We use the W_Ha versus [NII]/Ha (WHAN) diagram to provide a comprehensive emission-line classification of SDSS galaxies. This classification is able to cope with the large population of weak line galaxies that do not appear in traditional diagrams du
e to a lack of some of the diagnostic lines. A further advantage of the WHAN diagram is to allow the differentiation between two very distinct classes that overlap in the LINER region of traditional diagnostic diagrams. These are galaxies hosting a weakly active nucleus (wAGN) and retired galaxies (RGs), i.e. galaxies that have stopped forming stars and are ionized by their hot evolved low-mass stars. A useful criterion to distinguish true from fake AGN (i.e. the RGs) is the ratio (xi) of the extinction-corrected L_Ha with respect to the Ha luminosity expected from photoionization by stellar populations older than 100 Myr. This ratio follows a markedly bimodal distribution, with a xi >> 1 population composed by systems undergoing star-formation and/or nuclear activity, and a peak at xi ~ 1 corresponding to the prediction of the RG model. We base our classification scheme on the equivalent width of Ha, an excellent observational proxy for xi. Based on the bimodal distribution of W_Ha, we set the division between wAGN and RGs at W_Ha = 3 A. Five classes of galaxies are identified within the WHAN diagram: (a) Pure star forming galaxies: log [NII]/Ha < -0.4 and W_Ha > 3 A. (b) Strong AGN (i.e., Seyferts): log [NII]/Ha > -0.4 and W_Ha > 6 A. (c) Weak AGN: log [NII]/Ha > -0.4 and W_Ha between 3 and 6 A. (d) RGs: W_Ha < 3 A. (e) Passive galaxies (actually, line-less galaxies): W_Ha and W_[NII] < 0.5 A. A comparative analysis of star formation histories and of other properties in these different classes of galaxies corroborates our proposed differentiation between RGs and weak AGN in the LINER-like family. (Abridged)
We explore the prospects of predicting emission line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 A break, can predict many properties of g
alaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission line objects only. We use two independent methods, Artifical Neural Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify AGN and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming FMOS survey and the planned WFMOS survey.
