Gaia data processing. SEAPipe: The source environment analysis pipeline

TL;DR

This paper presents SEAPipe, a pipeline for Gaia data that detects new sources near known ones, comparing two methods and showing the image-subtraction approach is superior in performance.

Contribution

The paper introduces two options for the Gaia Source Environment Analysis pipeline, detailing their algorithms and performance, with the image-subtraction method outperforming the vanilla approach.

Findings

Image-subtraction pipeline has higher completeness and purity.

Performance metrics depend on source magnitude and separation.

The pipeline effectively identifies secondary sources near Gaia primaries.

Abstract

Aims. To describe two potential options for the Source Environment Analysis pipeline, SEAPipe, for the Gaia mission. This pipeline will enable the discovery of sources which are new to Gaia, in the sense that they were not found by the on-board detection algorithm. These additional sources (secondaries) are discoverable in the vicinity of those Gaia sources (primaries) that were found by the on-board detection. Methods. The main algorithmic steps required are described; the 2-dimensional image reconstruction of 1-dimensional transit data, the analysis of these images to find the additional sources present, and the determination of the mean positions, proper motions, parallaxes and brightness of these sources. Additionally, the Monte Carlo simulations used to characterise the performance of the pipelines are described. Results. The performance of the two options for SEAPipe, the vanilla and image-subtraction versions, are compared. Their selection functions are computed in terms of the magnitude of the secondary sources and their angular separations from their corresponding primary source. The completeness and purity of the resultant catalogue of secondary sources as found by each of the pipelines, given the expected magnitude distribution of the primary sources and the magnitude and angular separation distributions of the secondary sources, is also presented. The image-subtraction pipeline is shown to out-perform the vanilla pipeline.