In the multistream CNN, the input to each branch consists of the same DP information; however, the vector map containing the point cloud data is sorted in three different ways to achieve three different kernels (SI Fig. ). We tested this multistream CNN with the combination of the three kernel types and compared its accuracy to the accuracy of single-stream CNNs with only one of three kernel types. The squares in the first three DP images illustrate a kernel with size 2. The results show that the accuracy is the highest when we combine the three kernel types in the multistream CNN (SI Fig. ), and thus we subsequently adopt this architecture. By utilizing multiple kernel types, the CNN is able to more effectively home in on various physical features of the different adjacent Bragg disks and exploit the actual physical information encoded in the DPs to learn and make accurate predictions.