# DmpIRFs and DmpST: DAMPE Instrument Response Functions and Science Tools   for Gamma-Ray Data Analysis

**Authors:** Kai-Kai Duan, Wei Jiang, Yun-Feng Liang, Zhao-Qiang Shen, Zun-Lei Xu,, Yi-Zhong Fan, Fabio Gargano, Simone Garrappa, Dong-Ya Guo, Shi-Jun Lei, Xiang, Li, Mario Nicola Mazziotta, Maria Fernanda Munoz Salinas, Meng Su, Valerio, Vagelli, Qiang Yuan, Chuan Yue, Stephan Zimmer

arXiv: 1904.13098 · 2019-09-25

## TL;DR

This paper introduces the DAMPE instrument response functions and a dedicated software tool, DmpST, to improve gamma-ray data analysis for dark matter and astrophysics research, utilizing high-statistics simulations and likelihood methods.

## Contribution

It provides accurate instrument response functions and a specialized analysis software for DAMPE gamma-ray data, enhancing analysis capabilities.

## Key findings

- Validated the instrument response functions with simulations.
- Developed DmpST software for efficient data analysis.
- Applied maximum likelihood method for source disentanglement.

## Abstract

GeV gamma ray is an important observation target of DArk Matter Particle Explorer (DAMPE) for indirect dark matter searching and high energy astrophysics. We present in this work a set of accurate instrument response functions of DAMPE (DmpIRFs) including the effective area, point-spread function and energy dispersion that are crucial for the gamma-ray data analysis based on the high statistics simulation data. A dedicated software named DmpST is developed to facilitate the scientific analyses of DAMPE gamma-ray data. Considering the limited number of photons and the angular resolution of DAMPE, the maximum likelihood method is adopted in the DmpST to better disentangle different source components. The basic mathematics and the framework regarding this software are also introduced in this paper.

## Full text

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## Figures

9 figures with captions in the complete paper: https://tomesphere.com/paper/1904.13098/full.md

## References

18 references — full list in the complete paper: https://tomesphere.com/paper/1904.13098/full.md

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Source: https://tomesphere.com/paper/1904.13098