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Lett."],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>\n                    Modeling wavelength-dependent aberrations in broadband imaging systems requires high-dimensional parameter spaces that render Monte Carlo (MC) tolerance analysis intractable. We propose a low-dimensional spectral parameterization of Zernike coefficients that compresses broadband aberration modeling from 288 to 36 parameters and enables tractable MC tolerance analysis for manufactured systems. For a dispersion-dominated DD-CASSI system, we further introduce a Cauchy-derived Hybrid basis whose 1\/\n                    <jats:italic>\u03bb<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    dependence captures defocus-driven dispersion. Compared with nominal-PSF training, MC-augmented training with the proposed parameterization improves reconstruction PSNR by 1.06\u2009dB, of which 0.48\u2009dB is attributable to the Hybrid basis over a polynomial baseline.\n                  <\/jats:p>","DOI":"10.1364\/ol.600996","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:00:11Z","timestamp":1781521211000},"page":"3785","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-informed spectral parameterization for tractable tolerance sampling in broadband imaging"],"prefix":"10.1364","volume":"51","author":[{"given":"Xiang","family":"Li","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00a2xv884","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1181-8301","authenticated-orcid":true,"given":"Qiuyu","family":"Yue","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00a2xv884","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00a2xv884","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4586-6268","authenticated-orcid":true,"given":"Zhenrong","family":"Zheng","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00a2xv884","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"ol-51-13-3785-R1","doi-asserted-by":"publisher","first-page":"090901","DOI":"10.1117\/1.OE.52.9.090901","type":"journal-article","volume":"52","author":"Hagen","year":"2013","journal-title":"Opt. 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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"}]}}