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Ground Layer Adaptive Optics (GLAO) Experiment on Mauna Kea

Ground Layer Adaptive Optics (GLAO) Experiment on Mauna Kea. Doug Toomey. February 2013. The Imaka Project. This talk is about an experiment in support of a larger project called IMAKA

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Ground Layer Adaptive Optics (GLAO) Experiment on Mauna Kea

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  1. Ground Layer Adaptive Optics (GLAO) Experiment on Mauna Kea • Doug Toomey February 2013

  2. The Imaka Project This talk is about an experiment in support of a larger project called IMAKA • IMAKA is a project to build a wide field imager for large telescopes that has improved image quality by using a type of adaptive optics called Ground Layer Adaptive Optics (GLAO) • Adaptive optics involves the correction of atmospheric induced optical aberrations (seeing) using electronically controlled deformable mirrors • IMAKA achieves corrections over much larger fields than present instruments by only fixing atmospheric aberrations close to the ground

  3. Imaka Science • Examples of the projects this is useful for are: • Galaxy Formation and Evolution • Finding and Studying Kuiper Belt Objects • Resolving Stellar Populations of Nearby Galaxies

  4. Traditional Adaptive optics Traditional adaptive optics systems are limited in field of view because as the field increases the star light travels through different paths in the upper atmosphere. This limits corrected fields to about 1 arcminute

  5. Ground Layer Adaptive optics Experiments on Mauna Kea in Hawaii have shown that the turbulence is primarily found in two layers. One at or near the ground and one in the upper atmosphere.

  6. Ground Layer Adaptive optics By just correcting this lower layer a much larger field can be corrected and still produce a useful improvement reducing the image size from 0.5 to 1.25 arcseconds down to 0.3 to 0.4 arcseconds The ground turbulence aberrations and the telescope dome induced aberrations are removed.

  7. `Imaka has the goal to reach the “free-atmosphere” seeing over large fields of view (e.g. 10’s of arcminutes to a degree) Proof • How could we prove that this technique would work on a large telescope. • CFHT funded us last year to perform an experiment on the UH 2.2 meter and the 3.6 meter CFHT telescopes, to try to verify, on-the-sky, the level of performance we can achieve. • Do we really see large correlations of the wavefronts over these large angles when we look thru the telescope?

  8. Tests on the UH 2.2 meter Our foundation: Site studies with numerous optical turbulence profilers: SLODAR, LOLAS, LunarShabar, MKAM (MASS/DIMM)Each of these studies was done “outside” - e.g. not thru one of the big telescopes. Question: Is there something fundamentally different arising within the telescope enclosures? We started on the UH 2.2 meter telescope since we could get more time

  9. The Experiment • Approach: • Following Baranec (2007) - On Mt. Hopkins, in support of MMT GLAO, observe a constellation of stars with multiple wavefront sensors to measure the phase correlations and estimate the GLAO PSF over a 2’ FOV. • We would use five wavefront sensors, on a constellation of stars covering 0.5 degree on UH88” (Cassegrain) and 1 deg on CFHT (prime).

  10. Wavefront sensor Imaging the telescope entrance pupil (the primary mirror) onto a 2d array of lenses (Lenslet array) turn the 2 meter telescope into 400 9 cm telescopes. Measuring the star position from each sub-aperture measures the wavefront tilt in each sub aperature Shack Hartmann wavefront sensor

  11. mWFS experiment • GL and dome seeing - direct measure of the correlation of wavefronts over one degree • UH88: Prototype WFS - July `12 • UH88: 5 WFSs 0.5 deg - September `12 • CFHT: 6 WFS 1.0 deg - Dec/Jan `13 Schedule

  12. mWFS/UH88 • Using the optics for the UH8k camera replacing the focal plane with our five WFSs • First two runs were in September. • Observed two different constellations

  13. What the data looks like...

  14. This is a combination of SHWFS spots from three WFSs. Each WFS is a different color in the image. • When the wavefronts are correlated the spots are white • When the wavefronts are uncorrelated the spots separate into distinct colors What the data looks like... 0.25° 0.5°

  15. What can we learn from this data

  16. What the data tells us... These are the slope cross-covariance maps... A layer at the ground moving with the ground wind speed?

  17. What the data tells us... • From the data we can extract a number of quantitative measures: • Simplest: • Total phase variance (seeing) • We can extract the vertical profile (where in altitude the seeing comes from the ground up to about 600 meters) • What portion of the seeing is common to all wavefront sensors ( the GLAO correctable part of the aberations) • An estimate of the image size that a GLAO system could achieve

  18. What the data tells us... amplitude (nm) time step (50Hz)

  19. What the data tells us...

  20. Next steps... • Working our way thru an incredibly rich data set • Next phase is a GLAO demonstrator on the 88” proposal with NSF pending

  21. The Experiment Team • Mark Chun (UH) PI • Olivier Lai (CFHT) • Tim Butterley (Durham University) • Doug Toomey (MKIR) • Kevin Ho and Derrick Salmon (CFHT) • Yutaka Hayano/Shin Oya (Subaru) - contributing DM/electronics for demonstrator • Simon Thibault (Lavel University) - optical design of demonstrator • Christoph Baranec (CalTech) - real-time controller from RoboAO

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