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XDQSO, written in IDL, calculates photometric quasar probabilities to mimick SDSS-III’s BOSS quasar target selection or photometric redshifts for quasars, whether in three redshift ranges (z < 2.2; 2.2 leq z leq 3.5; z > 3.5) or arbitrary redshift ranges.
MLC_EPGs classifies intermediate redshift (z = 0.3–0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGN), or low-ionization nuclear emission regions (LINERs). It uses four supervised machine learning classification algorithms: k-nearest neighbors (KNN), support vector classifier (SVC), random forest (RF), and a multi-layer perceptron (MLP) neural network. For input features, it uses properties that can be measured from optical galaxy spectra out to z < 0.8—[O III]/Hβ, [O II]/Hβ, [O III] line width, and stellar velocity dispersion—and four colors (u−g, g−r, r−i, and i−z) corrected to z = 0.1.