V. Khramtsov, V. Akhmetov


We present new catalogue of ~8,500,000 extragalactic objects as a result of automatic classification of WISE and SuperCOSMOS (SCOS) cross-identification product.  The  main  goal  is  to  create  a  set  of  candidates  in extragalactic  objects  due  to  colour  (photometric)  features through machine learning techniques. Extragalactic sources were separated from stars in high-dimensional colour space using Support Vector Machine (SVM) classifier. Construction of catalogue of the extragalactic objects is based  on  the  four  important  procedures:  1.  Cross-identification of the WISExSCOS catalogues. 2. Training set creation (Gaia DR1 and 2MASX\XSC data). 3. Feature engineering  and  colour-space  constructing  for  further learning  and  classification.  4.  Fine-tuning  of  SVM  and separation and classification processes. In  result  we  got  high-accuracy  (~98%)  algorithm  for extragalactic  source  identification  in  built  colour  space. Product  of  algorithm  realization  is  presented  as photometric catalogue of the extragalactic objects and can be used for further astronomical investigations. 

Ключові слова

Catalogues; statistics; extragalactic objects; Methods; machine learning; data analysis

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