Dispersion Outperforms Absorption: EIT-Enhanced Atomic Localization and Gradient Sensing with Super-Gaussian Beams

Quantum Physics arXiv:2512.02063 (quant-ph) [Submitted on 29 Nov 2025] Title:Dispersion Outperforms Absorption: EIT-Enhanced Atomic Localization and Gradient Sensing with Super-Gaussian Beams Authors:Mahboob Ul Haq View a PDF of the paper titled Dispersion Outperforms Absorption: EIT-Enhanced Atomic Localization and Gradient Sensing with Super-Gaussian Beams, by Mahboob Ul Haq View PDF HTML (experimental) Abstract:This work presents a comprehensive theoretical comparison between absorption-based and electromagnetically induced transparency (EIT)-based atomic gradient sensing in a four-level tripod system. Both methods were evaluated under identical and optimized physical conditions to ensure a fair and unbiased comparison. The analysis demonstrates that EIT, driven by its steep dispersion response, consistently outperforms conventional absorption detection across a wide range of super-Gaussian beam profiles. Under optimal detuning, EIT achieved up to an order-of-magnitude enhancement in gradient sensitivity and maintained a twofold advantage even under identical detuning. Both approaches reached sub-diffraction spatial resolution in the range of 0.29lambda-0.40lambda, with EIT exhibiting sharper edge contrast and higher localization accuracy. These results confirm EIT as a fundamentally superior approach for precision atomic gradient sensing and sub-wavelength localization, offering clear guidance for the design of next-generation optical and quantum metrology systems. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2512.02063 [quant-ph] (or arXiv:2512.02063v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.02063 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mahboob Ul Haq [view email] [v1] Sat, 29 Nov 2025 11:59:24 UTC (4,605 KB) Full-text links: Access Paper: View a PDF of the paper titled Dispersion Outperforms Absorption: EIT-Enhanced Atomic Localization and Gradient Sensing with Super-Gaussian Beams, by Mahboob Ul HaqView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)