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Opt. Soc. Am. A"],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>Orbital angular momentum (OAM) mode recognition of vortex beams is essential for free-space optical communication; however, it becomes considerably more challenging under slant-path propagation where atmospheric turbulence and optical aberrations jointly distort the beam structure. Here, we propose an improved multi-task residual network, termed DCA-ResNet18, for the simultaneous recognition of the topological charge, spherical aberration coefficient, and coma aberration coefficient of vortex beams under slant-path atmospheric turbulence. The model incorporates dual 3\u00d73 input convolutions, task-adaptive branches, and a convolutional block attention module (CBAM) to enhance the extraction of ring, dark-core, and asymmetric distortion features. Three datasets corresponding to single-coma-aberration, single-spherical-aberration, and dual-aberration condition are generated using a slant-path multi-phase-screen propagation model. The proposed model achieves near-perfect accuracy under single-aberration conditions over different zenith angles (0\u00b0, 60\u00b0, 80\u00b0) and propagation distances (2000, 3000, and 4000\u00a0m). Under combined-aberration conditions, the recognition accuracy of all tasks remains above 98% even at a zenith angle of 80\u00b0 and a propagation distance of 3000\u00a0m, demonstrating strong robustness under severe distortion conditions. Compared with ResNet18, ResNet34, Xception, and EfficientNet-B0, DCA-ResNet18 consistently achieves superior performance in dual-aberration recognition. These results demonstrate the effectiveness and robustness of the proposed framework for simultaneous multi-parameter recognition of vortex beams under slant-path atmospheric turbulence with optical aberrations.<\/jats:p>","DOI":"10.1364\/josaa.611515","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T16:00:08Z","timestamp":1785859208000},"page":"1528","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-parameter identification of aberrated vortex beams under slant-path atmospheric turbulence using a multi-task convolutional neural network"],"prefix":"10.1364","volume":"43","author":[{"given":"Yumeihui","family":"Jin","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03m20nr07","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0480-3882","authenticated-orcid":true,"given":"Xiaoyun","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03m20nr07","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03m20nr07","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyu","family":"Huang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03m20nr07","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Shi","sequence":"additional","affiliation":[{"name":"Shenyang Ligong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueqiu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shenyang Ligong University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"josaa-43-10-1528-R1","doi-asserted-by":"publisher","first-page":"044001","DOI":"10.1117\/1.AP.7.4.044001","type":"journal-article","volume":"7","author":"Chen","year":"2025","journal-title":"Adv. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}