Prototype of S4EI (spectral sampling with slicer for stellar and extragalactical instrumentation): a new generation 3D Spectro-imager
Abstract
S4EI (Spectral Sampling with Slicer for Stellar and Extragalactical Instrumentation) is a new concept for extending Multichannel Subtractive Double Pass (ie S4I - Spectral Sampling with Slicer for Solar Instrumentation) to night-time astronomy. The Multichannel Subtractive Double Pass (MSDP) spectrographs have been widely used in solar spectroscopy because of their ability to provide an excellent compromise between field of view and the spatial and spectral resolutions. Compared with other spectrographs, MSDP can deliver simultaneous monochromatic images without any time-scanning requirements (as the standard Fabry-Perot), with limited loss of flux. Spatial resolution is the same as for an Imager given by the telescope: it can be very high. It is based on new generation reflecting plane image slicers working with large apertures specific to night-time telescopes. The resulting design could be potentially very attractive and innovative for different domains of astronomy, e.g., the simultaneous spatial mapping of accurately flux-calibrated emission lines between OH sky lines in extragalactic astronomy or the simultaneous imaging of stars, exoplanets and interstellar medium. The determination of physical and chemical properties of galaxies needs to observe several emission lines at different wavelengths. The combination of these lines gives access to the distribution in dust, star formation rate, metallicity, the kinematics or even to the electron density of the gas in the galaxies. The spatial resolution of MSDP allows, like the 3D or integral field spectrographs the construction of spatial distribution maps. The advantage of S4EI is that by measuring simultaneously the different lines, the relative errors of the flux calibration between the different wavelengths of the lines are potentially limited by the uncertainty of the calibration source used, which is expected to significantly reduce the associated errors and thus increase the precision and accuracy of estimates.