Earth's atmosphere as their detector volume. This makes the analysis more time-intensive with respect to the parameter space of interest than that of atmosphere-independent satellite observatories, like FERMI.
In order to tackle this challenge in data processing, the database-driven automation software autoMAGIC is being developed. This tool offers the possibility to reliably analyze larger timescales of data featuring broad parameter spaces with respect to observational data-taking parameters, in a reproducible manner. Utilizing autoMAGIC, this talk will present the analysis of long-term data of chosen BL Lac objects from the MAGIC Telescopes, give insights into the spectral parameters of the source throughout the extent of the analyzed timeframe of MAGIC data and demonstrate its promise for the data legacy of the MAGIC Collaboration.
