{"ID":2860995,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03074","arxiv_id":"2510.03074","title":"Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images","abstract":"Upcoming astronomical surveys will produce petabytes of high-resolution images of the night sky, providing information about billions of stars and galaxies. Detecting and characterizing the astronomical objects in these images is a fundamental task in astronomy -- and a challenging one, as most of these objects are faint and many visually overlap with other objects. We propose an amortized variational inference procedure to solve this instance of small-object detection. Our key innovation is a family of spatially autoregressive variational distributions that partition and order the latent space according to a $K$-color checkerboard pattern. By construction, the conditional independencies of this variational family mirror those of the posterior distribution. We fit the variational distribution, which is parameterized by a convolutional neural network, using neural posterior estimation (NPE) to minimize an expectation of the forward KL divergence. Using images from the Sloan Digital Sky Survey, our method achieves state-of-the-art performance. We further demonstrate that the proposed autoregressive structure greatly improves posterior calibration.","short_abstract":"Upcoming astronomical surveys will produce petabytes of high-resolution images of the night sky, providing information about billions of stars and galaxies. Detecting and characterizing the astronomical objects in these images is a fundamental task in astronomy -- and a challenging one, as most of these objects are fai...","url_abs":"https://arxiv.org/abs/2510.03074","url_pdf":"https://arxiv.org/pdf/2510.03074v1","authors":"[\"Jeffrey Regier\"]","published":"2025-10-03T15:01:34Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"astro-ph.IM\",\"cs.CV\"]","methods":"[]","has_code":false}