Automated detection of galaxy groups through probabilistic hough transform

Rafee T. Ibrahem*, Peter Tino, Richard J. Pearson, Trevor J. Ponman, Arif Babul

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Galaxy groups play a significant role in explaining the evolution of the universe. Given the amounts of available survey data, automated discovery of galaxy groups is of utmost interest. We introduce a novel methodology, based on probabilistic Hough transform, for finding galaxy groups embedded in a rich background. The model takes advantage of a typical signature pattern of galaxy groups known as “fingersof-God”. It also allows us to include prior astrophysical knowledge as an inherent part of the method. The proposed method is first tested in large scale controlled experiments with 2-D patterns and then verified on 3-D realistic mock data (comparing with the well-known friends-of-friends method used in astrophysics). The experiments suggest that our methodology is a promising new candidate for galaxy group finders developed within a machine learning framework.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Lui
Number of pages9
ISBN (Electronic) 9783319265551
ISBN (Print)9783319265544
Publication statusPublished - 2015
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference22nd International Conference on Neural Information Processing, ICONIP 2015


  • Galaxy group finder
  • Pattern Recognition
  • Probabilistic Hough transform

ASJC Scopus subject areas

  • General Computer Science
  • Theoretical Computer Science


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