Photometric classification of quasars from DES and photo-$z$ estimation with Machine Learning

This paper presents a comprehensive study of quasar photometric classification and redshift estimation using machine learning techniques. We cross-matched photometric data from the Dark Energy Survey Data Release 2 (DES DR2) with spectroscopic classifications from the Sloan Digital Sky Survey Data Release 16 (SDSS DR16), yielding an initial sample of 168,738 point-like objects. Using a K-Nearest Neighbors (KNN) algorithm with PSF magnitudes in the $g$, $r$, $i$, and $z$ bands, we achieved high-precision quasar/galaxy classification against stellar contaminants, reaching a recall of 0.77 at 0.99 precision. Photometric redshifts were subsequently estimated using a hybrid machine learning approach combining a Boosted Decision Tree from ANNz and a Decision Tree Regressor from scikit-learn. The resulting catalog spans redshifts from $z \approx 0.5$ to $z>3$, with a distinct population recovered at $z \approx 4$. A stacked outlier classifier was developed to mitigate catastrophic redshift errors. The full photometric redshift sample contains 872,372 objects and remains reliable for cosmological applications at $z \approx 4$. The cleaned catalog contains 675,683 objects and is suitable for large-scale structure studies in the range $0