Computing sky maps using the open-source package Gammapy and MAGIC data in a standardized format

TL;DR

This paper presents a method using the open-source Gammapy package to generate background acceptance models for MAGIC gamma-ray data, enabling accurate sky map computations with validated results on Crab Nebula observations.

Contribution

It introduces a novel approach to create background models for MAGIC data that account for azimuth and zenith dependencies, enhancing gamma-ray sky analysis.

Findings

Validated background acceptance model with Crab Nebula data

Demonstrated accurate sky map generation using Gammapy

Improved morphological analysis capabilities for MAGIC data

Abstract

The open-source Python package Gammapy, developed for the high-level analysis of gamma-ray data, requires gamma-like event lists combined with the corresponding instrument response functions. For a morphological analysis, these data have to include a background acceptance model. Here we report an approach to generate such a model for the MAGIC telescope data, accounting for the azimuth and zenith dependencies of the MAGIC background acceptance. We validate this method using observations of the Crab Nebula with different offsets from the pointing position.