Neural network correction of astrometric chromaticity

Details Neural network correction of astrometric chromaticity Neural network correction of astrometric chromaticity

2005-07-22

arxiv.org/abs/astro-ph/0507541v1

Mon.Not.Roy.Astron.Soc.362:1483,2005

Astronomy and Astrophysics

Astrophysics

9 pages, 8 figures Accepted by Monthly Notices of the Royal Astronomical Society

Scientific paper

10.1111/j.1365-2966.2005.09422.x

In this paper we deal with the problem of chromaticity, i.e. apparent position variation of stellar images with their spectral distribution, using neural networks to analyse and process astronomical images. The goal is to remove this relevant source of systematic error in the data reduction of high precision astrometric experiments, like Gaia. This task can be accomplished thanks to the capability of neural networks to solve a nonlinear approximation problem, i.e. to construct an hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with conveniently chosen moments, evaluated along the y axis. The technique proposed, in the current framework, reduces the initial chromaticity of few milliarcseconds to values of few microarcseconds.

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