We study the optimization landscape of maximum likelihood estimation for a binary latent tree model with hidden variables at internal nodes and observed variables at the leaves. This model, known as the Cavender-Farris-Neyman (CFN) model in statistical phylogenetics, is also a special case of the ferromagnetic Ising model. While the likelihood function is known to be non-concave with multiple critical points in general, gradient descent-type optimization methods have proven surprisingly effective in practice. We provide theoretical insights into this phenomenon by analyzing the population likelihood landscape in a neighborhood of the true parameter vector. Under some conditions on the edge parameters, we show that the expected log-likelihood is strongly concave and smooth in a box around the true parameter whose size is independent of both the tree topology and number of leaves. The key technical contribution is a careful analysis of the expected Hessian, showing that its diagonal entries are large while its off-diagonal entries decay exponentially in the graph distance between the corresponding edges. These results provide the first rigorous theoretical evidence for the effectiveness of optimization methods in this setting and may suggest broader principles for understanding optimization in latent variable models on trees.

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