post-processing in the context of wfirst-afta · •reference diversity (temporal): reference...

Post-processing in the context of WFIRST-AFTA

LOWFSC Meeting - JPL 13th February 2015

1

Marie Ygouf, Rémi Soummer, Laurent Pueyo, Marshall Perrin

New challenges in PSF subtraction

• Development of a coronagraphic instrument and dedicated post-processing methods at the same time !

• Contrast requirement: 10-8 (10-9 after post-processing)

DM1

DM2Apodiser

FPM

Lyot Stop

Coronagraph

Pueyo et al 2014

WFS-C

Post-!processing

Coronagraph

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Outline

33

• Exploration of different successful families of post-processing PSF subtraction methods in the context of WFIRST-AFTA ‣ => Estimation of PSF in focal plane to subtract it ‣ Spatial Diversity ‣ Spectral Diversity ‣ Reference Diversity

!• Joint objet estimation and phase retrieval: Medusae ‣ Tested and validated on SPHERE-like simulated data ‣ How this method be useful in the context of WFIRST-AFTA

PSF subtraction in focal plane

• Spatial diversity: Angular Differential Imaging (ADI) !!!

!!!

‣ Sophisticated algorithm using ADI: LOCI (Lafrenière et al. 2007) ‣ Need telescope rolls => yet to be decided for WFIRST-AFTA…

• Reference diversity (temporal): Reference Differential Imaging (RDI) • Spectral diversity: Spectral Differential imaging (SDI)

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(Marois et al., 2005/2008)


• Spatial diversity: Angular Differential Imaging (ADI) ‣ Sophisticated algorithm using ADI: LOCI ‣ Need telescope rolls => yet to be decided for WFIRST-AFTA…

• Reference diversity (temporal): Reference Differential Imaging (RDI) !!!!!!‣ Use different target as a reference PSF ‣ Sophisticated algorithm using PSF subtraction: KLIP

• Spectral diversity: Spectral Differential imaging (SDI)5

(HST data: Soummer et al., 2012, Choquet et al.)

WFIRST-AFTA observing scenario (Nemati)

RDI diversity, processed w/KLIP

• Karhunen-Loève Image Projection (Soummer et al. 2012) ‣ Principal Component Analysis

(PCA)

• “AFTA-like” noiseless temporal sequences with fake planets ‣ fake planets detected ‣ Separations between 3.5 and 4.2 λ/D

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Preliminary results ‣ Contrast gain of 5 on this data set

2 4 6 8 10 12 14Angular separation [h/D]

1

10

100

1000

5m le

vel o

f res

idua

l spe

ckle

s (A

rbitr

ary

norm

aliz

atio

n)

Gain of 5on contrast

After KLIPUnprocessed PSF

Before KLIP After KLIP



• Reference diversity (temporal): Reference Differential Imaging (RDI) ‣ Use different target as a reference PSF ‣ Sophisticated algorithm using PSF subtraction: KLIP

• Spectral diversity: Spectral Differential imaging (SDI) !!!!‣ Sophisticated algorithms using SDI: LOCI, KLIP (Soummer et al, 2012),

Spectral Deconvolution (Sparks and Ford, 2002)

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AFTA-like multispectral images

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(N’Diaye, Ygouf)

AFTA-like multispectral images

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(N’Diaye, Ygouf)

Spectral Deconvolution

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"Perfect" coronagraph − With DM − Planet at 9 h0/D − Contrast 1.E−09 − 36% bandpass − 4th−order polynomial fit

1 10Angular separation [h/D]

10−10

10−8

10−6

10−4

10−2

5m le

vel o

f res

idua

l spe

ckle

s (n

orm

aliz

ed to

non−c

oron

agra

phic

cen

tral s

tar)

Planet flux after CSDPlanet flux before CSDafter CSDhmax with DMh0 with DMhmin with DMhmax without DMh0 without DMhmin without DM

Gain of 7on speckles

Loss of 20on planet flux

"Perfect" coronagraph − Without DM − Planet at 9 h0/D − Contrast 5.E−06 − 36% bandpass − 4th−order polynomial fit

1 10Angular separation [h/D]

10−10

10−8

10−6

10−4

10−2

5m le

vel o

f res

idua

l spe

ckle

s (n

orm

aliz

ed to

non−c

oron

agra

phic

cen

tral s

tar)

Planet flux after CSDPlanet flux before CSDafter CSDhmax without DMh0 without DMhmin without DM

