Signal mixture estimation for degenerate heavy Higgses using a deep neural network - Kvellestad, Anders; Maeland, Steffen; Strümke, Inga (The European physical journal. C, Particles and fields, 2018)
If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a ∼ 20 % improvement in the estimate uncertainty.