Post-processing in the context of WFIRST-AFTA
LOWFSC Meeting - JPL 13th February 2015
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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)
PSF subtraction in focal plane
• 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
PSF subtraction in focal plane
• 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) !!!!‣ 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
PSF subtraction in focal plane
• 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) ‣ 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
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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
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 • 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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MEDUSAE: Multispectral Exoplanet Detection Using Simultaneous Aberration Estimation (Ygouf et al., 2013)
€
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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MEDUSAE: Multispectral Exoplanet Detection Using Simultaneous Aberration Estimation (Ygouf et al., 2013)
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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MEDUSAE: Multispectral Exoplanet Detection Using Simultaneous Aberration Estimation (Ygouf et al., 2013)
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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