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Opt. Soc. Am. A"],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:p>Wavefront sensing in conventional Shack\u2013Hartmann wavefront sensors (SHWSs) relies on sequential centroiding and wavefront slope fitting algorithms, which are computationally demanding and whose accuracy is limited by wavefront sampling. In this work, we propose hybrid machine learning (ML) strategies combining feature selection and extraction with artificial neural networks (ANNs) that enhance both the wavefront sensing accuracy and computational efficiency. The ML models proposed for use in a SHWS (8\u00d78 lenslets) are compared against a robust traditional wavefront sensing algorithm. Using a numerically simulated dataset of 10000 SHWS images, we evaluated three approaches: (1) manual feature selection (MFS) with ANNs, (2) random forest (RF)\u2013based feature selection with ANNs, and (3) principal component analysis (PCA)\u2013based feature extraction with ANNs. These methods achieved an average wavefront root-mean-square (RMS) error lower than the traditional algorithm, while maintaining sub-millisecond wavefront sensing time. To evaluate the models\u2019 performance under realistic conditions, we introduced a simulated Gaussian readout noise with a standard deviation of 15 ADU for a 12-bit camera. Evaluations using both noise-free models and those explicitly trained on noisy data confirmed that the proposed ML frameworks remain robust to readout noise. All the simulations and neural network training were performed on a personal laptop without a dedicated GPU or high-performance computing resources. These results demonstrate that integrating feature engineering with ANNs offers significant potential for accurate and computationally efficient SH wavefront sensing.<\/jats:p>","DOI":"10.1364\/josaa.590445","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T15:00:08Z","timestamp":1777302008000},"page":"D64","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Feature engineering with neural networks in\u00a0Shack\u2013Hartmann wavefront sensing"],"prefix":"10.1364","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5244-678X","authenticated-orcid":true,"given":"Akash Kumar","family":"Tarai","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/023vrr657","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Nanyang Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3954-7686","authenticated-orcid":true,"given":"Suman","family":"Sangiri","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/023vrr657","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Likhith Kumar","family":"Pampana","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/023vrr657","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sambit Kumar","family":"Shukla","sequence":"additional","affiliation":[{"name":"Department of Computer Science"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3987-2776","authenticated-orcid":true,"given":"Vyas","family":"Akondi","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/023vrr657","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"josaa-43-8-D64-R1","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195090192.001.0001","volume-title":"Adaptive Optics for Astronomical Telescopes","author":"Hardy","year":"1998"},{"key":"josaa-43-8-D64-R2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511525179","volume-title":"Adaptive Optics in Astronomy","author":"Roddier","year":"1999"},{"key":"josaa-43-8-D64-R3","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1038\/s41377-019-0215-1","type":"journal-article","volume":"8","author":"Salter","year":"2019","journal-title":"Light Sci. 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