Flowchart representing the methods, scoring, and classification workflows presented across sections. Each cross-correlated spatial pixel (spaxel) is treated as an independent instance and is passed through a classifier of static (statistic) or a dynamic (learning algorithm) type. The methods will evaluate the RV series and yield scoring metrics (e.g. a statistic or probability score). In order to perform classification, the scores need to first be separated using a meaningful threshold. The current standard classification scheme is yield by the S/N on the cross-correlation peak at the planet’s RV. We propose to analyse the RV series in a holistic approach using ML to detect the planets and their molecules, and use the resulting probability scores.