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Title: Star-galaxy classification in the Dark Energy Survey Y1 dataset

Abstract

Here, we perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. We explore and compare different techniques to address this problem: morphological versus using additional flux information, differences between machine-learning, cut based and template fitting including Hierarchical Bayesian methods and catalogue-based versus image-based analyses. This is done by performing a wide range of tests with and without external ‘truth’ information, which can be ported to other similar datasets. We make a broad evaluation of the performance of the classifiers in two science cases with DES data that are most affected by this systematic effect: large-scale structure and Milky Way studies. In general, even though the default morphological classifiers used for DES Y1 cosmology studies are sufficient to maintain a low level of systematic contamination from stellar mis-classification, contamination can be reduced to the O (1%) level by using multi-epoch and infrared information from external datasets. For Milky Way studies the stellar sample can be augmented by ~20% for a given flux limit. Reference catalogues used in this work are available at http://des.ncsa.illinois.edu/releases/y1a1.

Authors:
 [1]; ORCiD logo [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9]; ORCiD logo [10]; ORCiD logo [11];  [12];  [8];  [13];  [14];  [15];  [16];  [14];  [3];  [3] more »;  [6]; ORCiD logo [15];  [17];  [18];  [19];  [20];  [6];  [6];  [21];  [6];  [3];  [22];  [8];  [23];  [22];  [23];  [1];  [24];  [3];  [8];  [6];  [25];  [26];  [27]; ORCiD logo [17];  [28];  [22];  [6];  [29];  [30];  [31];  [29];  [3];  [32];  [33];  [25];  [34];  [22]; ORCiD logo [35];  [7];  [28];  [36];  [22];  [37];  [1];  [6];  [38];  [39];  [40];  [21];  [41];  [42]; ORCiD logo [43];  [14];  [39]; ORCiD logo [44];  [6];  [21] « less
  1. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
  2. Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, 85748 Garching, Germany; Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, 81679 München, Germany
  3. Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK
  4. Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot 76100, Israel
  5. LSST, 933 North Cherry Avenue, Tucson, AZ 85721, USA
  6. Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA
  7. 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
  8. Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain
  9. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
  10. Department of Physics, University of Surrey, Guildford GU2 7XH, UK
  11. Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK; Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
  12. CNRS, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France
  13. Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, IL 61801, USA
  14. National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA
  15. Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA
  16. Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK; Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK; Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, 85748 Garching, Germany
  17. Kavli Institute for Particle Astrophysics & Cosmology, P. O. Box 2450, Stanford University, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  18. Instituto de Física, UFRGS, Caixa Postal 15051, Porto Alegre, RS - 91501-970, Brazil; Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
  19. Brookhaven National Laboratory, Bldg 510, Upton, NY 11973, USA
  20. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, Illinois 60637, USA
  21. Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena, Chile
  22. 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
  23. Kavli Institute for Particle Astrophysics & Cosmology, P. O. Box 2450, Stanford University, Stanford, CA 94305, USA
  24. Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India
  25. Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA; Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
  26. Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain
  27. Institut d’Estudis Espacials de Catalunya (IEEC), 08193 Barcelona, Spain; Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain
  28. 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
  29. Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA
  30. 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
  31. Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
  32. Department of Astronomy/Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721-0065, USA; Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
  33. Australian Astronomical Observatory, North Ryde, NSW 2113, Australia
  34. Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, São Paulo, SP, 05314-970, Brazil; Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
  35. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
  36. Institució Catalana de Recerca i Estudis Avançats, E-08010 Barcelona, Spain; Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain
  37. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
  38. SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  39. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  40. School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK
  41. Brandeis University, Physics Department, 415 South Street, Waltham MA 02453, USA
  42. 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
  43. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
  44. Institute of Cosmology & Gravitation, University of Portsmouth, Portsmouth, PO1 3FX, UK
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States); 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:
1479690
Alternate Identifier(s):
OSTI ID: 1474431; OSTI ID: 1491677
Report Number(s):
arXiv:1805.02427; FERMILAB-PUB-18-112-AE-PPD; BNL-210901-2019-JAAM
Journal ID: ISSN 0035-8711; 1672083
Grant/Contract Number:  
AC02-07CH11359; AC05-00OR22725; SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 481; Journal Issue: 4; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 97 MATHEMATICS AND COMPUTING; photometric; statistical; data analysis

