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ClaRAN: Classifying Radio sources Automatically with Neural networks

Posted: Tue Jun 01, 2021 1:35 am
by Ada Coda
ClaRAN: Classifying Radio sources Automatically with Neural networks

Abstract: ClaRAN (Classifying Radio sources Automatically with Neural networks) classifies radio source morphology based upon the Faster Region-based Convolutional Neutral Network (Faster R-CNN). It is capable of associating discrete and extended components of radio sources in an automated fashion. ClaRAN demonstrates the feasibility of applying deep learning methods for cross-matching complex radio sources of multiple components with infrared maps. The promising results from ClaRAN have implications for the further development of efficient cross-wavelength source identification, matching, and morphology classifications for future radio surveys.

Credit: Wu, Chen; Wong, Oiwei Ivy; Rudnick, Lawrence; Shabala, Stanislav S.; Alger, Matthew J.; Banfield, Julie K.; Ong, Cheng Soon; White, Sarah V.; Garon, Avery F.; Norris, Ray P.; Andernach, Heinz; Tate, Jean; Lukic, Vesna; Tang, Hongming; Schawinski, Kevin; Diakogiannis, Foivos I.

Site: https://github.com/chenwuperth/rgz_rcnn
https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.1211W

Bibcode: 2021ascl.soft05018W

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.1211W

ID: ascl:2105.018