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HySCAI: A Novel Tool for 2D Auroral Imaging and Spectroscopy

In a recent article published in Earth, Planets and Space, researchers developed the "hyperspectral camera for auroral imaging (HySCAI)" to study auroral phenomena. This instrument aims to capture two-dimensional (2D) images of auroras with full spectral information.

HySCAI: A Novel Tool for 2D Auroral Imaging and Spectroscopy

Image Credit: Denis Belitsky/Shutterstock.com

Background

Auroras are natural light displays in the upper atmosphere caused by interactions between precipitating particles, mainly electrons and protons, and air molecules. The spectrum of an aurora provides critical information about these particles and the atmosphere, as atomic and molecular energy levels determine emission wavelengths. Therefore, comprehensive spectroscopic observations are essential for understanding auroras.

Traditional studies used either bandpass filters to isolate specific wavelengths or diffraction grating spectrometers to capture the full spectrum. However, these methods have limitations: filters provide data for only a few wavelengths, and grating spectrometers lack two-dimensional spatial resolution.

About the Research

In this study, the authors introduced HySCAI to overcome the limitations of traditional auroral imaging by simultaneously capturing both spectral and spatial information. The system includes several advanced components, such as an all-sky lens, a galvanometer scanner, a grating spectrograph, and an electron-multiplying charge-coupled device (EM-CCD) detector, to enhance the accuracy and detail of auroral observations.

The all-sky lens offers a wide view of the sky, while the galvanometer scanner enables two-dimensional spatial capture. The grating spectrograph, with switchable diffraction gratings, provides detailed spectral analysis, and the EM-CCD detector captures data with high sensitivity.

HySCAI's control system synchronizes the EM-CCD with the galvanometer, reconstructing 2D monochromatic images from spectral data. The system covers a spectral range of 400 to 800 nm, with resolutions of 2.1 nm or 0.73 nm, depending on the grating used. Its sensitivity is 2.1 counts/s/Rayleigh at 557.7 nm, with an exposure time of 64 seconds for a 2D image.

To reduce atmospheric attenuation, the field of view is limited to elevations above 45 degrees. Additionally, HySCAI is supported by all-sky imagers, including a liquid crystal filter and a color camera, which help compensate for longer exposure times. This combination of technologies makes the system a powerful tool for capturing detailed auroral images.

Research Findings

The HySCAI system was deployed at the Kiruna Esrange Optical Platform Site (KEOPS) in Kiruna, Sweden, to observe an aurora substorm on October 20-21, 2023. The initial observations provided valuable data, revealing the spatial distribution of various auroral emissions.

The system identified several key emission lines and bands in the aurora spectrum, including oxygen emissions at O I (1S) 557.7 nm, O I (1D) 630.0 nm, O2+ 1NG (1,1) 597.3 nm, and O2+ 1NG (0,0) 602.6 nm; nitrogen emissions at N2+ 1NG (0,1) 427.8 nm, N2+ 1NG (0,2) 470.9 nm, N I (2D) 520.0 nm, and N2+ 1NG (5,2) 670.5 nm; hydrogen emission at Hβ 486.1 nm; and sodium emission at NaD 589.3 nm.

2D monochromatic images from HySCAI data showed distinct differences in the spatial distribution of these emissions, reflecting their varying altitudes in the upper atmosphere. For example, the O I (557.7 nm) and O I (630.0 nm) emissions showed significant differences due to their peak altitudes of around 100 km and 150-350 km, respectively. The system also provided valuable insights into the characteristics of precipitating particles, the driving force behind auroral emissions.

By analyzing emission line ratios, the authors estimated the energy and flux of precipitating electrons and protons. In the observed substorm, the precipitating electron energy was calculated to be around 1.6 keV, offering crucial insights into energy transfer processes between the solar wind, magnetosphere, and upper atmosphere.

Applications

HySCAI has significant implications for auroral spectroscopy, enabling the simultaneous capture of spatial and spectral information. This capability can help researchers understand auroral physics, particularly the mechanisms behind different types of emissions.

By analyzing emission ratios, crucial parameters like the energy and flux of precipitating particles can be estimated, which is important for understanding interactions between the solar wind, magnetosphere, and atmosphere. Additionally, HySCAI's comprehensive spectral data can be useful for monitoring atmospheric changes during auroral events, providing insights into space weather.

Conclusion

The novel HySCAI system proved effective in advancing auroral spectroscopy research. By combining 2D imaging capabilities with high-resolution spectral measurements, this system provided researchers with a powerful tool for studying the complex physical processes underlying auroras.

The initial results from HySCAI observations provided valuable insights into auroral emissions and the precipitating particles responsible for them. As this innovative instrument continues to be deployed and refined, it will be instrumental in deepening our understanding of auroras and their role in Earth's upper atmosphere and space environment.

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Journal Reference

Yoshinuma, M., Ida, K., Ebihara, Y. (2024). Development of hyperspectral camera for auroral imaging (HySCAI). Earth Planets Space. DOI: 10.1186/s40623-024-02039-y, https://earth-planets-space.springeropen.com/articles/10.1186/s40623-024-02039-y

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Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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