Photometric redshift estimation can be approached using two primary methods:
-
Template-Based Methods: These rely on comparing observed photometric data to pre-existing Spectral Energy Distribution (SED) templates. By matching the observed colors to those of templates at various redshifts, one can estimate the redshift of the object. This approach necessitates a comprehensive library of SED templates.
-
Data-Driven Methods: These utilize machine learning algorithms trained on datasets with known redshifts to predict redshifts for new observations. Such methods do not require explicit SED templates but depend on a representative and extensive training set.
In summary, while SEDs are essential for template-based photometric redshift estimation, data-driven approaches can estimate redshifts without them, provided there is sufficient training data.