Condiciones generales que deben cumplir todos los aditivos químicos

For a long time the MPA/JHU was the only publicly available catalogue of stellar masses and star formation rates for the SDSS main galaxy sample, and it is certainly the most widely used dataset for those measurements. While the MPA/JHU could be described as an emission line/D4000/SED hybrid method of finding star formation rates, the GALEX-SDSS-WISE Legacy Catalog uses state-of-the-art SED fitting of UV and optical fluxes (Salim et al., 2016). In this section I will give a short description of the methodology used in the GSWLC and then present comparisons of the SFRs with the integrated MaNGA SFRs.

The main stellar masses and star formation rates in the GSWLC are calculated using data from the GALEX (Martin et al., 2005) surveys and the SDSS main galaxy sample (MGS). The catalogue covers 90% of the footprint of the SDSS MGS with shallow GALEX observations (t_{∼ 100s) and 49% of the MGS with medium GALEX observations (t ∼ 1500s).} These samples make up the GSWLC-A and GSWLC-M catalogues, there is also a GSWLC- D catalogue, however this only covers 7% of the MGS with deep GALEX observations (t_{∼ 30,000s) (Salim et al., 2016). The comparisons made in this chapter use the GSWLC-M} catalogue, which covers 70% of the MaNGA MPL-5 sample.

The GSWLC uses SED fitting with the Code Investigating GALaxy Emission (CIGALE) software (Noll et al., 2009). CIGALE is a code that produces grids of model SEDs and performs SED fitting. The model SEDs produced with CIGALE include UV/optical/near-IR stellar emission, and optionally dust and nebula emission. The GSWLC utilises the Bruzual and Charlot (2003) stellar population synthesis models, which allow for the fitting of nebular emission lines.

CIGALE then performs a Bayesian SED fitting regime to the UV photometry from GALEX, and the optical photometry from SDSS ModelMags (Stoughton et al., 2002). This fitting practice generates a probability distribution for the physical parameters by assigning probabilities to each model spectrum at the specified redshift, based on the goodness of fit between the model and the broadband SEDs.

Two star formation histories were tested for the GSWLC, a two-component model that includes two exponentially declining functions with their own starting times and e-folding times, and a delayed start exponential model that starts at zero star formation, peaks at some specified time and then decreases exponentially. The delayed exponential SFH is given by:

−2.5 −1.5 −0.5 0.5 1.5 log 10 (S F RM aN GA ) Slope = 1.14± 0.03, Intercept = 0.02 ± 0.01 Star Forming Slope = 0.87± −0.02, Intercept = 0.04 ± 0.02 Composite −2.5 −1.5 −0.5 0.5 1.5 log10(SF RGSW LC) −2.5 −1.5 −0.5 0.5 1.5 log 10 (S F RM aN GA ) Slope = 0.55± −0.39, Intercept = 0.03 ± 0.04 AGN/LI(N)ER −2.5 −1.5 −0.5 0.5 1.5 log10(SF RGSW LC) Slope = 0.66± −0.27, Intercept = 0.04 ± 0.06 Lineless

Fig. 3.11I compare the total star formation rates from the GSWLC catalog with those calculated with MaNGA. The top left panel shows Star Forming galaxies, the top right composite, the bottom left AGN/LI(N)ER hosts and bottom right Lineless galaxies. I show a linear fit calculated with a orthogonal distance regression in each panel as a solid line, with the parameters of each fit in the top left. The dashed line shows the 1-to-1 relation. The dotted line shows the linear fit to the whole sample.

SFR ∝ t τ2e

−t/τ _(3.7)

It was found that both models find similar stellar masses and SFRs, with no systematic differences between low SFR galaxies and only a 0.1 dex difference between actively star formation galaxies. However, the two-component SFH model provided a better goodness-of- fit, possibly due to the their non-smooth shape replicating the bursty nature of star formation in low mass galaxies (Weisz et al., 2011).

The catalogue uses a dust attenuation model that is a modified Calzetti et al. (2000) curve, which includes an additional bump in the UV range. It was found that this curve achieves a much better quality fit than a standard Calzetti curve.

In Figure 3.11 I compare the star formation rates of galaxies classified between the GSWLC-M catalogue and the integrated MaNGA data, with the galaxies split by the BPT classification in separate panels. I have performed a Orthogonal Distance Regression to fit a linear relationship between the two catalogues and provide the parameters of the fit with their standard errors in the top left corner of each panel. The linear fit is shown with the solid line and the 1-to-1 relation with the dashed line. The integrated MaNGA SFRs agree very well with the GSWLC for star forming and composite galaxies, with a slopes close to 1-to-1 and a scatter of just 0.25 dex and 0.4 dex, respectively. There are no systematic differences in the SFRs present for the star forming galaxies, however composite galaxies with low SFRs do show some bias towards higher star formation rates in MaNGA.

For the AGN/LI(N)ER hosts and Lineless galaxies, I see that there is a systematic differ- ence between the GSWLC SFRs and MaNGA. At the low end of SFR, I find that the MaNGA SFRs are generally much higher than the GSWLC, this is a consequence of the SSFR-Dn4000

limit that prevents us from measuring very low SFRs. Above log₁₀(SFRGSW LC) =−1.5,

however, I find that the MaNGA SFRs are consistently lower for AGN and Lineless galaxies. Similar discrepancies were found between the GSWLC and the MPA/JHU AGN and lineless galaxies. The additional star formation in the AGN galaxies was attributed to the inclusion of the UV bands from GALEX, which when removed reduced the AGN SFRs to be more in line with the MPA values. For the lineless galaxies, however, it was suggested that many of these galaxies with intermediate SFRs may in fact be post-starburst or ’E+A galaxies’ (Dressler and Gunn, 1983; Goto, 2007) and that these galaxies are very sensitive to the assumed dust model. When using a Calzetti dust model instead of the modified dust law, it was found that the SFRs from the SED fits were an order of magnitude lower, however the reduced chi-squared parameter was much higher, implying that the modified dust law better predicted the SFRs of these galaxies.