| |
 |
| Paper: |
Photometric Redshift Estimation of Quasars by Machine Learning |
| Volume: |
527, Astronomical Data Analysis Software and Systems XXIX |
| Page: |
197 |
| Authors: |
Zhang, Y.; Zhang, J.; Han, B.; Qiao, L.; Zhao, Y. |
| Abstract: |
We summarize various techniques for photometric redshift estimation of quasars. Based on datasets from SDSS and WISE databases, we compare the performance of K-nearest neighbours (KNN) with different data preprocessing and four machine learning algorithms with different data quality on this issue. In reality, the more complex algorithms maybe not achieve better performance. The performance of an algorithm is influenced by different factors, such as data preprocessing, sample selection, data quality and so on. In addition, we also put forward a new strategy to improve the accuracy of photometric redshift estimation of quasars. The result shows that this strategy is applicable and efficient for our case. In future, we will apply it on the new survey databases, such as LSST. |
|
|
|
|
|
