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Generation of simulated data is essential for data analysis in particlephysics, but current Monte Carlo methods are very computationally expensive.Deep-learning-based generative models have successfully generated simulateddata at lower cost, but struggle when the data are very sparse. We introduce anovel deep sparse autoregressive model (SARM) that explicitly learns thesparseness of the data with a tractable likelihood, making it more stable andinterpretable when compared to Generative Adversarial Networks (GANs) and othermethods. In two case studies, we compare SARM to a GAN model and a non-sparseautoregressive model. As a quantitative measure of performance, we compute theWasserstein distance ($W_p$) between the distributions of physical quantitiescalculated on the generated images and on the training images. In the firststudy, 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 scoresobtained with other state-of-the-art generative models. In the second study, oncalorimeter images in the vicinity of muons where 98% of the pixels arezero-valued, SARM produces images with $W_p$ scores that are 66-68% better.Similar observations made with other metrics confirm the usefulness of SARM forsparse data in particle physics. Original data and software will be madeavailable upon acceptance of the manuscript from the UCI Machine Learning inPhysics web portal at: http://mlphysics.ics.uci.edu/.

SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle Physics