Self-supervised masking strategy used for pretraining. For each light curve, 50% of the observation points are selected as the probed subset, which the model must predict. This subset consists of three components: 30% of the points are fully masked (hidden), 10% are replaced with random magnitudes, and 10% remain visible. This strategy forces the model to learn from context rather than simply memorizing positions.