Deep Probabilistic Direction Prediction in 3D with Applications to
Directional Dark Matter Detectors
Deep Probabilistic Direction Prediction in 3D with Applications to
Directional Dark Matter Detectors
We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere $\mathbb{S}^2$, giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special …