{"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/variational-inference-for-deblending-crowded","title":"Variational Inference for Deblending Crowded Starfields","arxiv_id":"2102.02409","date":"2021-02-04","proceeding":null,"authors":["Runjing Liu","Jon D. McAuliffe","Jeffrey Regier"],"abstract":"In images collected by astronomical surveys, stars and galaxies often overlap visually. Deblending is the task of distinguishing and characterizing individual light sources in survey images. We propose StarNet, a Bayesian method to deblend sources in astronomical images of crowded star fields. StarNet leverages recent advances in variational inference, including amortized variational distributions and an optimization objective targeting an expectation of the forward KL divergence. In our experiments with SDSS images of the M2 globular cluster, StarNet is substantially more accurate than two competing methods: Probabilistic Cataloging (PCAT), a method that uses MCMC for inference, and DAOPHOT, a software pipeline employed by SDSS for deblending. In addition, the amortized approach to inference gives StarNet the scaling characteristics necessary to perform Bayesian inference on modern astronomical surveys.","url_abs":"https://arxiv.org/abs/2102.02409v3","url_pdf":"https://arxiv.org/pdf/2102.02409v3.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":"variational-inference-for-deblending-crowded","repo_url":"https://github.com/Runjing-Liu120/DeblendingStarfields","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"variational-inference-for-deblending-crowded","repo_url":"https://github.com/prob-ml/bliss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"variational-inference-for-deblending-crowded","repo_url":"https://github.com/amspector100/deblendingstarfields","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.02409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.02409"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/prob-ml/bliss","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Runjing-Liu120/DeblendingStarfields","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/amspector100/deblendingstarfields","reach":null}],"summary":{"ran_violates":1,"ran_fixture":1,"ran_honours":1},"by_repo_kind":{"listed":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"b10f7493c765f2d2","entry":"convert_nmgy_to_mag","repo":"amspector100/deblendingstarfields","repo_kind":"listed","path":"blip_wrapper/performance_eval.py","file_url":"https://github.com/amspector100/deblendingstarfields/blob/HEAD/blip_wrapper/performance_eval.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b10f7493c765f2d2"}},{"code_sha256_prefix":"c48dfaa8abc3932c","entry":"filter_params","repo":"amspector100/deblendingstarfields","repo_kind":"listed","path":"blip_wrapper/performance_eval.py","file_url":"https://github.com/amspector100/deblendingstarfields/blob/HEAD/blip_wrapper/performance_eval.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c48dfaa8abc3932c"}},{"code_sha256_prefix":"e07d2fb8e9c4f706","entry":"get_locs_error","repo":"amspector100/deblendingstarfields","repo_kind":"listed","path":"blip_wrapper/performance_eval.py","file_url":"https://github.com/amspector100/deblendingstarfields/blob/HEAD/blip_wrapper/performance_eval.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e07d2fb8e9c4f706"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}