Gain of 531on speckles

Loss of 20on planet flux

Without DM => Detection

With one DM => Non detection



• Reference diversity (temporal): Reference Differential Imaging (RDI) ‣ Use different target as a reference PSF ‣ Sophisticated algorithm using PSF subtraction: KLIP

• Spectral diversity: Spectral Differential imaging (SDI) ‣ Sophisticated algorithms using CDI: LOCI, KLIP (Soummer et al, 2012),

Spectral Deconvolution (Sparks and Ford, 2002) ‣ Different speckle behavior with the wavelength on WFIRST-AFTA ‣ Less than 20% broadband on WFIRST-AFTA ‣ Need for different kind of methods:

- precise model of speckle evolution with the wavelength - model of the planet

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Outline

1212

• Exploration of different successful families of post-processing PSF subtraction methods in the context of WFIRST-AFTA ‣ => Estimation of PSF in focal plane to subtract it ‣ Spatial Diversity ‣ Spectral Diversity ‣ Reference Diversity

!• Joint objet estimation and phase retrieval: Medusae ‣ Tested and validated on SPHERE-like simulated data ‣ How this method be useful in the context of WFIRST-AFTA

• Joint object estimation and phase retrieval • Inverse problem associated with an imaging model • Estimation of posterior probability (Bayes Formalism) • Possibility to use prior information on the system to regularize the

problem

13

MEDUSAE: Multispectral Exoplanet Detection Using Simultaneous Aberration Estimation (Ygouf et al., 2013)

• Joint object estimation and phase retrieval • Inverse problem associated with an imaging model !!!!!‣ Model of instrument links aberrations in pupil plane to PSF in the focal

plane ‣ Estimation of aberrations (phase retrieval) and of object map (object is

modeled!)

• Estimation of posterior probability P(o,phi|i) (Bayes Formalism) • Possibility to use prior information on the system to regularize the

problem14


• Joint object estimation and phase retrieval • Inverse problem associated with an imaging model • Maximization of posterior probability L(o,phi|(Bayes Formalism) ‣ Probabilistic framework ‣ Noise statistic gives likelihood L(i|o,phi) ‣ Use Bayes rule to convert L(i|o,phi) into L(o,phi|i)

!!

• Possibility to use prior information on the system to regularize the problem

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€

J oλ{ }, fλ*{ },δu( ) =1

2σ n2 x,y( )

iλ − fλ* ⋅ hλ

c δu( ) − oλ ∗hλnc2

x,y∑

λ

∑ x,y( ) + Rx,y,λ o,δ( )

• Joint object estimation and phase retrieval • Inverse problem associated with an imaging model • Estimation of posterior probability P(o,phi|i) (Bayes Formalism) ‣ Probabilistic framework ‣ Noise statistic gives likelihood ‣ Use Bayes rule: L(i|o,phi => L(o,phi|i) !!!

‣ Criterion minimization ‣ Difficult problem

• Possibility to use prior information on the system to regularize the problem

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Convex criterion Non-convex criterion

€

J oλ{ }, fλ*{ },δu( ) =1

2σ n2 x,y( )

iλ − fλ* ⋅ hλ

c δu( ) − oλ ∗hλnc2

x,y∑

λ

∑ x,y( ) + Rx,y,λ o,δ( )

Regularization terms

Starting point 1 Starting point 2 Starting point 1 Starting point 2

Global minimum

• Joint object estimation and phase retrieval • Inverse problem associated with an imaging model • Estimation of posterior probability (Bayes Formalism) • Possibility to use prior information on the system to regularize the

problem ‣ Informations on the object (point source or extended…), on the

instrument (stability of aberrations, dominant aberrations…), on the physics (propagation regime…)

‣ Possible and desirable interactions with WFS&C (clever starting point for algorithm, complementary estimation of aberrations)

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Conclusions

• PSF subtraction is still one of the challenges of high-contrast imaging especially in the context of future space-based instruments with a totally different regime of contrast

• Spatial diversity (ADI) ‣ Need telescope rolls => yet to be decided for WFIRST-AFTA…

• Reference diversity (RDI) ‣ Most promising => cheap and already demonstrated a gain on AFTA-like

simulated images ‣ More work to assess the real expected performance

• Spectral diversity (SDI) ‣ Different speckle behavior with the wavelength and limited bandpass

(<20%) ‣ Need to test more sophisticated methods using prior information on the

system: Medusae ‣ The more information we have on the instrument, the better it is!

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post-processing in the context of wfirst-afta · •reference diversity (temporal): reference...

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