# Observation data pre-processing and scientific data products generation of POLAR

## TL;DR

This paper describes the data pre-processing pipeline and algorithms developed for POLAR, a space-borne detector launched in 2016, which converts raw detection data into scientific data products for gamma-ray burst polarization studies.

## Contribution

It introduces a comprehensive data processing pipeline and core algorithms specifically designed for POLAR's in-orbit data, enabling efficient generation of scientific data products.

## Key findings

- Pipeline successfully applied to POLAR data in orbit
- Efficient pre-processing of large volumes of raw data
- Generation of high-level scientific data products

## Abstract

POLAR is a compact space-borne detector initially designed to measure the polarization of hard X-rays emitted from Gamma-Ray Bursts in the energy range 50-500keV. This instrument was launched successfully onboard the Chinese space laboratory Tiangong-2 (TG-2) on 2016 September 15. After being switched on a few days later, tens of gigabytes of raw detection data were produced in-orbit by POLAR and transferred to the ground every day. Before the launch date, a full pipeline and related software were designed and developed for the purpose of quickly pre-processing all the raw data from POLAR, which include both science data and engineering data, then to generate the high level scientific data products that are suitable for later science analysis. This pipeline has been successfully applied for use by the POLAR Science Data Center in the Institute of High Energy Physics (IHEP) after POLAR was launched and switched on. A detailed introduction to the pipeline and some of the core relevant algorithms are presented in this paper.