Astronomy and Astrophysics – Astronomy
Scientific paper
Jul 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010spie.7740e..97w&link_type=abstract
Software and Cyberinfrastructure for Astronomy. Edited by Radziwill, Nicole M.; Bridger, Alan. Proceedings of the SPIE, Volume 7
Astronomy and Astrophysics
Astronomy
Scientific paper
We employ k-nearest neighbor algorithm (KNN) for photometric redshift measurement of quasars with the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). KNN is an instance learning algorithm where the result of new instance query is predicted based on the closest training samples. The regressor do not use any model to fit and only based on memory. Given a query quasar, we find the known quasars or (training points) closest to the query point, whose redshift value is simply assigned to be the average of the values of its k nearest neighbors. Three kinds of different colors (PSF, Model or Fiber) and spectral redshifts are used as input parameters, separatively. The combination of the three kinds of colors is also taken as input. The experimental results indicate that the best input pattern is PSF + Model + Fiber colors in all experiments. With this pattern, 59.24%, 77.34% and 84.68% of photometric redshifts are obtained within ▵z < 0.1, 0.2 and 0.3, respectively. If only using one kind of colors as input, the model colors achieve the best performance. However, when using two kinds of colors, the best result is achieved by PSF + Fiber colors. In addition, nearest neighbor method (k = 1) shows its superiority compared to KNN (k ≠ 1) for the given sample.
Wang Dan
Zhang Yanxia
Zhao Yongheng
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