Numerical model built for the simulation of the earth magnetopause by lobster-eye-type soft X-ray imager onboard SMILE satellite

Peng, Songwu; Ye, Yizhong; Wei, Fei; Yang, Zuhua; Guo, Yong; Sun, Tianran

doi:10.1364/oe.26.015138

Cited by 16 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the previous work, we demonstrated the lobster-eye model and ray tracing method based on imaging simulation and magnetohydrodynamic (MHD) model (Peng et al, 2018;Sun et al, 2019Sun et al, , 2020. Here, we use MHD model to simulate dayside magntosheath X-ray intensity distribution (Wang et al, 2013).…”

Section: Observing Simulationmentioning

confidence: 99%

“…The instrument results are simulated based on the MHD images (Figure 9). Please refer to previous work (Peng et al, 2018) for details of the simulation method. For the instrument image simulation, we use the lobster-eye optic parameters as following: the radius of curvature is 60 cm; micro-channel width and plate thickness are 40 μm and 1.2 mm; the reflectivity material is Ir with 0.5 nm surface roughness; the integration time of the three images is 300 s, and the average energy of one X-ray light is 0.5 keV.…”

Section: Observing Simulationmentioning

confidence: 99%

See 1 more Smart Citation

A Lunar-based Soft X-ray Imager (LSXI) for the Earth’s magnetosphere

Guo

Wang

Wei

et al. 2021

Sci. China Earth Sci.

Self Cite

View full text Add to dashboard Cite

We propose to use the Moon as a platform to obtain a global view of Earth's magnetosphere by a Lunar-based Soft X-ray Imager (LSXI). LSXI is a wide field-of-view Soft X-ray telescope, which can obtain X-ray images of Earth's magnetosphere based on the solar wind charge exchange (SWCX) X-ray emission. Global perspective is crucial to understand the overall interaction of the solar wind with magnetosphere. LSXI is capable of continuously monitoring the evolution of geospace conditions under the impact of the solar wind by simultaneous observation of the bow shock, magnetosheath, magnetopause and cusps for the first time. This proposal is answering the call for the Chinese Lunar Exploration Program Phase IV.

show abstract

Section: Observing Simulationmentioning

confidence: 99%

Section: Observing Simulationmentioning

confidence: 99%

A Lunar-based Soft X-ray Imager (LSXI) for the Earth’s magnetosphere

Guo

Wang

Wei

et al. 2021

Sci. China Earth Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thereafter, many lobster eye telescopes were installed on pico or nano satellites (Van Inneman et al 2000;Hudec 2018;Su et al 2018) or the International Space Station (Tremsin & Siegmund 1998;Fraser et al 2002) for X-ray observations. Thanks to these successful applications, lobster eye telescopes are now considered important wide-field X-ray survey instruments for many satellites, such as the Microchannel X-ray Telescope on board the SVOM (Gotz et al 2014), the Wide-field X-ray Telescope (WXT) on board the Einstein Probe (EP; Yuan et al 2018), the Soft X-ray Imager (SXI) on board the SMILE (Peng et al 2018), the LEXT on board the Gamow (Feldman et al 2021), and the SXI on board the Theseus (Feldman et al 2020). The point-spread function (PSF) of lobster eye telescopes (Rhea et al 2021) is very unique, which would spread photons to a very large scale.…”

Section: Introductionmentioning

confidence: 99%

Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms

Jia

Liu

et al. 2023

ApJS

View full text Add to dashboard Cite

Lobster eye telescopes are ideal monitors to detect X-ray transients because they could observe celestial objects over a wide field of view in the X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point-spread functions, making it hard to design a high-efficiency target detection algorithm. In this paper, we integrate several machine-learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would first generate two 2D images with different pixel scales according to positions of photons on the detector. Then, an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. Finally, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope on board the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mcrab (9.6 × 10−11 erg cm−2 s−1) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.

show abstract

“…Samsonov (2022) [16] used the two global MHD models, SWMF and LFM, and simulated artificial and real events. This paper uses the same approach as Sun, using the simulation images generated by PPMLR-MHD code and SXI simulation software [17].…”

Section: Introductionmentioning

confidence: 99%

Magnetopause Detection under Low Solar Wind Density Based on Deep Learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

Extracting the peak value of the X-ray signal in the original magnetopause detection method of soft X-ray imaging (SXI) for the SMILE satellite is problematic because of the unclear interface of the magnetosphere system under low solar wind density and the short integration time. Herein, we propose a segmentation algorithm for soft X-ray images based on depth learning, we construct an SXI simulation dataset, and we segment the magnetospheric system by learning the spatial structure characteristics of the magnetospheric system image. Then, we extract the maximum position of the X-ray signal and calculate the spatial configuration of the magnetopause using the tangent fitting approach. Under a uniform universe condition, we achieved a pixel accuracy of the maximum position of the photon number detected by the network as high as 90.94% and contained the position error of the sunset point of the 3D magnetopause below 0.2 RE. This result demonstrates that the proposed method can detect the peak photon number of magnetospheric soft X-ray images with low solar wind density. As such, its use improves the segmentation accuracy of magnetospheric soft X-ray images and reduces the imaging time requirements of the input image.

show abstract

Numerical model built for the simulation of the earth magnetopause by lobster-eye-type soft X-ray imager onboard SMILE satellite

Cited by 16 publications

References 20 publications

A Lunar-based Soft X-ray Imager (LSXI) for the Earth’s magnetosphere

A Lunar-based Soft X-ray Imager (LSXI) for the Earth’s magnetosphere

Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms

Magnetopause Detection under Low Solar Wind Density Based on Deep Learning

Contact Info

Product

Resources

About