Architecture for the CNN model. The input layers first receive the periodogram, which is then processed by an initial convolutional block (ConvBlock) with kernel sizes of 5 and 8. This is followed by three identical convolutional blocks, each with kernel sizes of 11 and 10. Each ConvBlock concludes with a MaxPooling layer to reduce the dimensionality of the data. All convolutional layers utilize the ReLU activation function. Subsequently, the data is flattened and passed through a compact fully connected neural network with dropout layers. Finally, the network predicts the three target parameters, which are normalized within the range of 0 to 1.