Target acquisition for multi-object spectroscopy with JWST NIRSpec
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
NIRSpec is the flagship spectrograph for JWST in the 0.6 to 5.3 micron wavelength range. Observation with the Micro- Shutter Assembly (MSA) for multiobject spectroscopy (MOS) will use configurable shutters to form spectral slits and provide the first space-based MOS capabilities. The NIRSpec Micro-shutter Assembly Target Acquisition (MSATA) is an autonomous target acquisition scheme to acquire and position targets accurately with respect to the spectral slits. The method uses measured centroid positions of reference stars with accurately known relative positions across the target field for this process. MSATA performs not only linear offsets, but any required telescope orient (roll) correction to remove blind-pointing alignment error. The MSATA procedure can be used for most NIRSpec science and will be a prerequisite for most NIRSpec MOS mode observations. Astrometry relating the positions of science targets and candidate reference stars with a relative accuracy of 5 - 10 mas will be needed to deliver the best calibration accuracy of science sources. With this level of planning accuracy, the MSATA procedure should yield a final total pointing accuracy for NIRSpec MOS targets of <20 mas within the preselected 200 mas-wide MSA shutter. Here we present analysis of test cases using simulated datasets that were used to help define and check operations flow for NIRSpec MSATA.
Original language | English |
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Title of host publication | Observatory Operations : Strategies, Processes, and Systems VII |
Volume | 10704 |
Publisher | SPIE - International Society for Optical Engineering |
Publication date | 11 Jun 2018 |
Article number | 107041J |
DOIs | |
Publication status | Published - 11 Jun 2018 |
Series | Proceedings of SPIE |
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Number | 10704 |
ISSN | 0277-786X |
- JWST, NIRSpec, Target Acquisition, multi-object spectroscopy, Imaging, TA, MOS, MSA
Research areas
ID: 221834868