Bibcode
DOI
Serra-Ricart, Miquel; Trapero, Joaquin; Beckman, John E.; Garrido, Lluis; Gaitan, Vicens
Bibliographical reference
The Astronomical Journal (ISSN 0004-6256), vol. 109, no. 1669, p. 312-318
Advertised on:
1
1995
Citations
4
Refereed citations
4
Description
In this paper we propose a method for interpolating multidimensional
unbinned data, which could also be sparse, using artificial neural
network techniques. An artificial example is first presented in order to
show the reliability and potential of the neural network interpolator. A
robust behavior is found. We apply the technique to the mapping of a
cloud of interstellar atomic hydrogen. The cloud was mapped in H I at 21
cm and we find the neural network method ideal for interpolating the
unevenly sampled data, yielding a map from which the global physical
parameters of the cloud can be readily obtained.