摘要：We provide a fast approach incorporating the usage of deep learning for studying the effects of the number of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is a first attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a case study and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) at Daya Bay have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multiexponential 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.
