Diagram of the CNN architecture used in our analysis. The network processes 128 × 128 × 1 input velocity maps through a series of convolution and max-pooling layers to extract hierarchical features. The resulting feature map is flattened and passed through a fully connected dense layer of 512 units, culminating in a 128D embedding vector that provides a compact, discriminative representation of the cluster kinematics.