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Table 2

Summary of the previous classifiers included in this work.

Name Algorithm used Type Input Output Authors
CNN1 Convolutional Neural Network Classification only Array of fluxes and errors conf_*_CNN1 Rodrigues et al. (2023)
CNN1NE Convolutional Neural Network Classification only Array of fluxes conf_*_CNN1NE Rodrigues et al. (2023)
CNN2 Convolutional Neural Network Classification only 2D image including fluxes and errors conf_*_CNN2 Rodrigues et al. (2023)
RF Random Forest Classification only Array of fluxes conf_*_RF Rodrigues et al. (2023)
LGBM LightGBM Classification only Array of fluxes conf_*_LGBM Rodrigues et al. (2023)
ANN1 Artificial Neural Network Classification only Array of magnitudes + hybridisation conf_*_ANN1 Martínez-Solaeche et al. (2023)
ANN2 Artificial Neural Network Classification only Array of fluxes + hybridisation conf_*_ANN2 Martínez-Solaeche et al. (2023)
SQUEzE Random Forest Classification and redshift Specific line metrics conf_SQUEZE_**, z_SQUEZE_**, class_SQUEZE Pérez-Ràfols et al. (2023a)
QPz Template-based fitting Redshift only Array of fluxes zphot_QPz Queiroz, private communication
LePhare Template-based fitting Redshift only Array of fluxes zphot_LePhare Queiroz, private communication

Notes. A single * in the output columns represents the different classes and should be replaced by star, gal, and lqso (for low-z quasars) and hqso (for high-z quasars). In the same column, the double ** represents alternative classifications and should be replaced with numbers 0 to 4 (0 being the most confident classification and 4 being the least confident). We refer the reader to the respective papers for details about the architectures and hyperparameters of the codes.

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