Proceedings Article | 12 July 2018
Anurag Deshpande, Nora Lützgendorf, Pierre Ferruit, Giovanna Giardino, Catarina Alves de Oliveira, Stephan Birkmann, Torsten Böker, Elena Puga, Timothy Rawle, Marco Sirianni, Maurice te Plate
KEYWORDS: Iterated function systems, Prisms, Lamps, Sensors, Signal to noise ratio, Data modeling, James Webb Space Telescope, Spectral models, Near infrared spectroscopy, Near infrared, Space telescopes, Infrared backgrounds
The Near Infrared Spectrograph (NIRSpec) instrument is one of the four scientific instruments aboard the James Webb Space Telescope (JWST). NIRSpec can be operated in Multi-Object Spectroscopy (MOS), Fixed-slit Spectroscopy (FS), and Integral Field Spectroscopy (IFS) modes; with spectral resolutions from 100 to 2700. Two of these modes, MOS and IFS, share the same detector real estate and are mutually exclusive. Consequently, the micro-shutters used to select targets in MOS mode must all be closed when observing in IFS mode. However, due to the finite contrast of the micro-shutter array (MSA), some amount of light passes through them even when they are commanded closed. This light creates a low, but potentially significant, parasitic signal, which can affect IFS observations. Here, we present the work carried out to study and model this signal. Firstly, we show the results of an analysis to quantify its levels for all NIRSpec spectral bands and resolution powers. We find a level of parasitic signal that is, in general, lower than 10% of the incident, extended IFS signal. We also show how these results were combined with signal-to-noise considerations to help consolidate the observation strategy for the IFS mode and to prepare guidelines for designing observations. In general, we find that this parasitic signal will be less than the statistical noise of a Zodiacal light exposure up to ~40 groups for the NIRSpec grating configurations, and ~10 groups for the prism configuration. In a second part, we report on the results of our work to model and subtract this signal. We describe the model itself, its derivation, and its accuracy as determined by applying it to ground test data.
