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Active Galaxies with a jet pointing towards us, so-called blazars, play animportant role in the field of high-energy astrophysics. One of the mostimportant features in the classification scheme of blazars is the peakfrequency of the synchrotron emission ($\nu_{\rm peak}^S$) in the spectralenergy distribution (SED). In contrast to standard blazar catalogs that usuallycalculate the $\nu_{\rm peak}^S$ manually, we have developed a machine-learningalgorithm - BlaST - that not only simplifies the estimation, but also providesa reliable uncertainty evaluation. Furthermore, it naturally accounts foradditional SED components from the host galaxy and the disk emission, which maybe a major source of confusion. Using our tool, we re-estimate the synchrotronpeaks in the Fermi 4LAC-DR2 catalog. We find that BlaST, improves the $\nu_{\rmpeak}^S$ estimation especially in those cases where the contribution ofcomponents not related to the jet is important.

BlaST -- A Machine-Learning Estimator for the Synchrotron Peak of Blazars