Hello!

1. Hello!

2. Photometric Identification of Quasars Rita Sinha, N. Sajeeth Philip & Ajit Kembhavi

3. Colour-Colour Diagram

4. SDSS-DR5

5. The Sample • All Unresolved objects with psf magnitudes in u, b, u,g,I,r,z, redshifts, extinctions… • Stars, quasars with z<2,3 and high redshit quasars with z>2.3 • Quasars z<2.3 79,234 • Quasars z> 2.3 11,217 • Stars 154,925

11. Difference Boosting Neural Network • DBNN is a Bayesian classification tool • It follows the Bayesian rule for updating weights for each outcome during the training and testing process • It focuses on differences in the system and boosts (updates) its weights to to highlight differences in the multiclass problem • DBNN is fast, robust and accurate in classification • It assigns a confidence value to every prediction that it makes.

12. Training and Testing Sample Data Use adaptive data selection to identify training set Train the network Test to determine accuracy

13. Training Set • Shuffle the data, then divide the sample set into sets of 10,000 objects • Use the colours u-g, g-r, r-i, i-z • Train the DBNN, and use the trained network to classify objects form the whole set • Use simple colour cuts to obtain subsamples for training and testing

14. R4 R3 R0 R1 R2

18. Thank You!