2022
DOI: 10.48550/arxiv.2202.09334
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First hard X-ray imaging results by Solar Orbiter STIX

Paolo Massa,
Andrea F. Battaglia,
Anna Volpara
et al.

Abstract: Context. The Spectrometer/Telescope for Imaging X-rays (STIX) is one of 6 remote sensing instruments on-board Solar Orbiter. It provides hard X-ray imaging spectroscopy of solar flares by sampling the Fourier transform of the incoming flux. Aims. To show that the visibility amplitude and phase calibration of 24 out of 30 STIX sub-collimators is well advanced and that a set of imaging methods is able to provide the first hard X-ray images of the flaring Sun from Solar Orbiter. Methods. We applied four visibilit… Show more

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Cited by 2 publications
(2 citation statements)
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“…STIX is an indirect imaging system that reconstructs images from a set of 30 visibilities. For the analysis presented here, the calibration as of January 2022 has been used, which provides good calibration for 24 subcollimators with angular resolutions ranging from 14 up to 180 (for details we refer to Massa et al 2022). The images shown in this paper are reconstructed using the CLEAN algorithm (Högbom 1974) adapted for STIX with a clean beam size of 16.5 FWHM.…”
Section: Instrumentationmentioning
confidence: 99%
“…STIX is an indirect imaging system that reconstructs images from a set of 30 visibilities. For the analysis presented here, the calibration as of January 2022 has been used, which provides good calibration for 24 subcollimators with angular resolutions ranging from 14 up to 180 (for details we refer to Massa et al 2022). The images shown in this paper are reconstructed using the CLEAN algorithm (Högbom 1974) adapted for STIX with a clean beam size of 16.5 FWHM.…”
Section: Instrumentationmentioning
confidence: 99%
“…In the case of radio astronomy, interpolation in the visibility space and application of inverse Fourier transform are sufficient to obtain reliable images of the radio source (Arras et al 2021). In the RHESSI and even more in the STIX frameworks, more sophisticated mathematics based on regularization theory for ill-posed problems is needed to reduce the ambiguity induced by such a sparse sampling of the data space (Massa et al 2022;Perracchione et al 2021;Massa et al 2020;Massone et al 2009).…”
Section: Introductionmentioning
confidence: 99%