The number of non-zero column-wise sums is the total number of detected emission scenarios, which is called detection coverage. The objective is to find the optimal selection of k rows from this matrix to maximize the detection coverage. The next step is to apply Pareto optimization to the best subset selection problem we have formulated. Pareto optimization has been developed to handle situations with conflicting objectives. In our case, we want to find a subset of rows from the detection matrix to maximize the detection coverage, while minimizing the size of the subset, that is, using fewer sensors if possible. The Pareto optimization problem is formulated by following the set up used in Qian et al. .