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Title: Identification of RR Lyrae Stars in Multiband, Sparsely Sampled Data from the Dark Energy Survey Using Template Fitting and Random Forest Classification

Abstract

Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag) in a single exposure; over 5000 deg$$^{2}$$) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (median 4 observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~31% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets.

Authors:
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Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (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:
1531217
Alternate Identifier(s):
OSTI ID: 1523419
Report Number(s):
arXiv:1905.00428; FERMILAB-PUB-18-680-AE; DES-2018-0375
Journal ID: ISSN 1538-3881; 1732992
Grant/Contract Number:  
AC02-07CH11359; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Astronomical Journal (Online)
Additional Journal Information:
Journal Name: Astronomical Journal (Online); Journal Volume: 158; Journal Issue: 1; Journal ID: ISSN 1538-3881
Publisher:
IOP Publishing - AAAS
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Stringer, K. M., Long, J. P., Macri, L. M., Marshall, J. L., Drlica-Wagner, A., Martínez-Vázquez, C. E., Vivas, A. K., Bechtol, K., Morganson, E., Kind, M. Carrasco, Pace, A. B., Walker, A. R., Nielsen, C., Li, T. S., Rykoff, E., Burke, D., Rosell, A. Carnero, Neilsen, E., Ferguson, P., Cantu, S. A., Myron, J. L., Strigari, L., Farahi, A., Paz-Chinchón, F., Tucker, D., Lin, Z., Hatt, D., Maner, J. F., Plybon, L., Riley, A. H., Nadler, E. O., Abbott, T. M. C., Allam, S., Annis, J., Bertin, E., Brooks, D., Buckley-Geer, E., Carretero, J., Cunha, C. E., D’Andrea, C. B., Costa, L. N. da, Vicente, J. De, Desai, S., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gruen, D., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Melchior, P., Miquel, R., Ogando, R. L. C., Plazas, A. A., Sanchez, E., Santiago, B., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Vikram, V., and Yanny, B. Identification of RR Lyrae Stars in Multiband, Sparsely Sampled Data from the Dark Energy Survey Using Template Fitting and Random Forest Classification. United States: N. p., 2019. Web. doi:10.3847/1538-3881/ab1f46.
Stringer, K. M., Long, J. P., Macri, L. M., Marshall, J. L., Drlica-Wagner, A., Martínez-Vázquez, C. E., Vivas, A. K., Bechtol, K., Morganson, E., Kind, M. Carrasco, Pace, A. B., Walker, A. R., Nielsen, C., Li, T. S., Rykoff, E., Burke, D., Rosell, A. Carnero, Neilsen, E., Ferguson, P., Cantu, S. A., Myron, J. L., Strigari, L., Farahi, A., Paz-Chinchón, F., Tucker, D., Lin, Z., Hatt, D., Maner, J. F., Plybon, L., Riley, A. H., Nadler, E. O., Abbott, T. M. C., Allam, S., Annis, J., Bertin, E., Brooks, D., Buckley-Geer, E., Carretero, J., Cunha, C. E., D’Andrea, C. B., Costa, L. N. da, Vicente, J. De, Desai, S., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gruen, D., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Melchior, P., Miquel, R., Ogando, R. L. C., Plazas, A. A., Sanchez, E., Santiago, B., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Vikram, V., & Yanny, B. Identification of RR Lyrae Stars in Multiband, Sparsely Sampled Data from the Dark Energy Survey Using Template Fitting and Random Forest Classification. United States. doi:10.3847/1538-3881/ab1f46.
Stringer, K. M., Long, J. P., Macri, L. M., Marshall, J. L., Drlica-Wagner, A., Martínez-Vázquez, C. E., Vivas, A. K., Bechtol, K., Morganson, E., Kind, M. Carrasco, Pace, A. B., Walker, A. R., Nielsen, C., Li, T. S., Rykoff, E., Burke, D., Rosell, A. Carnero, Neilsen, E., Ferguson, P., Cantu, S. A., Myron, J. L., Strigari, L., Farahi, A., Paz-Chinchón, F., Tucker, D., Lin, Z., Hatt, D., Maner, J. F., Plybon, L., Riley, A. H., Nadler, E. O., Abbott, T. M. C., Allam, S., Annis, J., Bertin, E., Brooks, D., Buckley-Geer, E., Carretero, J., Cunha, C. E., D’Andrea, C. B., Costa, L. N. da, Vicente, J. De, Desai, S., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gruen, D., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Melchior, P., Miquel, R., Ogando, R. L. C., Plazas, A. A., Sanchez, E., Santiago, B., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Vikram, V., and Yanny, B. Fri . "Identification of RR Lyrae Stars in Multiband, Sparsely Sampled Data from the Dark Energy Survey Using Template Fitting and Random Forest Classification". United States. doi:10.3847/1538-3881/ab1f46.
@article{osti_1531217,
title = {Identification of RR Lyrae Stars in Multiband, Sparsely Sampled Data from the Dark Energy Survey Using Template Fitting and Random Forest Classification},
author = {Stringer, K. M. and Long, J. P. and Macri, L. M. and Marshall, J. L. and Drlica-Wagner, A. and Martínez-Vázquez, C. E. and Vivas, A. K. and Bechtol, K. and Morganson, E. and Kind, M. Carrasco and Pace, A. B. and Walker, A. R. and Nielsen, C. and Li, T. S. and Rykoff, E. and Burke, D. and Rosell, A. Carnero and Neilsen, E. and Ferguson, P. and Cantu, S. A. and Myron, J. L. and Strigari, L. and Farahi, A. and Paz-Chinchón, F. and Tucker, D. and Lin, Z. and Hatt, D. and Maner, J. F. and Plybon, L. and Riley, A. H. and Nadler, E. O. and Abbott, T. M. C. and Allam, S. and Annis, J. and Bertin, E. and Brooks, D. and Buckley-Geer, E. and Carretero, J. and Cunha, C. E. and D’Andrea, C. B. and Costa, L. N. da and Vicente, J. De and Desai, S. and Doel, P. and Eifler, T. F. and Flaugher, B. and Frieman, J. and García-Bellido, J. and Gaztanaga, E. and Gruen, D. and Gschwend, J. and Gutierrez, G. and Hartley, W. G. and Hollowood, D. L. and Hoyle, B. and James, D. J. and Kuehn, K. and Kuropatkin, N. and Melchior, P. and Miquel, R. and Ogando, R. L. C. and Plazas, A. A. and Sanchez, E. and Santiago, B. and Scarpine, V. and Schubnell, M. and Serrano, S. and Sevilla-Noarbe, I. 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 Vikram, V. and Yanny, B.},
abstractNote = {Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag) in a single exposure; over 5000 deg$^{2}$) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (median 4 observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~31% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets.},
doi = {10.3847/1538-3881/ab1f46},
journal = {Astronomical Journal (Online)},
number = 1,
volume = 158,
place = {United States},
year = {2019},
month = {6}
}

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