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Photometric Identification of Quasars

Rita Sinha, N. Sajeeth Philip & Ajit Kembhavi

Colour-Colour

Diagram

SDSS-DR5

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

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.

Sample Data

Use adaptive data selection to identify training set

Train the network

Test to determine accuracy

Training and Testing

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

R0

R4 R3

R1 R2

Quasars Stars Quasars found

Efficiency

All 66030 51077 86% 94%

R0 57672 9744 99 98

R1 61806 40833 94 6

R2 1146 4486 71 50

Thank You!