Transfer learning for galaxy feature detection: Finding giant star-forming clumps in low-redshift galaxies using Faster Region-based Convolutional Neural Network

Jürgen J. Popp, Hugh J Dickinson, Stephen Serjeant, Mike Walmsley, Dominic Adams, Lucy Fortson, Kameswara Mantha, Vihang Mehta, James M. Dawson, Sandor Kruk, Brooke Simmons

Research output: Contribution to journalArticlepeer-review

Abstract

Giant star-forming clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z 1) galaxies but their formation and role in galaxy evolution remain unclear. Observations of low-redshift clumpy galaxy analogues are rare but the availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples much more feasible. Deep Learning (DL), and in particular Convolutional Neural Networks (CNNs), have been successfully applied to image classification tasks in astrophysical data analysis. However, one application of DL that remains relatively unexplored is that of automatically identifying and localizing specific objects or features in astrophysical imaging data. In this paper, we demonstrate the use of DL-based object detection models to localize GSFCs in astrophysical imaging data. We apply the Faster Region-based Convolutional Neural Network object detection framework (FRCNN) to identify GSFCs in low-redshift (z 0.3) galaxies. Unlike other studies, we train different FRCNN models on observational data that was collected by the Sloan Digital Sky Survey and labelled by volunteers from the citizen science project 'Galaxy Zoo: Clump Scout'. The FRCNN model relies on a CNN component as a 'backbone' feature extractor. We show that CNNs, that have been pre-trained for image classification using astrophysical images, outperform those that have been pre-trained on terrestrial images. In particular, we compare a domain-specific CNN - 'Zoobot' - with a generic classification backbone and find that Zoobot achieves higher detection performance. Our final model is capable of producing GSFC detections with a completeness and purity of =0.8 while only being trained on ∼5000 galaxy images.

Original languageEnglish (US)
Pages (from-to)174-197
Number of pages24
JournalRAS Techniques and Instruments
Volume3
Issue number1
DOIs
StatePublished - Jan 1 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

Keywords

  • Data Methods
  • Deep Learning
  • Galaxies: Structure
  • Machine Learning
  • Object Detection
  • Transfer Learning

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