  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.
