Advanced EVS Pixel Modeling to Improve Dim RSO Detection up to 2.9× and Enable Accurate Photometric Reconstruction for SDA

Advanced EVS Pixel Modeling to Improve Dim RSO Detection up to 2.9× and Enable Accurate Photometric Reconstruction for SDA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Advanced EVS Pixel Modeling to Improve Dim RSO Detection up to 2.9× and Enable Accurate Photometric Reconstruction for SDA Brian McReynolds, Rachel Oliver, Zachry Theis, Peter McMahon-Crabtree, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8900804/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract gls{EVS} have been increasingly utilized for \gls{SDA} applications over the past several years. Newer models demonstrate improved performance compared to early generation systems; however, the absolute limits of their sensitivity with respect to fundamental physical parameters (i.e. on-chip photon flux) for detecting dim point source objects has not been fully quantified.Additionally, the complex exercise of optimizing tunable sensor biases for SDA has not been explored for the newest generation sensors. This work shows that with optimized biasing, scan rate, and a novel denoising strategy, the IMX636 sensor is capable of detecting point sources that produce on the order of several hundred photons per second on the focal plane. Our detailed characterization allows users of the technology to predict detection limits when paired with varied optical systems. The result is included in an open-source software tool. Additionally, bias optimization is shown to extend sensitivity by 1.05 visual magnitudes, m v , and when coupled with the our denoising method, enables detection of 2.9× more star streaks compared with default biases. The detailed modeling of pixel level performance further enables a method for reconstructing object brightness from event camera output by calibrating the sensor's dark current and incorporating a model that accounts for the pixel's illumination dependent temporal response and logarithmic compression. Ultimately, the ability to accurately extract photometric information from an event camera can extend to other characterization methods including polarimetry and spectral analysis with proven EVS benefits of excellent temporal resolution, low power, sparse data output. Event-based Vision Space Domain Awareness Sensor Optimization Photometric Reconstruction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8900804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622482211,"identity":"09933963-8c64-4910-9a6a-02d78d42b96c","order_by":0,"name":"Brian 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