The Cherenkov Telescope Array (CTA) will be the next-generation observatory
in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle
physics. The traditional approach to data analysis in this field is to apply
quality cuts, optimized using Monte Carlo simulations, on the data acquired to
maximize sensitivity. Subsequent steps of the analysis typically use the
surviving events to calculate one set of instrument response functions (IRFs)
to physically interpret the results. However, an alternative approach is the
use of event types, as implemented in experiments such as the Fermi-LAT. This
approach divides events into sub-samples based on their reconstruction quality,
and a set of IRFs is calculated for each sub-sample. The sub-samples are then
combined in a joint analysis, treating them as independent observations. In
previous works we demonstrated that event types, classified using Machine
Learning methods according to their expected angular reconstruction quality,
have the potential to significantly improve the CTA angular and energy
resolution of a point-like source analysis. Now, we validated the production of
event-type wise full-enclosure IRFs, ready to be used with science tools (such
as Gammapy and ctools). We will report on the impact of using such an
event-type classification on CTA high-level performance, compared to the
traditional procedure.Comment: 7 pages, 3 figures, Presented at the 38th International Cosmic Ray
Conference (ICRC 2023), 2023 (arXiv:submit/2309.08219