Source Artefact Classification in Interferometric Images using - PowerPoint PPT Presentation

Source Artefact Classification in Interferometric Images using Machine Learning Arun Aniyan SKA SA & Rhodes University Cape Town , South Africa

Motivation The field around 3C147, at 5 million dynamic range using 21cm JVLA data. Smirnov et. al 2015

Motivation 1. Improving reliability of source finders and thus creating better source catalogues. 
 2. Reducing the steps in the reduction

Objective Distinguishing real sources from artefacts in radio interferometric images in order to make reliable sources catalogs

Dataset Generation Simulation pipeline with Meqtrees Noordam & Smirnov , 2010 http://meqtrees.net/

Dataset Generation Generate sources in Extract sources with random positions PyBDSM Cross match with known Induce DDE + pointing sky model & PyBDSM errors model Extract PyBDSM + Generate Images for “n” hours of observations generated features

Dataset Generation Generated simulated skies for the JVLA in C-configuration using L-band Generated Images for observational periods from 1 hr to 25 hrs Ran PyBDSM to get catalogues of sources and artefacts with their features.

Feature Extraction 1. Flux features 
 Total flux, Peak flux 2. Axis-Angle features 
 FWHM of deconvolved major axis etc 3. Nearest bright source features 
 Distance to nearest bright source Total 28 features

Results Classifier Accuracy Recall Decision Tree 88.67 92.05 KNN 95.92 99.94 Random 95.22 99.18 Forest Naive Bayes 82.04 84.75

Feature Analysis


 Feature Analysis Features rich in discriminatory power 1. Nearest bright source features 2 . Flux features 


Conclusion and Future Direction High accuracy Classification of Sources and Artefacts Identification of useful features for classification Development of Convolution Neural Network

Thank You ! arun@ska.ac.za