Machine learning for exoplanet detection in high-contrast spectroscopy - Revealing exoplanets by leveraging hidden molecular signatures in cross-correlated spectra with convolutional neural networks

Open Access

Fig. 11

Quantification of scoring performance with ROC curves. The plot shows the improvements in the ROC trade-off between TPR and FPR. The improvement is measured in terms of area under the ROC curves (AUC). The CNNs outperform the baselines in finding true positives while limiting the increase in FPR.

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