  Generation of simulated data is essential for data analysis in particle
physics, but current Monte Carlo methods are very computationally expensive.
Deep-learning-based generative models have successfully generated simulated
data at lower cost, but struggle when the data are very sparse. We introduce a
novel deep sparse autoregressive model (SARM) that explicitly learns the
sparseness of the data with a tractable likelihood, making it more stable and
interpretable when compared to Generative Adversarial Networks (GANs) and other
methods. In two case studies, we compare SARM to a GAN model and a non-sparse
autoregressive model. As a quantitative measure of performance, we compute the
Wasserstein distance ($W_p$) between the distributions of physical quantities
calculated on the generated images and on the training images. In the first
study, featuring images of jets in which 90% of the pixels are zero-valued,
SARM produces images with $W_p$ scores that are 24-52% better than the scores
obtained with other state-of-the-art generative models. In the second study, on
calorimeter images in the vicinity of muons where 98% of the pixels are
zero-valued, SARM produces images with $W_p$ scores that are 66-68% better.
Similar observations made with other metrics confirm the usefulness of SARM for
sparse data in particle physics. Original data and software will be made
available upon acceptance of the manuscript from the UCI Machine Learning in
Physics web portal at: http://mlphysics.ics.uci.edu/.
