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Algorithms for maintaining quasi-

static alignment of the ATST



Robert Upton









1

Outline



• Problem statement

• Operating conditions and figures-of-merit

• Alignment strategy

• ATST linear optical system

• Quasi-static alignment (QSA) strategy using damped

least squares and maximum likelihood reconstructors

• Modeling and simulation results

• Ongoing development and analysis







2

Problem statement



• Design and implement an active alignment strategy that:

– does not rely on least-square minimization embedded within

optical software









3

Operating conditions and

figures-of-merit

• The active alignment strategy must:

– not be sensitive to atmospheric noise and guiding error

– correct 50% encircled energy diameter from ~2 arc-sec in

perturbed state to less than 0.017 arc-sec

– correct for large amplitude and low spatial frequency optical

errors

– be integrated into the ATST wavefront sensing commissioning

strategy

• ATST figures-of-merit:

– RMS wavefront error

– 50 % encircled energy diameter





4

Alignment strategy

Truncated singular values for

• The ATST has up to 44 DOF from Gregorian front end

perturbed degrees-of-freedom DOF Truncated sing val

that can be corrected in a least- 1

2

M1A

M1B

2.01E+03

1.77E+03

norm sense with the QSA 3 M1 XTrefoil 2.45E+01

4 M1C 3.52E+00

– M1: 6 Rigid body modes + 4 5 M2B 3.18E+00

Bending modes 6 M2C 3.18E+00

7 M1 YTrefoil 1.82E+00

– M2: 6 Rigid body modes + 4 8 M2X 1.12E+00

9 M2A 5.20E-01

Bending modes 10 M2Z 5.06E-01

– M4: 6 Rigid body modes 11 M1 45Astig 5.41E-02

12 M2 XTrefoil 4.02E-02

– M7: 6 Rigid body modes 13 M2 YTrefoil 3.83E-02

14 M2Y 2.20E-03

– M10: 6 Rigid body modes 15 M2 XAstig 4.30E-04

– M11: 6 Rigid body modes 16 M1Z 2.09E-04

17 M2 45Astig 1.47E-04

18 M1Y 7.85E-05

19 M1X 1.66E-05

5

20 M1 XAstig 1.47E-05

ATST Perturbed mirrors





M7

M9



M8 M3 M4

M5, M6 Camera

M2 M13

M12

M11

M10

M9







M1









• Perturbed mirrors are in Gregorian front-end and Coude

spaces 6

Alignment strategy



• The alignment compensators are M2 rigid bodies and

M1 low-order bending modes

• Pupil and image boresight are maintained with M3 and

M6 by lookup table

• For noisy systems and large telescope perturbations the

damped-least squares and maximum likelihood

reconstructors are used









7

Alignment strategy

• The alignment signal is a matrix multiply between the

wavefront sensor signals and the reconstructor

• Wavefront sensor geometry chosen as an optimum

subset of a 3x3 grid

• Wavefront sensor modes are 8 Zernike RMS coefficients

over three field positions

Y Y



WFS1(1.5,1.5)





X WFS2(0,0)

X





WFS3(1.5,-1.5)

8

ATST linear optical

system

   

• The ATST is linearized to form g  Hf  h0  n



g Vector of RMS weighted Zernike polynomial coefficients



f Vector of ATST active degrees-of-freedom



h0 Vector of Zernike polynomial coefficients. Baseline optical performance



n Vector of noise contributions to Zernike polynomial coefficients

H ATST active optics interaction matrix



• The subset of columns representing the active optics

compensators in H are chosen in the least-norm inverse

• The compensator matrix is HS

9

Reconstructors



• The damped least-squares reconstructor: Tikhonov

filtering of singular values



 S  

f    2 m

 T T   



un vn g  h0  n 

m 1   m   





• The maximum likelihood reconstructor: Maximize joint

probability P(f|n) with respect to f in the presence of n





f   H S nnT





T 1

H S  ff T 1

H





T

S nn T 1



  

g  h0  n 

10

Reconstructors



• The damped least-squares reconstructor:

– Useful for obtaining linear least-squares solution for large

perturbation amplitudes

– Good formalism for reconstruction with non-optimized noise

control

• The maximum likelihood reconstructor:

– “Optimized” reconstructor in the presence of noise and known

telescope perturbation statistics

– Not optimized for reconstruction of alignment with perturbation of

Coude conics. Covariance of telescope error requires treatment



f   H S nnT





T 1

H S  ff T 1

H





T

S nn T 1



  

g  h0  n 

11

Modeling and simulation results:

Estimating atmospheric noise

• The noise contribution is estimated for a Kolmogorov atmosphere

that obeys the Taylor frozen flow hypothesis

• Sample case*:

r0=5cm: =0.5m: h=20 m

bandwidth=10 Hz: Speed=5 m/s

average over 250 samples

Zernike RMS noise

radial order (nm)

N=2 7.95

N=3 6.52

N=4 6.08



• In simulations 10 nm RMS

amplitude noise is added to each

Zernike polynomial coefficient 12

*F.Hill etal, “Site testing for the ATST.” SPIE 6267

Analysis cases: Monte-

Carlo perturbation of ATST

• Case I: Perturbation of Gregorian front-end: Decenters

amplitude = 500 m; Tilts amplitude = 10e-03 degrees;

Surface bending amplitude = 500 nm





• Case II: Perturbation of Gregorian front-end and Coude

optics: Gregorian front-end: As in Case I. Coude optics:

Decenters amplitude = 1.5 mm; Tilts amplitude = 15e-03

degrees









13

Damped least-squares

reconstructor results:CASE I

Perturbed Corrected

RMS

wavefront

error









50%

encircled

energy 97% of cases

<17e-03 arc sec









14

Damped least-squares

reconstructor results:CASE II

Perturbed Corrected

RMS

wavefront

error









50%

encircled

100% of cases

energy <17e-03 arc sec









15

Maximum likelihood

reconstructor results:CASE I

Perturbed Corrected

RMS

wavefront

error









50%

encircled

energy 100% of cases

<17e-03 arc sec









16

Maximum likelihood

reconstructor results:CASE II

Perturbed Corrected

RMS

wavefront

error









50%

encircled 95% of cases

energy <17e-03 arc sec









17

Conclusions



• Demonstrated strategy and algorithm for quasi-static

active optical control of ATST using WFS distributed in

FOV

• The damped least squares reconstructor is vital for

controlling ATST non-linear behavior

• Monte-Carlo simulations in the presence of atmospheric

noise and Coude misalignments demonstrated

• Demonstrated reconstruction using maximum likelihood

and Tikhonov filtering exceeds error budget

requirements





18

QSA extension



• aO wavefront sensor optical design is complete

• QSA is demonstrated with the aO wavefront sensor

optical design

• QSA and HOAO optical designs can be integrated with

instrument designs to model non-common path errors









19

Bibliography



• Roddier, “Adaptive optics for astronomy.” Cambridge University

Press (2004)

• Conan, “Temporal filtering of atmosphere.”JOSA (1994)

• Barrett and Myers, “Principles of image science.” Wiley (2004)

• Vogel, “Inverse methods.” SIAM (2002)

• Lubliner and Nelson, “Stressed mirror polishing. 1: Atechnique for

producing non-axisymmetric mirrors” App. Opt (1980)

• Upton, Rimmele and Hubbard “Active optical alignment of ATST.”

SPIE (2006)

• Upton, “Optical control of ATST.” App. Opt. (2006)









20



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