Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters

Details Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters

Jun 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007chjaa...7..448l&link_type=abstract

Chinese Journal of Astronomy and Astrophysics, Volume 7, Issue 3, pp. 448-456 (2007).

Astronomy and Astrophysics

Astrophysics

4

Scientific paper

We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 (SDSS DR2) Galaxy Sample using artificial neural networks (ANNs). Different input sets based on various parameters (e.g. magnitude, color index, flux information) are explored. Mainly, parameters from broadband photometry are utilized and their performances in redshift prediction are compared. While any parameter may be easily incorporated in the input, our results indicate that using the dereddened magnitudes often produces more accurate photometric redshifts than using the Petrosian magnitudes or model magnitudes as input, but the model magnitudes are superior to the Petrosian magnitudes. Also, better performance results when more effective parameters are used in the training set. The method is tested on a sample of 79 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddened magnitudes, the rms error in redshift estimation is σz = 0.020184. The ANN is highly competitive tool compared to the traditional template-fitting methods when a large and representative training set is available.

Affiliated with

Also associated with

No associations

Rating

Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters 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 Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-877695