Image reconstruction is carried out using spiros in IMAGING mode. It is recommended to first use background method 3 in conjunction with the flat-field background model. The optimization statistic should first be used, while the LIKEH can be tried in a second run. In principle, both optimization statistics should produce compatible results. However, with the statistic all the bins with zero counts are ignored, and this can lead to biases (hard tail at high energy, or problem with pseudo-detectors at low energy where the number of double/triple can be null). On the other hand, statistic seems to be more robust and faster in most cases.
spiros searches for the brightest source first, it then removes its contribution to the original binned event data set, and continues iteratively with increasingly fainter sources. The maximum number of sources spiros should search for must be specified as well as the minimum acceptable sigma for source detection (the number of sources is meant to be in addition to those that may be specified in a source_cat file). For the source number, try first with the number of sources that should be detected in your INTEGRAL data set given the field of view, plus 1 or 2 for possible unexpected sources. For the sigma threshold, use 5 or 6 to be safe. Below that level you may get spurious detections as spiros is a bit optimistic at the lower S/N end.
Since spiros reconstruct a sky image, the FoV has to be specified. A convenient way to do that is to use the FoV parameter. With POINTING+ZCFOV, spiros you will always image a square angular region defined by the extent of the centers of all pointings, augmented by the SPI zero-coded FoV (see below Sect. 7.9 for a possible strong bias).
Sky images are produced at each iteration. The final images produced
after the last iteration are
``spiros_image_intensity_result.fits'' and
``spiros_image_sigma_result.fits''. The source positions and fluxes
are output in source_res.fits.