Preparing and correcting extracted BRITE observations

TL;DR

This paper introduces a set of Python routines designed to prepare and correct BRITE satellite lightcurves for scientific analysis by addressing instrumental effects.

Contribution

The paper presents a new publicly available Python toolkit for processing and correcting BRITE lightcurve data, detailing the methodology and steps involved.

Findings

The routines effectively correct instrumental effects in BRITE lightcurves.

The toolkit is publicly accessible for the scientific community.

The method improves data quality for subsequent analysis.

Abstract

Extracted BRITE lightcurves must be carefully prepared and corrected for instrumental effects before a scientific analysis can be performed. Therefore, we have created a suite of Python routines to prepare and correct the lightcurves, which is publicly available. In this paper we describe the method and successive steps performed by these routines.