Tests conducted on planetary signals (H₂O) embedded in idealistic simulated Gaussian noise. Left: ROC curves and AUC showing that neural networks are barely able to perform better than the S/N. Middle-Left and Middle-Right: Frequency distributions of aggregated scores assigned to the negative group (red) against the positive group (blue) for the S/N (left) and the CNN (right) in the presence of Gaussian noise. The CNNs are not able to separate the scores and improve conspicuity as well as in the case of non-Gaussian noise. Right: Classification predictions under a Gaussian noise regime, given a FDR≤ 5%. The use of MLCCS to extract signals becomes ineffective under Gaussian noise, as they are not able to extract more information than the S/N.