[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81607-en":3,"doc-seo-81607-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81607,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","AS Bridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys","Observational cosmology in the next decade will be shaped by large sky surveys such as LSST at the Vera C. Rubin Observatory and the Euclid space mission, whose differing modalities, footprints, PSFs, and scanning cadences complicate joint inference. AS-Bridge introduces a bidirectional generative model translating between ground- and space-based observations via a diffusion model using a stochastic Brownian Bridge process. By conditioning on overlapping sky regions, it enables probabilistic prediction of missing observations and inter-survey detection of rare events, supporting future LSST–Euclid pipelines.","AS-Bridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys  \nDichang Zhang  \nStony Brook University Stony Brook, NY, USA [diczhang@cs.stonybrook.edu](diczhang@cs.stonybrook.edu)  \nYixuan Shao  \nStony Brook University Stony Brook, NY, USA [yixuan.shao@stonybrook.edu](yixuan.shao@stonybrook.edu)  \nSimon Birrer  \nStony Brook University Stony Brook, NY, USA [simon.birrer@stonybrook.edu](simon.birrer@stonybrook.edu)  \nDimitris Samaras  \nStony Brook University Stony Brook, NY, USA [samaras@cs.stonybrook.edu](samaras@cs.stonybrook.edu)  \narXiv :2603 . 11928v2 [ astro-ph .IM] 10 Jul 2026  \nAbstract  \nThe upcoming decade of observational cosmology will be shaped by large sky surveys, such as the ground-based LSST at the Vera C. Rubin Observatory and the space-based Euclid mission. While they promise an unprecedented view of the Universe across depth, resolution, and wavelength, their differences in observational modality, sky coverage, point-spread function, and scanning cadence make joint analysis beneficial, but also challenging. To facilitate joint analysis, we introduce A(stronomical)S(urvey)-Bridge, a bidirectional generative model that translates between ground-and space-based observations. AS-Bridge learns a diffusion model that employs a stochastic Brownian Bridge process between the LSST and Euclid observations. The two surveys have overlapping sky regions, where we can explicitly model the conditional probabilistic distribution between them. We show that this formulation enables new scientific capabilities beyond single-survey analysis, including faithful probabilistic predictions of missing survey observations and inter-survey detection of rare events. These results establish the feasibility of inter-survey generative modeling. AS-Bridge is therefore well-positioned to serve as a complementary component of future LSST–Euclid joint data pipelines, enhancing the scientific return once data from both surveys become available. Data and code are available at [https://github.com/ZHANG7DC/AS-Bridge](https://github.com/ZHANG7DC/AS-Bridge).  \nCCS Concepts  \n• Applied computing → Astronomy; • Computing methodologies → Reconstruction; Anomaly detection.  \nKeywords  \nObservational Cosmology; Astronomical Survey; Generative Model; Multimodal Learning  \nACM Reference Format:  \nDichang Zhang, Yixuan Shao, Simon Birrer, and Dimitris Samaras. 2026. ASBridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys. In Proceedings of the 32nd ACM SIGKDD Conference  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. KDD’26, Jeju Island, Republic of Korea  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2259-2/2026/08  \n[https://doi.org/10.1145/3770855.3818953](https://doi.org/10.1145/3770855.3818953)  \non Knowledge Discovery and Data Mining V.2 (KDD’26), August 09–13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 12 pages. [https:](https:)//[doi.org/10.1145/3770855.3818953](doi.org/10.1145/3770855.3818953)  \nFigure 1: Overview of AS-Bridge. The central panel shows the visible sky, with the LSST and Euclid survey footprints marked in blue and red, respectively. The overlapping regions indicate areas jointly observed by the ground-based LSST (left) and the space-based Euclid mission (right). LSST provides multi-band optical images that are more blended due to atmospheric seeing, while Euclid delivers sharper nearinfrared observations from space. From these overlapping regions, matched image cutouts are extracted and used to train AS-Bridge, which models the probabilistic translation between the two survey domains using a Brownian bridge formulation.  \n1 Introduction  \nIn this decade, observational cosmology will be driven by major flagship surveys, such as the ground-based Legacy Survey of Space and Time (LSST) at the NSF–DOE Vera C. Rubin Observatory [18] and the space-based ESA Euclid mission [10] . 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predictions for missing observations and supports inter-survey detection of rare events using generative modeling across 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