ner_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model B-Citation I-Citation I-Citation I-Citation O O O O O B-Dataset I-Dataset I-Dataset B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-Dataset I-Dataset I-Dataset O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O B-Model I-Model O O B-Model I-Model O O O O B-Model I-Model B-Citation I-Citation O O O B-Model I-Model B-Citation I-Citation I-Citation I-Citation O O O B-Model O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O B-Wavelength B-Wavelength I-Wavelength O B-Wavelength B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O B-Software B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O B-Model O O O O O O O B-Model O O O O O O B-Survey O O O O O O O O O O O B-Survey B-Observatory O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset O O O B-Dataset O O O O O O O O O O O O O O
tokens: Since there are many possible choices for stellar libraries, IMFs, and isochrones, many SPMs are available. The systematic differences in AGN identification due to the choice of SPM are explored in detail by Chen et al. ( 2018 ). SPMs based on theoretical stellar libraries are subject to model dependence on line profiles and opacity uncertainties. Empirical stellar libraries are not model dependent but need a large number of observed stars that cover the parameter space as completely as possible. The MILES SPM (Vazdekis et al. 2010 ) is constructed from the MILES stellar library (Falcón-Barroso et al. 2011 ), currently the largest and most well-calibrated empirical stellar library. The MILES stellar library consists of 985 stars with a wavelength range of 3525–7500 Å and a spectral resolution of 2.5 Å. In this work, we use the MILES SPM, with a unimodal IMF of slope 1.3, the Salpeter IMF (Salpeter 1955 ), and the Padova isochrone (Girardi et al. 2000 ). It contains SSPs with ages from 63 Myr to 17.78 Gyr and metallicities ranging between [M/H] = −2.32 and +0.22. For each spectrum in our galaxy sample, wavelength ranges with emission lines and telluric contamination bands are masked. Since each galaxy from 6dF was observed in two bands, V (4000–5500 Å) and R (5500–7500 Å), we combine them into a single spectrum by matching in the overlap region and smooth the combined spectrum to the lower ( R -band) resolution, before fitting. The SSPs are broadened to match the spectral resolution of the observed spectra. Each data spectrum is fit as a linear combination of the MILES SSPs using the full-spectrum-fitting program pPXF (Cappellari Emsellem 2004 ). A large fraction of the spectra are fit by a linear combination of one or two SSPs, and nearly all have fewer than five SSPs in the linear combination. For the SDSS spectra only an overall normalization is added to the templates. For 6dF, CTIO, and FAST spectra that do not have absolute flux calibration, the fitting algorithm adds a polynomial, of up to eighth order, to account for the instrumental effects on the spectral shape. The fitting algorithm further broadens the templates with a Gaussian kernel to mimic the stellar velocity dispersion in the integrated spectrum of the host galaxy. The fits are required to yield a physical stellar velocity dispersion, σ fit 1000 km s −1 , in order to be considered acceptable. The fitting routine uses the error spectrum, provided with the data, to calculate a reduced χ 2 value for each acceptable fit. The acceptable fit with the lowest reduced χ 2 is taken to be the best-fit spectrum for the galaxy. Figure 4 shows a typical spectrum from the SDSS sample (black), the error (blue), the best fit (red), and the residual spectra after subtraction in the regions around the emission lines used for AGN identification. Figure 4. Full-spectrum fitting of an illustrative SDSS DR8 spectrum. (a) The DR8 data spectrum is shown as a black line, the error spectrum is shown as