This paper shows that a simple convolutional neural network (CNN) can be used to build an object-agnostic wavefront sensor. Using the well-known Phase Diversity approach as a point of departure, Fourier-space metrics are computed from the conventional and diversity images and then fed to the CNN, which predicts values of the underlying Zernike coefficients. The methodology is shown to work in the presence of Gaussian noise. Prediction errors for defocus, astigmatism, and spherical are on the order of 1/100 of the wavelength.
NASA’s James Webb Space Telescope (JWST) is a 6.5m diameter, segmented, deployable telescope for cryogenic IR space astronomy. The JWST Observatory includes the Optical Telescope Element (OTE) and the Integrated Science Instrument Module (ISIM), that contains four science instruments (SI) and the Fine Guidance Sensor (FGS). The SIs are mounted to a composite metering structure. The SIs and FGS were integrated to the ISIM structure and optically tested at NASA's Goddard Space Flight Center using the Optical Telescope Element SIMulator (OSIM). OSIM is a full-field, cryogenic JWST telescope simulator. SI performance, including alignment and wavefront error, was evaluated using OSIM. We describe test and analysis methods for optical performance verification of the ISIM Element, with an emphasis on the processes used to plan and execute the test. The complexity of ISIM and OSIM drove us to develop a software tool for test planning that allows for configuration control of observations, implementation of associated scripts, and management of hardware and software limits and constraints, as well as tools for rapid data evaluation, and flexible re-planning in response to the unexpected. As examples of our test and analysis approach, we discuss how factors such as the ground test thermal environment are compensated in alignment. We describe how these innovative methods for test planning and execution and post-test analysis were instrumental in the verification program for the ISIM element, with enough information to allow the reader to consider these innovations and lessons learned in this successful effort in their future testing for other programs.
The James Webb Space Telescope (JWST) Observatory is the follow-on mission to the Hubble Space Telescope (HST). JWST will be the biggest space telescope ever built and it will lead to astounding scientific breakthroughs. The mission will be launched in October 2018 from Kourou, French Guyana by an ESA provided Ariane 5 rocket. NIRSpec, one of the four instruments on board of the mission, recently underwent a major upgrade. New infrared detectors were installed and the Micro Shutter Assembly (MSA) was replaced as well. The rework was necessary because both systems were found to be degrading beyond a level that could be accepted. Now in its final flight configuration, NIRSpec underwent a final cryogenic performance test at NASA’s Goddard Space Flight Center (GSFC) as part of the Integrated Science Instrument Module (ISIM). This paper will present a status overview and results of the recent test campaigns.
The Near-Infrared Spectrograph (NIRSpec) is one of the four instruments on the James Webb Space Telescope (JWST) which is scheduled for launch in 2018. NIRSpec is developed by the European Space Agency (ESA) with Airbus Defense and Space Germany as prime contractor. The instrument offers seven dispersers covering the wavelength range from 0.6 to 5.3 micron with resolutions from R ∼ 100 to R ∼ 2700. NIRSpec will be capable of obtaining spectra for more than 100 objects simultaneously using an array of micro-shutters. It also features an integral field unit with 3” x 3” field of view and a range of slits for high contrast spectroscopy of individual objects and time series observations of e.g. transiting exoplanets. NIRSpec is in its final flight configuration and underwent cryogenic performance testing at the Goddard Space Flight Center in Winter 2015/16 as part of the Integrated Science Instrument Module (ISIM). We present the current status of the instrument and also provide an update on NIRSpec performances based on results from the ISIM level test campaign.