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Practical guide to THz imaging using thermal atomic vapour [dataset] Open Access
This tutorial aims to provide details on the underlying principles and methodologies of atom-based terahertz imaging techniques. Terahertz imaging is a growing field of research which can provide complementary information to techniques using other regions of the electromagnetic spectrum. Unlike infrared, visible and ultraviolet radiation, terahertz passes through many everyday materials, such as plastics, cloth and card. Compared with images formed using lower frequencies, terahertz images have superior spatial resolution due to the shorter wavelength, while compared to x-rays and gamma rays, terahertz radiation is non-ionising and safe to use. The tutorial begins with the basic principles of terahertz to optical conversion in alkali atoms before discussing how to construct a model to predict the fluorescent spectra of the atoms, on which the imaging method depends. We discuss the practical aspects of constructing an imaging system, including the subsystem specifications. We then review the typical characteristics of the imaging system including spatial resolution, sensitivity andbandwidth. We conclude with a brief discussion of some potential applications.
- Resource type
Contact person: Downes, Lucy 1
Data collector: Downes, Lucy 1
Contact person: Weatherill, Kevin 1
Data curator: Weatherill, Kevin 1
1 Durham University, UK
Engineering and Physical Sciences Research Council
- Research methods
- Other description
Chiefly contains TIFF images and Python code used to produce figures.
- Terahertz imaging
- Cited in
- Creative Commons Attribution 4.0 International (CC BY)
- Date Created
- L.A. Downes
- Date Uploaded
- 27 September 2022, 11:09:08
- Date Modified
- 6 March 2023, 11:03:02
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File format: zip (ZIP Format)
Mime type: application/zip
File size: 779996442
Last modified: 2022:09:27 12:47:26+01:00
Original checksum: 755b76d4c66abd846e6279d85752c4e0
|User L.A. Downes has updated Practical guide to THz imaging using thermal atomic vapour [dataset]||8 months ago|