deletions | additions       diff --git a/everyday_seds.tex b/everyday_seds.tex  index 12dd3b5..fdec8b0 100644  --- a/everyday_seds.tex  +++ b/everyday_seds.tex 
...
\section{SED Analysis: an overview}  \label{sec:overview}  Spectral energy distributions (SEDs) provide a wealth of knowledge for astronomical sources. With datasets increasing in number and size over the years, astronomers have been able to utilize broadband SEDs more frequently for their research [citation?]. As such, many robust SED analysis codes have been created to help astronomers model, fit, and derive physical quantities from their SED data \cite{2011Ap&SS.331....1W}. \cite{http://adsabs.harvard.edu/abs/2011Ap&SS.331....1W}.  These codes implement a diverse set of methods, for instance: inversion (examples), template-based (examples), Bayesian statistics (examples); also common are home-grown fitting routines (Bongiorno's, others) [Omar: Why Bongiorno and others are home-grown and the others are not? Let's remember to reference the sedfitting.org review here, and probably Tramacere et al. 2009 and SVO's VOSA]. Most distributed fitting packages are tailored for specific data sets or spectral ranges (PAHFIT, STARLIGHT, ), providing robust fitting methods and results. They require the data to be in a specific format with specific units in order for the tool to work properly. When fitting a broadband SED that spans over decades in the spectrum, the astronomer will gather data from different public archives and team members to add to their own dataset. More often than not, the datasets are presented in different file formats and units. The user must provide their own methods to extract the necessary data from each file, convert the units, and output a file in the single format supported by the tool.