1 Manal Chebbo, Alastair Basden, Richard Myers, Nazim Bharmal, Tim Morris, Thierry Fusco, Jean-Francois Sauvage Fast E2E simulation tools and calibration strategies for EAGLE-MOAO on the E-ELT Manal Chebbo AO4ELT3 30 May 2013

2 DASP VS E2E-S Durham Ao Simulation Platform VS E2E- Sparse (LAM / ONERA)  Conventional MVM dense  Sparse methods utilized.  Atmosphere to Slopes slow but accurate model.  Typically needs to run for several hours to simulate 10 – 100 s of operational time, but with high fidelity…  Sparse methods only.  Atmosphere to Slopes fast but approximated model.  Speed comparable to analytical codes, but capabilities of E2E codes, for ex: Optical tolerance studies… Basden et al 2007 Chebbo et al 2011 AO4ELT2

Synopsis 3 1.Sparse E2E Simulator 2.DASP Simulator 3.Conclusion and Perspective Manal Chebbo AO4ELT3 30 May 2013

Synopsis 4 1.Sparse E2E Simulator Manal Chebbo AO4ELT3 30 May 2013

5  Sparse E2E Simulator AO in blocks Configuration system Turbulent generator WFS model Tomographic reconstructor DM model Manal Chebbo AO4ELT3 30 May 2013

6  Sparse E2E Simulator  Description of the different modules Configuration System Manal Chebbo AO4ELT3 30 May 2013

7  Sparse E2E Simulator  Description of the different modules Turbulence generator  Generate layers in the form of pixilated phase screen Simulation and computation of white noise FT Inverse FT → real part d/r 0 Wind speed, direction for the considered layer Manal Chebbo AO4ELT3 30 May 2013

8  Sparse E2E Simulator  Description of the different modules Wave Front Sensor Sparse-Model ➜ Realistic sparse Geometry Model of Shack- Hartman WFS Illuminated and partly illuminated subapertures are managed 400 subapertures / 308 are fully or partly illuminated in the pupil RCO/RCO_d FORMAT : (Representation Complete and Ordered) Ds = {rco} Ds.r = 2* Ns 2 Ds.c = N 2 Ds.n = 4* nb_pix* Ns 2 Ds.ix = ptr_new([0,make_array(D.r, /long)]) Ds.jx = ptr_new(lonarr(D.n + bandwidth)) Ds.xn = ptr_new(make_array(D.n+ bandwidth, /float )) Manal Chebbo AO4ELT3 30 May 2013

9  Sparse E2E Simulator  Description of the different modules Sparse Wave Front Reconstruction  Sparse noise covariance matrix C n Noise are not correlated noise is uniform →  Turbulence covariance matrix Manal Chebbo AO4ELT3 30 May 2013

10  Sparse E2E Simulator  Description of the different modules Deformable Mirror sparse-Model ➜ DM with sparse influence functions Ifs profile ~ double Gaussian IF = 0 beyond N acinf Fs → RCO → [ N 2, N act-valid ] 64*64 actuators, Nacinf= 4 Nacinf= 6 30% of mechanical coupling for minimizing the fitting error. Manal Chebbo AO4ELT3 30 May 2013

11  Sparse E2E Simulator  Challenges of AO with Tomography Tip, Tilt and Defocus Indetermination ➜ Goal: Generate realistic WFS measurement’s with filtered out TTF S TTF Manal Chebbo AO4ELT3 30 May 2013

12  Sparse E2E Simulator  Challenges of AO with Tomography Tip, Tilt and Defocus Indetermination Manal Chebbo AO4ELT3 30 May methods present : ? Setting mean slopes to zero. ? M contain all the modes generated by the AO system. 2 methods present : ? Setting mean slopes to zero. ? M contain all the modes generated by the AO system.

13  Sparse E2E Simulator  Challenges of AO with Tomography Tip, Tilt and Defocus Indetermination Method-1  TT is reduced to 7%  Defocus is not filtered Manal Chebbo AO4ELT3 30 May 2013 Subtraction of average slopes

14  Challenges of AO with Tomography  Tip, Tilt and Defocus Indetermination M contains the WFS answer to all the modes generated by the system Method - 2 Karhunen–Loève TTF are filtered out Manal Chebbo AO4ELT3 30 May 2013

Synopsis 15 2.DASP Simulator Manal Chebbo AO4ELT3 30 May 2013

16Manal Chebbo AO4ELT3 30 May 2013 DASP VS E2E-S  Conventional MVM dense  Sparse methods utilized.  Atmosphere to Slopes slow but accurate model.  No, Simple only TT  Sparse methods only.  Atmosphere to Slopes fast but approximated model  TTF filtered out. Basden et al 2007 Chebbo et al 2011 AO4ELT2

17Manal Chebbo AO4ELT3 30 May 2013  DASP ● Durham AO Simulation Platform ● Python and C ● MPI, shared memory, and multi-threaded ● Suitable for ELT-scale problems ● Cross checked with other codes: Octopus (42 m), YAO (4.2 m), Fourier (ONERA) (42 m)...

18Manal Chebbo AO4ELT3 30 May 2013  DASP  EAGLE E2E DASP-simulations Simulation Parameter: 42 m, central obscuration = 6 m R0 = nm. L0= 30 m SH With 84*84 LGS closed loop GLAO with M4 and the same with open loop correction on the MOAO DM 40 s of telescope time (10000 iterations) No NGS, 6 LGS in a regular hexagon

19Manal Chebbo AO4ELT3 30 May 2013  DASP  EAGLE E2E DASP-simulations H-band Ensquared energy in 75mas over the field of view. 6 LGS at 7.3arcmin diameter 6 LGS, 7.3 arcminute diameter Strehl Map

Actuators required ● By making use of global M4 GLAO correction – Can reduce requirements for MOAO DMs – MOAO can be relaxed somewhat without dramatically affecting AO performance. ● To ones that can be bought today!  DASP  EAGLE E2E DASP-simulations 20Manal Chebbo AO4ELT3 30 May 2013

● Steep fall with rotation ● 0.2 degrees ● ~1/8 th subap at edges 21 Alignment tolerance ● Lateral misalignment reduces performance – Up to 1/8th of a sub-aperture has small effect  DASP  EAGLE E2E DASP-simulations Rotational tolerance Manal Chebbo AO4ELT3 30 May 2013

Conclusion: Current Project is to produce a new validated rapid E2E simulation which combines the speed advantages of analytic codes with the ease performing engineering tolerance analysis, for example: Rotational and misalignment Future Work:  Cross check the codes  New user interface for E2E-S code  Aim to produce a new form of systems engineering for ELT scale AO problems. 22Manal Chebbo AO4ELT3 30 May 2013

23Manal Chebbo AO4ELT3 30 May Thank You

Comparison with other codes 24Manal Chebbo AO4ELT3 30 May 2013