Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks

Details Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks

2003-06-19

arxiv.org/abs/astro-ph/0306390v1

Mon.Not.Roy.Astron.Soc.348:1038,2004

Astronomy and Astrophysics

Astrophysics

Submitted to MNRAS; 9 pages; University of Sussex, UK. Postscript containing higher resolution versions of figures 2 and 3 is

Scientific paper

10.1111/j.1365-2966.2004.07429.x

Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen data, it is found that correlations between predicted and actual properties are around 0.9 with rms errors of order ten per cent. Thus, given a representative training set, these properties may be reliably estimated for galaxies in the survey for which there are no spectra and without human intervention.

Affiliated with

Also associated with

No associations

Rating

Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-402345