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

被引：18

作者：

Stringer, K. M. ^{[1
]}

Long, J. P. ^{[2
]}

Macri, L. M. ^{[1
]}

Marshall, J. L. ^{[1
]}

Drlica-Wagner, A. ^{[3
,4
]}

Martinez-Vaquez, C. E. ^{[5
]}

Vivas, A. K. ^{[5
]}

Bechtol, K. ^{[6
,7
]}

Morganson, E. ^{[8
,9
]}

Kind, M. Carrasco ^{[8
,9
]}

Pace, A. B. ^{[1
]}

Walker, A. R. ^{[5
]}

Nielsen, C. ^{[1
,10
]}

Li, T. S. ^{[11
]}

Rykoff, E. ^{[12
,13
,14
]}

Burke, D. ^{[12
,13
,14
]}

Carnero Rosell, A. ^{[15
,16
]}

Neilsen, E. ^{[11
]}

Ferguson, P. ^{[1
]}

Cantu, S. A. ^{[1
]}

Myron, J. L. ^{[1
]}

Strigari, L. ^{[1
]}

Farahi, A. ^{[17
]}

Paz-Chinchon, F. ^{[8
,9
]}

Tucker, D. ^{[3
]}

Lin, Z. ^{[2
]}

Hatt, D. ^{[3
]}

Maner, J. F. ^{[1
]}

Plybon, L. ^{[1
]}

Riley, A. H. ^{[1
]}

Nadler, E. O. ^{[12
,13
]}

Abbott, T. M. C. ^{[5
]}

Allam, S. ^{[11
]}

Annis, J. ^{[11
]}

Bertin, E. ^{[18
,19
]}

Brooks, D. ^{[20
]}

Buckley-Geer, E. ^{[11
]}

Carretero, J. ^{[21
]}

Cunha, C. E. ^{[12
,13
]}

D'Andrea, C. B. ^{[22
]}

da Costa, L. N. ^{[15
,16
]}

De Vicente, J. ^{[23
]}

Desai, S. ^{[24
]}

Doel, P. ^{[20
]}

Eifler, T. F. ^{[25
,26
]}

Flaugher, B. ^{[11
]}

Frieman, J. ^{[3
,11
]}

Garcia-Bellido, J. ^{[27
]}

Gaztanaga, E. ^{[28
,29
]}

Gruen, D. ^{[12
,13
,14
]}

机构：

[1] Texas A&M Univ, Dept Phys & Astron, George P & Cynthia W Mitchell Inst Fundamental Ph, College Stn, TX 77843 USA

[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA

[3] Univ Chicago, Kavli Inst Cosmol Phys, Chicago, IL 60637 USA

[4] Univ Chicago, Dept Astron & Astrophys, Chicago, IL 60637 USA

[5] Natl Opt Astron Observ, Cerro Tololo Interamer Observ, Casilla 603, La Serena, Chile

[6] WIPAC, Madison, WI 53703 USA

[7] Univ Wisconsin, Dept Phys, 1150 Univ Ave, Madison, WI 53706 USA

[8] Natl Ctr Supercomp Applicat, 1205 West Clark St, Urbana, IL 61801 USA

[9] Univ Illinois, Dept Astron, 1002 W Green St, Urbana, IL 61801 USA

[10] Purdue Univ, Dept Phys, 1396 Phys Bldg, W Lafayette, IN 47907 USA

[11] Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA

[12] Stanford Univ, Kavli Inst Particle Astrophys & Cosmol, Stanford, CA 94305 USA

[13] Stanford Univ, Dept Phys, 382 Via Pueblo Mall, Stanford, CA 94305 USA

[14] SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 USA

[15] Lab Interinst E Astron LIneA, Rua Gal Jose Cristino 77, BR-20921400 Rio De Janeiro, RJ, Brazil

[16] Observ Nacl, Rua Gal Jose Cristino 77, BR-20921400 Rio De Janeiro, RJ, Brazil

[17] Carnegie Mellon Univ, Dept Phys, Pittsburgh, PA 15312 USA

[18] Inst Astrophys Paris, CNRS, UMR 7095, F-75014 Paris, France

[19] Sorbonne Univ, UPMC Univ Paris 06, UMR 7095, Inst Astrophys Paris, F-75014 Paris, France

[20] UCL, Dept Phys & Astron, Gower St, London WC1E 6BT, England

[21] Barcelona Inst Sci & Technol, IFAE, Campus UAB, E-08193 Bellaterra, Barcelona, Spain

[22] Univ Penn, Dept Phys & Astron, Philadelphia, PA 19104 USA

[23] CIEMAT, Madrid, Spain

[24] IIT Hyderabad, Dept Phys, Kandi 502285, Telangana, India

[25] Steward Observ, Dept Astron, 933 North Cherry Ave, Tucson, AZ 85721 USA

[26] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA

[27] Univ Autonoma Madrid, CSIC, Inst Fis Teor, E-08034 Madrid, Spain

[28] IEEC, E-08034 Barcelona, Spain

[29] CSIC, Inst Space Sci ICE, Campus UAB,Caner Can Magrans S-N, E-08193 Barcelona, Spain

[30] Swiss Fed Inst Technol, Dept Phys, Wolfgang Pauli Str 16, CH-8093 Zurich, Switzerland

[31] Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA

[32] Max Planck Inst Extraterr Phys, Giessenbachstr, D-85748 Garching, Germany

[33] Ludwig Maximilians Univ Munchen, Univ Sternwarte, Fak Phys, Scheinerstr 1, D-81679 Munich, Germany

[34] Harvard Smithsonian Ctr Astrophys, 60 Garden St, Cambridge, MA 02138 USA

[35] Macquarie Univ, Australian Astron Opt, N Ryde, NSW 2113, Australia

[36] Princeton Univ, Dept Astrophys Sci, Peyton Hall, Princeton, NJ 08544 USA

[37] Inst Catalana Recerca & Estudis Avancats, E-08010 Barcelona, Spain

[38] Univ Fed Rio Grande do Sul, Inst Fis, Caixa Postal 15051, BR-91501970 Porto Alegre, RS, Brazil

[39] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA

[40] Univ Southampton, Sch Phys & Astron, Southampton SO17 1BJ, Hants, England

[41] Brandeis Univ, Phys Dept, 415 South St, Waltham, MA 02453 USA

[42] Univ Estadual Campinas, Inst Fis Gleb Wataghin, BR-13083859 Campinas, SP, Brazil

[43] Oak Ridge Natl Lab, Comp Sci & Math Div, POB 2009, Oak Ridge, TN 37831 USA

[44] Univ Portsmouth, Inst Cosmol & Gravitat, Portsmouth PO1 3FX, Hants, England

[45] Argonne Natl Lab, 9700 South Cass Ave, Lemont, IL 60439 USA

来源：

ASTRONOMICAL JOURNAL | 2019年 / 158卷 / 01期

基金：

美国国家科学基金会; 澳大利亚研究理事会; 欧洲研究理事会;

关键词：

catalogs; galaxy: halo; galaxy: structure; methods: statistical; stars: variables: RR Lyrae; DIGITAL SKY SURVEY; MILKY-WAY SATELLITE; STELLAR EVOLUTION DATABASE; PERIOD-LUMINOSITY RELATION; VARIABLE-STARS; DWARF GALAXY; POPULATION SYNTHESIS; GLOBULAR-CLUSTER; HALO STRUCTURE; DISCOVERY;

D O I：

10.3847/1538-3881/ab1f46

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

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 similar to 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 (a median of four 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, similar to 28% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets.

引用

页数：26