{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/approximate-inference-for-constructing","title":"Approximate Inference for Constructing Astronomical Catalogs from Images","arxiv_id":"1803.00113","date":"2018-02-28","proceeding":null,"authors":["Jeffrey Regier","Andrew C. Miller","David Schlegel","Ryan P. Adams","Jon D. McAuliffe","Prabhat"],"abstract":"We present a new, fully generative model for constructing astronomical\ncatalogs from optical telescope image sets. Each pixel intensity is treated as\na random variable with parameters that depend on the latent properties of stars\nand galaxies. These latent properties are themselves modeled as random. We\ncompare two procedures for posterior inference. One procedure is based on\nMarkov chain Monte Carlo (MCMC) while the other is based on variational\ninference (VI). The MCMC procedure excels at quantifying uncertainty, while the\nVI procedure is 1000 times faster. On a supercomputer, the VI procedure\nefficiently uses 665,000 CPU cores to construct an astronomical catalog from 50\nterabytes of images in 14.6 minutes, demonstrating the scaling characteristics\nnecessary to construct catalogs for upcoming astronomical surveys.","url_abs":"http://arxiv.org/abs/1803.00113v3","url_pdf":"http://arxiv.org/pdf/1803.00113v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"approximate-inference-for-constructing","repo_url":"https://github.com/jeff-regier/Celeste.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.00113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.00113"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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