The figure illustrates the architecture of our deep learning model. At each observation epoch, the image cutout is processed through a ConvLSTM2D channel, while the corresponding time value is fed into a standard LSTM channel. The outputs from both channels are then concatenated and passed through a dense layer. The final layer, consisting of a single node with sigmoid activation, outputs a value 𝒫 ∈ (0, 1), representing the probability that the sample is lensed at that particular observation epoch. We use the binary-cross-entropy loss function for binary classification. Note the number of bands is N_B = 4 and 1 for the multiband and single-band analyses, respectively.