Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images

• URL: http://arxiv.org/abs/2510.03074v1
• Date: Fri, 03 Oct 2025 15:01:34 GMT
• Title: Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images
• Authors: Jeffrey Regier,
• Abstract summary: Upcoming astronomical surveys will produce petabytes of high-resolution images of the night sky.<n>Most of these objects are faint and many visually overlap with other objects.<n>We propose an amortized variational inference procedure to solve this instance of small-object detection.
• Score: 5.144104651619037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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