Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural network correction of astrometric chromaticity   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.

20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
1
The categories of this paper are: cs
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom