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Planck CMB Lensing
Julien CarronOn behalf of Planck’s collaboration
Several slides credit to Antony Lewis
CMB Lensing :
• 2 arcmin deflection of CMB photons on their path from last scattering surface to us by the large scale structure.
• Valuable cosmological signal in itself, that also correlates to large scale structure tracers.
• Very linear (z ~ 2), no intrinsic alignments, no photo-z’s to obtain…
• Lensing potential map always estimated from data via estimators quadratic in CMB data (Okamoto & Hu 2003)
Methodology
7
Quadratic estimators for a small anisotropy source :
Okamoto & Hu 2003,Hanson & Lewis 2012
Inverse filtering
�
�
First iteration of a vastly more sophisticated max. likelihood procedure, assuming Gaussian unlensed CMB fields. First step enough for Planck.
� ln p[�]
�(n) /Z
dn1,2
✓T
S +N
◆(n1)
�⇠TT (n1, n2)
��(n)
✓T
S +N
◆(n2)
→ Inverse variance filter input maps
→ estimate lensing potential
Filtering
Quadratic Estimator
Power Spectrum Estimation
Filtering
Quadratic Estimator
Data / Sims Data / Sims
Cross-correlation
ϕ Tracer
Filtering
Data / Sims
Filtering
Data / Sims
→ estimate lensing power spectrum.
Planck Lensing Pipeline:
Planck reconstruction noise levels
Planck potential maps are mostly noise. Minimum Variance reconstruction dominated by TT, TE.
S/N = 1
detected at ~50σ.
CIB provides an independent,
high S/N probe of the lensing
potential.
Cross-correlation with the infrared background
Useful for lensing B-modes, CMB delensing…
CIB(@545GHz)⇥ �
Null tests
14
• Must control a variety of large biases and sources of anisotropy.
« Mean fields »: mask, noise, beams… Mostly large scales. Subtracted using MCs.
Spectrum biases
�2
�1
0
1
2
10 100 500 1000
L
TE ⇥ TE(⇥1) TE ⇥ TE(⇥1)
10 100 500 1000
L
EB ⇥ EB(⇥0.15) EB ⇥ EB(⇥0.15)
10 100 500 1000
L
TB ⇥ TB(⇥0.05) TB ⇥ TB(⇥0.05)
10 100 500 1000
L
EE ⇥ EE(⇥0.5) EE ⇥ EE(⇥0.5)�2
�1
0
1
2Half � ring(⇥200)Half � ring(⇥200) Half �mission(⇥100)Half �mission(⇥100) MV ⇥ MV (⇥15) MV ⇥ MV (⇥15) TT ⇥ TT (⇥15) TT ⇥ TT (⇥15)
[L(L
+1)]2C
L/2⇡[⇥
107]
Curl Curl
CurlCurlCurlCurl
Conservative likelihood uses 40 ≤ L ≤ 400
2015 Null tests
TT Curl
Stable to foregroundsPrel
imina
ryCurl feature seen in all relevant frequency channels, and component separation methods.
• First step of the pipeline requires
• So far, Planck lensing filtering always knew about the mask, but used a constant noise matrix N.
Improving the polarisation analysis
(S +N)�1
0
@TQU
1
A
• Planck noise maps have a huge dynamical range. • We are improving our estimators
using noise variance maps.
log
DC ��
L
E= C��
L +N0,L +X
L0
N1,LL0C��L0
| {z }N1,L
C��L = (�LL0 +N1LL0)�1
⇣C ��
L0 �N0L0
⌘
• First internal determination of N1
N1 bias
• So far Planck always subtracted a fiducial N1 bias.
• We are now testing N1 deconvolution:(but 2015 likelihood uses full result and recalculates N1 in parameter space)
Dev
iatio
ns w
.r.t.
FFP9
inpu
t sp
ectru
m
Cur
l spe
ctru
m
N1 deconvolution
• Consistent reconstruction (and curl). • Slight improvements in covariance matrix. • (but no expected gain in S/N)
Prelim
inary
23
- New release of CMB lensing products by the end of year.
- Have interest in a particular product or a variant thereof ?
- New maps and simulations reducing map-level systematics.
- Better characterisation of foreground and contaminations - Investigations of null test failures.
- More optimal weighting of polarization improving S/N; improvements from more optimal estimators.
What to expect from Planck lensing ?
Talk to us !
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