is likely to be most noticeable in the bluer, narrow bands. However, the modeling of emission lines requires the inclusion of secondary stellar population with young ages ($<0.01$ Gyr) and/or post-asymptotic giant branch stars [@2017ApJ...840...44B] plus a prescription for nebular emission [e.g. @1999astro.ph.12179F; @1999ApJS..123....3L]. To simplify the modeling, we instead adopt a robust fitting approach that may deal with outliers, including possibly emission lines, adopting a Student’s $t$-distribution log-likelihood.

Similar to the normal distribution, the Student’s $t$-distribution is a symmetric and bell-shaped distribution, but with long tails that allow for a non-negligible probability far from the center of the distribution [see @gelmanbda04]. Assuming that we are modeling $N$ discrete bands in a given SED, the log-likelihood that we map is given by

$$\begin{aligned}
 \ln p(D | \theta ) &=& 
 N\log \left [ \frac{\Gamma\left (\frac{\nu + 1}{2}\right )}{\sqrt{\pi (\nu-2)}\Gamma\left (\frac{\nu}{2} \right )}\right ] \nonumber \\ 
 &-& \frac{1}{2}\sum_{i=1}^{N}\log \sigma_{i}^2 \nonumber \\ 
 &-&\frac{\nu+1}{2}\sum_{i=1}^N \log \left [ 1 + \frac{\mu_{i}^2}{\sigma_{i}^2 (\nu-2)} \right ]\mbox{,}
 \label{eq:llf}\end{aligned}$$

where $\Gamma(x)$ represents the gamma function of variable $x$, $\nu$ represents the degrees of freedom of the Student’s $t$-distribution, $\sigma_{i}$ represents the uncertainties of a given for the $i$-th band, and the mean $\mu_{i}$ represents the difference between the observed 