Forward fitting STIX visibilities

Abstract

Aims. We seek to determine to what extent the problem of forward fitting visibilities measured by the Spectrometer/Telescope Imaging X-rays (STIX) on board Solar Orbiter becomes more challenging with respect to the same problem in the case of previous hard X-ray solar imaging missions. In addition, we aim to identify an effective optimization scheme for parametric imaging for STIX. Methods. This paper introduces a global search optimization for forward-fitting STIX visibilities and compares its effectiveness with respect to the standard simplex-based optimization used so far for the analysis of visibilities measured by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). We made this comparison by considering experimental visibilities measured by both RHESSI and STIX, as weel as synthetic visibilities generated by accounting for the STIX signal formation model. Results. We found that among the three global search algorithms for parametric imaging, particle swarm optimization (PSO) exhibits the best performances in terms of both stability and computational effectiveness. This method is as reliable as the simplex method in the case of RHESSI visibilities. However, PSO is significantly more robust when applied to STIX simulated and experimental visibilities. Conclusions. A standard optimization based on local search of minima is not effective enough for forward-fitting the few visibilities sampled by STIX in the spatial frequency plane. Therefore, more sophisticated optimization schemes based on global search must be introduced for parametric imaging in the case of the Solar Orbiter X-ray telescope. The forward-fitting routine based on PSO proved to be significantly robust and reliable, and it could be considered as an effective candidate tool for parametric imaging in the STIX context.

Identifying the energy release site in a solar microflare with a jet

“…For the reconstruction we only included the subcollimators 10 to 6 and we used the CLEAN algorithm (Högbom 1974), with a CLEAN beam width of 45 arcsec, which reflects the spatial resolution of subcollimators 6. In light blue, we indicate the error on the source location as deduced from the visibility forward fitting algorithm (VIS_FWDFIT, Volpara et al 2022). A preliminary STIX aspect solution (Warmuth et al 2020) has been applied, but the currently available accuracy is only accurate within about 30 arcsec.…”

Unbiased CLEAN for STIX in Solar Orbiter

“…Table 1 contains the reconstructed parameters associated with these four maps and with the CLEAN components map. The mean and standard deviations in this table are obtained by perturbing the visibilities according to Volpara et al (2022). The parameters associated with the maximum of the flux are rather similar for all reconstructions, while CLEAN photometry decreases with increasing FWHM values.…”

“…However, we are now in the Solar Orbiter (Müller et al 2020) era and other image reconstruction methods besides CLEAN are currently used for the analysis of the hard-X-ray visibilities recorded by the Spectrometer/ Telescope for Imaging X-rays (STIX) on board the ESA cluster (Krucker et al 2020). This is due to the fact that novel imaging algorithms have been introduced (Massa et al 2020;Volpara et al 2022;Perracchione et al 2021a;Siarkowski et al 2020;Duval-Poo et al 2018;Felix et al 2017), which are characterized by notable reliability and by an automation degree higher than the one offered by CLEAN. In fact, the step of the iterative scheme that requires the convolution of the CLEAN components map with the CLEAN beam significantly depends on the user's choice, since the functional shape of the CLEAN beam (e.g., its FWHM) is typically designed according to heuristic motivations.…”

Solar flare hard X-rays from the anchor points of an eruptive filament

“…The complex visibility values were computed by means of the August 2022 version of the STIX Ground Software 4 . For the reconstruction of the STIX images, we used the Clean method (stx_vis_clean; Högbom 1974), the maximum entropy method (MEM_GE; Massa et al 2020), and the new procedure for forward fitting (stx_vis_fwdfit_pso; Volpara et al 2022). The rationale behind the forward-fitting method is the following.…”

“…Then, the values of the parameters that best fit the data (i.e., minimize the χ 2 ) were determined by means of the particle swarm optimization algorithm (PSO; Kennedy & Eberhart 1995). Finally, estimates of the uncertainty on the parameter values were provided by repeating the forward-fitting procedure 20 times, each time from visibilities perturbed with Gaussian noise, and by computing the standard deviation of the 20 results (Volpara et al 2022). Hence, the forward-fitting procedure returned not only a reconstructed image, but also the values of position, size, and flux of each source and the associated error.…”