An Automated Classification Algorithm for Multi-wavelength Data

1. LAMOST An Automated Classification Algorithm for Multi-wavelength Data Yanxia Zhang, Ali Luo,Yongheng Zhao National Astronomical Observatories, China 2005.8.16, Lijiang

2. ROSAT ~keV DSS Optical IRAS 25m 2MASS 2m GB 6cm WENSS 92cm NVSS 20cm IRAS 100m Astronomy facing “data avalanche”

3. Necessity Necessity Is the Mother of Invention Data avalanche Virtual Observatories DM & KDD

4. Data Mining & KDD Knowledge Pattern evaluation • DM—core of KDD DM task selection Data warehouse Data cleaning Data federation Database

5. One Task of DM:Classification The scheme of classification of multiwavelength data Training set Classification method Feature selection Selected features Test set classifier Validated set predict all features New data Cross identification

6. Data sample Near infrared 2MASS J,H,K optical USNO A2.0 B,R X ray ROSAT CR,HR1,HR2, ext,extl

7. Known sample star SIMBAD Normal galaxies RC3 AGNs Veron(2000)

8. Feature Selection Parameters:B+2.5lgCR,J+2.5lgCR,B-R,J-H,H-K,lgCR, HR1,HR2,ext,extl Methods: ReliefF Result of feature selection:

9. Classification Method: Naïve Bayes classifier Classification results for three situations

10. Summary 1. By feature selection, we can deal with high dimensional data, and select important attributes, thus improve the efficiency and effect of classification. 2. The Naïve Bayes algorithm is an robust method to classify multiwavelength data with high accuracy of classification. It is not only used for multiwavelength data, but also for other data, such as photometric data, spectra data, image data or the combined data of these types of data. 3. With the classifier, it is helpful to preselect source candidates for large surveys and classify the new data. 4. The methods will be part of VO toolkits.

11. Thanks a lot !!!