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Title: Transfer learning for galaxy morphology from one survey to another [Knowledge transfer of Deep Learning for galaxy morphology from one survey to another]

Abstract

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 labeled 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. Here, 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 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. Furthermore redshift evolution effects or significant depth differences are not taken into account in this study.

Authors:
ORCiD logo [1];  [2];  [1];  [3];  [1];  [4];  [5];  [6];  [7];  [8];  [6];  [9];  [10];  [11];  [12];  [1];  [9];  [12];  [13];  [8] more »;  [14];  [15];  [16];  [17];  [15];  [15];  [18];  [10];  [9];  [6];  [19];  [20];  [21];  [22];  [23];  [24];  [6];  [8];  [9];  [1];  [25];  [10];  [26];  [6];  [27];  [13];  [6];  [28];  [29];  [30];  [4];  [31];  [32];  [33];  [34];  [29];  [7];  [4];  [35] « less
  1. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
  2. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA; LERMA, Observatoire de Paris, PSL Research University, CNRS, Sorbonne Universités, UPMC Univ. Paris 06, F-75014 Paris, France; University of Paris Denis Diderot, University of Paris Sorbonne Cité (PSC), F-75205 Paris Cedex 13, France; Instituto de Astrofísica de Canarias, E-38200 La Laguna, Tenerife, Spain; Departamento de Astrofísica, Universidad de La Laguna, E-38206 La Laguna, Tenerife, Spain
  3. Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield, Herts AL10 9AB, UK
  4. Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena, Chile
  5. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK; Department of Physics and Electronics, Rhodes University, PO Box 94, Grahamstown 6140, South Africa
  6. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA
  7. Institute of Cosmology & Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK
  8. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK
  9. Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil; Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil
  10. Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, IL 61801, USA; National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA
  11. Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona) Spain
  12. Kavli Institute for Particle Astrophysics & Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA
  13. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
  14. Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA; Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  15. Institut dÉstudis Espacials de Catalunya (IEEC), E-08193 Barcelona, Spain; Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain
  16. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA; Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
  17. Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, E-28049 Madrid, Spain
  18. Kavli Institute for Particle Astrophysics & Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  19. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK; Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland
  20. Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA
  21. Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA; Department of Physics, The Ohio State University, Columbus, OH 43210, USA
  22. Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, D-85748 Garching, Germany; Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr 1, D-81679 München, Germany
  23. Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
  24. Australian Astronomical Observatory, North Ryde, NSW 2113, Australia
  25. Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
  26. Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona) Spain; Institució Catalana de Recerca i Estudis Avanccats, E-08010 Barcelona, Spain
  27. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
  28. SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  29. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  30. School of Physics and Astronomy, University of Southampton, Southampton SO17 1BJ, UK
  31. Physics Department, Brandeis University, 415 South Street, Waltham MA 02453, USA
  32. Instituto de Física Gleb Wataghin, Universidade Estadual de Campinas, 13083-859, Campinas, SP, Brazil; Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil
  33. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
  34. National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA
  35. Institute for Astronomy, University of Edinburgh, Edinburgh EH9 3HJ, UK
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Contributing Org.:
DES Collaboration
OSTI Identifier:
1491209
Alternate Identifier(s):
OSTI ID: 1474426; OSTI ID: 1503971
Report Number(s):
arXiv:1807.00807; FERMILAB-PUB-18-106
Journal ID: ISSN 0035-8711; 1695805
Grant/Contract Number:  
AC02-07CH11359; AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 484; Journal Issue: 1; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION; galaxies: structure; methods: observational-surveys

Citation Formats

Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., Buckley-Geer, E., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Cunha, C. E., D’Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Doel, P., Evrard, A. E., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., March, M., Melchior, P., Menanteau, F., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Walker, A. R., and Zuntz, J. Transfer learning for galaxy morphology from one survey to another [Knowledge transfer of Deep Learning for galaxy morphology from one survey to another]. United States: N. p., 2018. Web. doi:10.1093/mnras/sty3497.
Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., Buckley-Geer, E., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Cunha, C. E., D’Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Doel, P., Evrard, A. E., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., March, M., Melchior, P., Menanteau, F., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Walker, A. R., & Zuntz, J. Transfer learning for galaxy morphology from one survey to another [Knowledge transfer of Deep Learning for galaxy morphology from one survey to another]. United States. doi:10.1093/mnras/sty3497.
Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., Buckley-Geer, E., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Cunha, C. E., D’Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Doel, P., Evrard, A. E., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., March, M., Melchior, P., Menanteau, F., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Walker, A. R., and Zuntz, J. Fri . "Transfer learning for galaxy morphology from one survey to another [Knowledge transfer of Deep Learning for galaxy morphology from one survey to another]". United States. doi:10.1093/mnras/sty3497.
@article{osti_1491209,
title = {Transfer learning for galaxy morphology from one survey to another [Knowledge transfer of Deep Learning for galaxy morphology from one survey to another]},
author = {Domínguez Sánchez, H. and Huertas-Company, M. and Bernardi, M. and Kaviraj, S. and Fischer, J. L. and Abbott, T. M. C. and Abdalla, F. B. and Annis, J. and Avila, S. and Brooks, D. and Buckley-Geer, E. and Carnero Rosell, A. and Carrasco Kind, M. and Carretero, J. and Cunha, C. E. and D’Andrea, C. B. and da Costa, L. N. and Davis, C. and De Vicente, J. and Doel, P. and Evrard, A. E. and Fosalba, P. and Frieman, J. and García-Bellido, J. and Gaztanaga, E. and Gerdes, D. W. and Gruen, D. and Gruendl, R. A. and Gschwend, J. and Gutierrez, G. and Hartley, W. G. and Hollowood, D. L. and Honscheid, K. and Hoyle, B. and James, D. J. and Kuehn, K. and Kuropatkin, N. and Lahav, O. and Maia, M. A. G. and March, M. and Melchior, P. and Menanteau, F. and Miquel, R. and Nord, B. and Plazas, A. A. and Sanchez, E. and Scarpine, V. and Schindler, R. and Schubnell, M. and Smith, M. and Smith, R. C. and Soares-Santos, M. and Sobreira, F. and Suchyta, E. and Swanson, M. E. C. and Tarle, G. and Thomas, D. and Walker, A. R. and Zuntz, J.},
abstractNote = {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 labeled 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. Here, 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 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. Furthermore redshift evolution effects or significant depth differences are not taken into account in this study.},
doi = {10.1093/mnras/sty3497},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 1,
volume = 484,
place = {United States},
year = {2018},
month = {12}
}

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DOI: 10.1093/mnras/sty3497

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