The Global sphere reconstruction (GSR) - Demonstrating an independent implementation of the astrometric core solution for Gaia

TL;DR

This paper presents an independent implementation of Gaia's global sphere reconstruction to validate astrometric results, demonstrating sub-microarcsecond accuracy in simulations and aiming to improve data quality assurance.

Contribution

It introduces a new independent global sphere reconstruction method (GSR) for Gaia, enabling validation of the primary astrometric solution without replicating the entire data processing pipeline.

Findings

GSR reproduces AGIS results at sub-microarcsecond level in simulations

GSR effectively compares and validates Gaia's astrometric solutions

Further development aims to handle real data and discrepancies

Abstract

Context. The Gaia ESA mission will estimate the astrometric and physical data of more than one billion objects, providing the largest and most precise catalog of absolute astrometry in the history of Astronomy. The core of this process, the so-called global sphere reconstruction, is represented by the reduction of a subset of these objects which will be used to define the celestial reference frame. As the Hipparcos mission showed, and as is inherent to all kinds of absolute measurements, possible errors in the data reduction can hardly be identified from the catalog, thus potentially introducing systematic errors in all derived work. Aims. Following up on the lessons learned from Hipparcos, our aim is thus to develop an independent sphere reconstruction method that contributes to guarantee the quality of the astrometric results without fully reproducing the main processing chain. Methods. Indeed, given the unfeasibility of a complete replica of the data reduction pipeline, an astrometric verification unit (AVU) was instituted by the Gaia Data Processing and Analysis Consortium (DPAC). One of its jobs is to implement and operate an independent global sphere reconstruction (GSR), parallel to the baseline one (AGIS, namely Astrometric Global Iterative Solution) but limited to the primary stars and for validation purposes, to compare the two results, and to report on any significant differences. Results. Tests performed on simulated data show that GSR is able to reproduce at the sub-$\mu$as level the results of the AGIS demonstration run presented in Lindegren et al. (2012). Conclusions. Further development is ongoing to improve on the treatment of real data and on the software modules that compare the AGIS and GSR solutions to identify possible discrepancies above the tolerance level set by the accuracy of the Gaia catalog.