Photometric Detection ofQuasars

ISRO - Funding
Santhom Computing Centre@St. Thomas College in Kozhencheri

Sheelu Abraham
Ninan Sajeeth PhilipUnderstanding
Analysis
Archives
DataUniverseObjective

Illustration of Machine Learning
Applications in Astronomy
Using SDSS data ArchiveQuasar Colours

Difference Boosting
Neural Network
Object types are identified on the basisof subtle differences in features.

What is Bayes Rule?
Suggests to update your belief on thebasis of evidences in 3 steps.
Prior P(A) : Initial belief
Evidence B : Make an Observation
P(A|B) : Update Prior with a posterior
Repeat until the confidence is acceptable

How to compute the Posterior
Probability?

P(A) is prior belief that the outcome is A
P(A|B) is the updated belief about the outcome
P(B|A) is the likelihood for B to cause A
P(B): probability for B to occur.

Iterative learning

Where Pk stands for theposterior for the actual classand Pk* for that of the predictedclass.

Start with a small and equal value for Wm initially and as
each new data is seen, update Wm along the negative
gradient of the probability estimates – which in effect is thedifference of their posterior estimates.

Training data
Since the entire learning processdepends upon the data, it is importantthat the data used for training has all
the features to be expected in theproblem domain.

Iterative identification of
Principal Samples
Randomly pick up just one example from each classand train the network on it.

Test the network on the remaining data
Look for the example that has failed withmaximum posterior probability from each classand add it to the training data.

REPEAT THE PROCESS UNTIL ALL EXAMPLESARE CORRECTLY CLASSIFIED.

Optimal Data Selection

Classification Results
N am e

SDSS

DBNN%

S tar

72449

68747

9 4 .8 9G a la x y1868

238

1 2 .7 4

Q u a sa r90363

88290

9 7 .7H iz Q S O

3702

3289

8 8 .8 6

S t a r-L a t e

759

184

2 4 .2 4T otal

1691411607489 5 .0 3

What caused failures?

Colors of quasars and stars areindistinguishable at some redshift patches

What caused failures?

Incorrect Spectroscopic classifications

What caused failures?

Incorrect Spectroscopic Classification

Spectral Classification Statistics

Total Number of Failed Objectswith Spectra :111

1647

Objects with wrong classification labels :283

How to reduce failures?
Define training samples with accuratelabels
Increase the parameter space withmultiwavelength observations
Build a hybrid spec-photo network

Usefulness of the method

Outliers detection – objects with peculiar colors areautomatically assigned low confidence values by DBNN.

Usefulness of the method

Outliers detection – objects with peculiar colors areautomatically assigned low confidence values by DBNN.

Objects with Interesting Spectra

Objects with interesting spectra in the list offailed 1647 objects:

119Usefulness of the method
Most suited for candidate selection forfollow up observations
The assigned probability can be used tocompromise between completeness andcontamination on trained networks
Very fast – can classify a million objects in
one second on a modern PC

Question TimeThank Younspp@iucaa.ernet.in
http://www.iucaa.ernet.in/~nspp