Vincenzo Ripepi, Y. Choi, Smitha Subramanian, Valentin D. Ivanov, Steven R. Majewski, Gisella Clementini, Jacco Th. van Loon, Dennis Zaritsky, Angus H. Wright, S. Rubele, Samyaday Choudhury, Ricardo R. Muñoz, Richard de Grijs, Clara M. Pennock, Florian Niederhofer, Noelia E. D. Noël, David Martínez-Delgado, Knut Olsen, Maria-Rosa L. Cioni, Pol Massana, Cameron P. M. Bell, David L. Nidever, A. Katherina Vivas, Joana M. Oliveira, Marcella Marconi, Ben L. Tatton, European Research Council, National Science Foundation (US), Ministerio de Ciencia, Innovación y Universidades (España), European Commission, Comisión Nacional de Investigación Científica y Tecnológica (Chile), and Science and Technology Facilities Council (UK)
We present a map of the total intrinsic reddening across similar or equal to 34 deg(2) of the Small Magellanic Cloud (SMC) derived using optical (ugriz) and near-infrared (IR; YJK(s)) spectral energy distributions (SEDs) of background galaxies. The reddening map is created using a subsample of 29 274 galaxies with low levels of intrinsic reddening based on the LEPHARE chi(2) minimization SED-fitting routine. We find statistically significant enhanced levels of reddening associated with the main body of the SMC compared with regions in the outskirts [Delta E(B - V) similar or equal to 0.3 mag]. A comparison with literature reddening maps of the SMC shows that, after correcting for differences in the volume of the SMC sampled, there is good agreement between our results and maps created using young stars. In contrast, we find significant discrepancies between our results and maps created using old stars or based on longer wavelength far-IR dust emission that could stem from biased samples in the former and uncertainties in the far-IR emissivity and the optical properties of the dust grains in the latter. This study represents one of the first large-scale categorizations of extragalactic sources behind the SMC and as such we provide the LEPHARE outputs for our full sample of similar to 500 000 sources. © 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society, This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 682115). SR acknowledges support from the ERC consolidator grant project STARKEY(grant agreement no. 615604). YC acknowledges support from NSF grant AST 1655677. DMDacknowledges financial support from the Spanish Ministry for Science, Innovation and Universities and FEDER funds through grant AYA2016-81065-C2-2, the State Agency for Research of the Spanish MCIU through the 'Centre of Excellence Severo Ochoa' award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709) and from grant PGC2018-095049B-C21. RRM acknowledges partial support from project BASAL AFB-170002 as well as FONDECYT project no. 1170364. SS acknowledges support from the Science and Engineering Research Board, India through a Ramanujan Fellowship. The authors would like to thank the Cambridge Astronomy Survey Unit (CASU) and theWide Field Astronomy Unit (WFAU) in Edinburgh for providing the necessary data products under the support of the Science and Technology Facilities Council (STFC) in theU.K. The authorswould also like to thank K. Gordon and J. Roman-Duval for discussions related to the use of the HERITAGE dust maps. The authors would like to extend their gratitude to the referee, Geoff Clayton, who provided several comments that improved the manuscript. This study was based on observations made with VISTA at the La Silla Paranal Observatory under programme ID 179.B-2003. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the STFC, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo 'a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the Ministerio da Ciencia, Tecnologia e Inovacao, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Zurich, Fermi NationalAccelerator Laboratory, the University of Illinois atUrbanaChampaign, the Institut de Ciencies de l'Espai (IEEC/CSIC), the Institut de Fisica d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universitat Munchen and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University. Based on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory (NOAO Prop. ID: 2013A-0411 and 2013B-0440; PI: Nidever), which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. Finally, this project has made extensive use of the Tool for OPerations on Catalogues And Tables (TOPCAT) software package (Taylor 2005) as well as the following open-source PYTHON packages: Astropy (The Astropy Collaboration 2018), MATPLOTLIB (Hunter 2007), NUMPY (Oliphant 2015), PANDAS (McKinney 2010), and SCIPY (Virtanen et al. 2020).