The O3N2 and N2 abundance indicators revisited: improved calibrations based on CALIFA and T_e-based literature data^⋆,⋆⋆

¹ CEI Campus Moncloa, UCM-UPM, Departamento de Astrofísica y CC. de la AtmósferaFacultad de CC. Físicas, Universidad Complutense de Madrid, Avda. Complutense s/n, 28040 Madrid, Spain
e-mail: ramarino@ucm.es
² Departamento de Física Teórica, Universidad Autónoma de Madrid, 28049 Madrid, Spain
³ Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis E. Erro 1, 72840 Tonantzintla, Puebla, Mexico
⁴ Instituto de Astrofísica de Andalucía (CSIC), Camino Bajo de Huétor s/n, Aptdo. 3004, 18080 Granada, Spain
⁵ Centro Astronómico Hispano-Alemán, Calar Alto, (CSIC-MPG), C/Jesús Durbán Remón 2-2, 04004 Almeria, Spain
⁶ University of Cambridge, Institute of Astronomy, Madingley Road, Cambridge, CB3 0HA, UK
⁷ Australian Astronomical Observatory, PO Box 915, NSW 1670 North Ryde, Australia
⁸ Department of Physics and Astronomy, Macquarie University, NSW 2109, Australia
⁹ CENTRA – Instituto Superior Tecnico, Av. Rovisco Pais 1, 10 49-001 Lisbon, Portugal
¹⁰ Depto. Astrofísica, Universidad de La Laguna (ULL), 38206, La Laguna, Tenerife, Spain
¹¹ Instituto de Astrofísica de Canarias (IAC), 38205, La Laguna, Tenerife, Spain
¹² Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany
¹³ University of Vienna, Department of Astrophysics, Türkenschanzstrasse 17, 1180 Vienna, Austria
¹⁴ CIEMAT, Departamento de Investigación Básica, Avda. Complutense 40, 28040 Madrid, Spain
¹⁵ Centro de Astrofísica and Faculdade de Ciencias, Universidade do Porto, rua das Estrelas, 4150-762 Porto, Portugal
¹⁶ Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany

Received: 24 May 2013
Accepted: 19 July 2013

Abstract

The use of integral field spectroscopy is since recently allowing to measure the emission line fluxes of an increasingly large number of star-forming galaxies, both locally and at high redshift. Many studies have used these fluxes to derive the gas-phase metallicity of the galaxies by applying the so-called strong-line methods. However, the metallicity indicators that these datasets use were empirically calibrated using few direct abundance data points (T_e-based measurements). Furthermore, a precise determination of the prediction intervals of these indicators is commonly lacking in these calibrations. Such limitations might lead to systematic errors in determining the gas-phase metallicity, especially at high redshift, which might have a strong impact on our understanding of the chemical evolution of the Universe. The main goal of this study is to review the most widely used empirical oxygen calibrations, O3N2 and N2, by using newdirect abundance measurements. We pay special attention to (1) the expected uncertainty of these calibrations as a function of the index value or abundance derived and (2) the presence of possible systematic offsets. This is possible thanks to the analysis of the most ambitious compilation of T_e-based H ii regions to date. This new dataset compiles the T_e-based abundances of 603 H ii regions extracted from the literature but also includes new measurements from the CALIFA survey. Besides providing new and improved empirical calibrations for the gas abundance, we also present a comparison between our revisited calibrations with a total of 3423 additional CALIFA H ii complexes with abundances derived using the ONS calibration from the literature. The combined analysis of T_e-based and ONS abundances allows us to derive their most accurate calibration to date for both the O3N2 and N2 single-ratio indicators, in terms of all statistical significance, quality, and coverage of the parameters space. In particular, we infer that these indicators show shallower abundance dependencies and statistically significant offsets compared to others’. The O3N2 and N2 indicators can be empirically applied to derive oxygen abundances calibrations from either direct abundance determinations with random errors of 0.18 and 0.16, respectively, or from indirect ones (but based on a large amount of data), reaching an average precision of 0.08 and 0.09 dex (random) and 0.02 and 0.08 dex (systematic; compared to the direct estimations), respectively.