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Fig. 2.


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Multi-output DNN graph. The neural architecture consists of three modules, indicated by boxes, each comprising some layers. The photometric input data (above) feeds the Base module, which performs common operations. The Classification module receives its input from the Base module and outputs a binary classification probability p for ET (pET ∼ 0) and LT (pLT ∼ 1) galaxies. The Redshift module receives inputs from the Base and the Classification modules, and outputs an estimate of the redshift. Inside each module, the individual layers are identified along with the number of neurons and the activation function (dense layers) or the dropout fraction (dropout layers).

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