Citation Formats

Sevilla-Noarbe, I., Hoyle, B., Marchã, M. J., Soumagnac, M. T., Bechtol, K., Drlica-Wagner, A., Abdalla, F., Aleksić, J., Avestruz, C., Balbinot, E., Banerji, M., Bertin, E., Bonnett, C., Brunner, R., Carrasco-Kind, M., Choi, A., Giannantonio, T., Kim, E., Lahav, O., Moraes, B., Nord, B., Ross, A. J., Rykoff, E. S., Santiago, B., Sheldon, E., Wei, K., Wester, W., Yanny, B., Abbott, T., Allam, S., Brooks, D., Carnero-Rosell, A., Carretero, J., Cunha, C., da Costa, L., Davis, C., de Vicente, J., Desai, S., Doel, P., Fernandez, E., Flaugher, B., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gruen, D., Gruendl, R., Gschwend, J., Gutierrez, G., Hollowood, D. L., Honscheid, K., James, D., Jeltema, T., Kirk, D., Krause, E., Kuehn, K., Li, T. S., Lima, M., Maia, M. A. G., March, M., McMahon, R. G., Menanteau, F., Miquel, R., Ogando, R. L. C., 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., Tucker, D. L., and Walker, A. R. Star-galaxy classification in the Dark Energy Survey Y1 dataset. United States: N. p., 2018. Web. doi:10.1093/mnras/sty2579.
Sevilla-Noarbe, I., Hoyle, B., Marchã, M. J., Soumagnac, M. T., Bechtol, K., Drlica-Wagner, A., Abdalla, F., Aleksić, J., Avestruz, C., Balbinot, E., Banerji, M., Bertin, E., Bonnett, C., Brunner, R., Carrasco-Kind, M., Choi, A., Giannantonio, T., Kim, E., Lahav, O., Moraes, B., Nord, B., Ross, A. J., Rykoff, E. S., Santiago, B., Sheldon, E., Wei, K., Wester, W., Yanny, B., Abbott, T., Allam, S., Brooks, D., Carnero-Rosell, A., Carretero, J., Cunha, C., da Costa, L., Davis, C., de Vicente, J., Desai, S., Doel, P., Fernandez, E., Flaugher, B., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gruen, D., Gruendl, R., Gschwend, J., Gutierrez, G., Hollowood, D. L., Honscheid, K., James, D., Jeltema, T., Kirk, D., Krause, E., Kuehn, K., Li, T. S., Lima, M., Maia, M. A. G., March, M., McMahon, R. G., Menanteau, F., Miquel, R., Ogando, R. L. C., 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., Tucker, D. L., & Walker, A. R. Star-galaxy classification in the Dark Energy Survey Y1 dataset. United States. doi:10.1093/mnras/sty2579.
Sevilla-Noarbe, I., Hoyle, B., Marchã, M. J., Soumagnac, M. T., Bechtol, K., Drlica-Wagner, A., Abdalla, F., Aleksić, J., Avestruz, C., Balbinot, E., Banerji, M., Bertin, E., Bonnett, C., Brunner, R., Carrasco-Kind, M., Choi, A., Giannantonio, T., Kim, E., Lahav, O., Moraes, B., Nord, B., Ross, A. J., Rykoff, E. S., Santiago, B., Sheldon, E., Wei, K., Wester, W., Yanny, B., Abbott, T., Allam, S., Brooks, D., Carnero-Rosell, A., Carretero, J., Cunha, C., da Costa, L., Davis, C., de Vicente, J., Desai, S., Doel, P., Fernandez, E., Flaugher, B., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gruen, D., Gruendl, R., Gschwend, J., Gutierrez, G., Hollowood, D. L., Honscheid, K., James, D., Jeltema, T., Kirk, D., Krause, E., Kuehn, K., Li, T. S., Lima, M., Maia, M. A. G., March, M., McMahon, R. G., Menanteau, F., Miquel, R., Ogando, R. L. C., 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., Tucker, D. L., and Walker, A. R. Fri . "Star-galaxy classification in the Dark Energy Survey Y1 dataset". United States. doi:10.1093/mnras/sty2579. https://www.osti.gov/servlets/purl/1479690.
@article{osti_1479690,
title = {Star-galaxy classification in the Dark Energy Survey Y1 dataset},
author = {Sevilla-Noarbe, I. and Hoyle, B. and Marchã, M. J. and Soumagnac, M. T. and Bechtol, K. and Drlica-Wagner, A. and Abdalla, F. and Aleksić, J. and Avestruz, C. and Balbinot, E. and Banerji, M. and Bertin, E. and Bonnett, C. and Brunner, R. and Carrasco-Kind, M. and Choi, A. and Giannantonio, T. and Kim, E. and Lahav, O. and Moraes, B. and Nord, B. and Ross, A. J. and Rykoff, E. S. and Santiago, B. and Sheldon, E. and Wei, K. and Wester, W. and Yanny, B. and Abbott, T. and Allam, S. and Brooks, D. and Carnero-Rosell, A. and Carretero, J. and Cunha, C. and da Costa, L. and Davis, C. and de Vicente, J. and Desai, S. and Doel, P. and Fernandez, E. and Flaugher, B. and Frieman, J. and Garcia-Bellido, J. and Gaztanaga, E. and Gruen, D. and Gruendl, R. and Gschwend, J. and Gutierrez, G. and Hollowood, D. L. and Honscheid, K. and James, D. and Jeltema, T. and Kirk, D. and Krause, E. and Kuehn, K. and Li, T. S. and Lima, M. and Maia, M. A. G. and March, M. and McMahon, R. G. and Menanteau, F. and Miquel, R. and Ogando, R. L. C. 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 Tucker, D. L. and Walker, A. R.},
abstractNote = {Here, we perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. We explore and compare different techniques to address this problem: morphological versus using additional flux information, differences between machine-learning, cut based and template fitting including Hierarchical Bayesian methods and catalogue-based versus image-based analyses. This is done by performing a wide range of tests with and without external ‘truth’ information, which can be ported to other similar datasets. We make a broad evaluation of the performance of the classifiers in two science cases with DES data that are most affected by this systematic effect: large-scale structure and Milky Way studies. In general, even though the default morphological classifiers used for DES Y1 cosmology studies are sufficient to maintain a low level of systematic contamination from stellar mis-classification, contamination can be reduced to the O (1%) level by using multi-epoch and infrared information from external datasets. For Milky Way studies the stellar sample can be augmented by ~20% for a given flux limit. Reference catalogues used in this work are available at http://des.ncsa.illinois.edu/releases/y1a1.},
doi = {10.1093/mnras/sty2579},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 4,
volume = 481,
place = {United States},
year = {2018},
month = {9}
}

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