University of Hertfordshire

From the same journal

From the same journal

By the same authors

Transfer learning for galaxy morphology from one survey to another

Research output: Contribution to journalArticlepeer-review


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    Accepted author manuscript, 1.06 MB, PDF document

  • H. Domínguez Sánchez
  • M. Huertas-Company
  • M. Bernardi
  • J. L. Fischer
  • T. M. C. Abbott
  • F. B. Abdalla
  • J. Annis
  • S. Avila
  • E. Buckley-Geer
  • A. Carnero Rosell
  • M. Carrasco Kind
  • J. Carretero
  • C. E. Cunha
  • C. B. D'Andrea
  • L. N. da Costa
  • C. Davis
  • J. De Vicente
  • P. Doel
  • A. E. Evrard
  • P. Fosalba
  • J. Frieman
  • J. García-Bellido
  • E. Gaztanaga
  • D. W. Gerdes
  • D. Gruen
  • R. A. Gruendl
  • J. Gschwend
  • G. Gutierrez
  • W. G. Hartley
  • D. L. Hollowood
  • K. Honscheid
  • B. Hoyle
  • K. Kuehn
  • N. Kuropatkin
  • O. Lahav
  • M. A. G. Maia
  • M. March
  • P. Melchior
  • F. Menanteau
  • R. Miquel
  • B. Nord
  • A. A. Plazas
  • E. Sanchez
  • V. Scarpine
  • R. Schindler
  • M. Schubnell
  • M. Soares-Santos
  • F. Sobreira
  • E. Suchyta
  • M. E. C. Swanson
  • G. Tarle
  • A. R. Walker
  • J. Zuntz
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Original languageEnglish
Pages (from-to)93-100
Number of pages8
JournalMonthly Notices of the Royal Astronomical Society
Early online date28 Dec 2018
Publication statusE-pub ahead of print - 28 Dec 2018


Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.


© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.

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