FortesFit efficiently explores and discriminates between various spectral energy distributions (SED) models of astronomical sources. The Python package adds Bayesian inference to a framework that is designed for the easy incorporation and relative assessment of SED models, various fitting engines, and a powerful treatment of priors, especially those that may arise from non-traditional wave-bands such as the X-ray or radio emission, or from spectroscopic measurements. It has been designed with particular emphasis for its scalability to large datasets and surveys.

iragnsep performs IR SED fits separated into AGN and galaxy contributions, and measures host galaxy properties free of AGN contamination. The advantage of iragnsep is that, in addition to fitting observed broadband photometric fluxes, it also incorporates IR spectra in the fits which, if available, improves the robustness of the galaxy-AGN separation. For the galaxy component, iragnsep uses a library of galaxy templates. In terms of the AGN contribution, if the input dataset is a mixture of spectral and photometric data, iragnsep uses a combination of power-laws for the AGN continuum, and some broad features for the silicate emission. If instead the dataset contains photometric data alone, the AGN contribution is accounted for by using a library of AGN templates. The advanced fitting techniques used by iragnsep combined with the powerful model comparison tests allows iragnsep to provide a statistically robust interpretation of IR SEDs in terms of AGN-galaxy contributions, even when the AGN contribution is highly diluted by the host galaxy emission.