{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-detection-and-characterization-of","title":"Automatic detection and characterization of random telegraph noise in sCMOS sensors","arxiv_id":"2505.24540","date":"2025-05-30","proceeding":null,"authors":["Arda Özdoğru","Sergey Karpov","Asen Christov","Stanislav Vítek"],"abstract":"Scientific CMOS (sCMOS) image sensors are a modern alternative to typical CCD detectors and are rapidly gaining popularity in observational astronomy due to their large sizes, low read-out noise, high frame rates, and cheap manufacturing. However, numerous challenges remain in using them due to fundamental differences between CCD and CMOS architectures, especially concerning the pixel-dependent and non-Gaussian nature of their read-out noise. One of the main components of the latter is the random telegraph noise (RTN) caused by the charge traps introduced by the defects close to the oxide-silicon interface in sCMOS image sensors, which manifests itself as discrete jumps in a pixel's output signal, degrading the overall image fidelity. In this work, we present a statistical method to detect and characterize RTN-affected pixels using a series of dark frames. Identifying RTN contaminated pixels enables post-processing strategies that mitigate their impact and the development of manufacturing quality metrics.","url_abs":"https://arxiv.org/abs/2505.24540v1","url_pdf":"https://arxiv.org/pdf/2505.24540v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"automatic-detection-and-characterization-of","repo_url":"https://github.com/ardaozd/rtn_detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}