  We provide a fast approach incorporating the usage of deep learning for
evaluating the effects of photon sensors in an antineutrino detector on the
event reconstruction performance therein. This work is an attempt to harness
the power of deep learning for detector designing and upgrade planning. Using
the Daya Bay detector as a benchmark case and the vertex reconstruction
performance as the objective for the deep neural network, we find that the
photomultiplier tubes (PMTs) have different relative importance to the vertex
reconstruction. More importantly, the vertex position resolutions for the Daya
Bay detector follow approximately a multi-exponential relationship with respect
to the number of PMTs and hence, the coverage. This could also assist in
deciding on the merits of installing additional PMTs for future detector plans.
The approach could easily be used with other objectives in place of vertex
reconstruction.
