Catastrophic errors of photo-z: biasing dark energy parameter estimates with cosmic shear
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Transcript of Catastrophic errors of photo-z: biasing dark energy parameter estimates with cosmic shear
Catastrophic errors of photo-z: biasing Catastrophic errors of photo-z: biasing dark energy parameter estimates with dark energy parameter estimates with
cosmic shearcosmic shear
Sun Lei (Sun Lei ( 孙磊孙磊 ))
Peking UniversityPeking University
Collaborators: Z.-H. Fan, C. Tao, J.-P. Kneib, S. Jouvel, A. TilquinCollaborators: Z.-H. Fan, C. Tao, J.-P. Kneib, S. Jouvel, A. Tilquin
Cosmic shear and the systematicsCosmic shear and the systematics
Knox, Song, & Zhan (06)Hu Zhan (06)
Huterer et al. (06)
powerful !
measure: DA(z) & G(z)
bias-free
But systematics control is crucial !!
Catastrophic errors Catastrophic errors
Catastrophic errorCatastrophic error
by A.Conollyby S.Jouvel
LSST without u band SNAP standard filters
Causes: e.g. Lyman break(~1000 A) & Balmer break (~4000 A) confused
Catastrophic errorCatastrophic error
z_spec
z_ph
ot
Loosely defined: e.g. | z_p - z_s | > 1
Brodwin et al.03 (CFDF) Gavazzi & Soucail 06 (CFHT)
Catastrophic errors seen in a realistic galaxy z-distribution n(z)Catastrophic errors seen in a realistic galaxy z-distribution n(z)
How do the lensing signals depend on the source galaxy How do the lensing signals depend on the source galaxy distribution n(z)distribution n(z)
n(z)
bias !
Ref
regi
er 2
003
z
n(z)
SNAP : a space-based survey
survey geometry
area: 1000 deg²
number densigy: 100 gal/arcmin²
depth: zmed = 1.26
with = 2, =1.5
fidicual n(z) of galaxy:
Employ 5 tomographic z bins:
Its weak lensing design:
Lensing tomography: how many redshift bins to Lensing tomography: how many redshift bins to use?use?
wa
w0
fcata=1 % at z_spec ~ 0.4 z_phot ~ 3.5
with zm = 0.4, = 0.1, Acata determined by fcata.
SNAP Photo-z simulation results: with its standard 9 filter set
To characterize true n(z) of :
To estimate the bias on cosmological paramters:
Extension of Fisher matrix:
Chi-square fitting analysis:
C
for signal ‘S’
for model ‘M’
n(z)
SM‘Bin-0’:
Assume a 7-param fiducial model [m, w0, wa, 8, h, b, n], with a Gaussian priors (pi)=0.05 applied on all hidden params except (b)=0.01.
² fitting: Fisher matrix approximation:
Biases on dark energy equation of state (w0, wa):
fiducial values
biased values
To fight against catastrophic failure: spectroscopic calibrationTo fight against catastrophic failure: spectroscopic calibration
Sampling N spectra out of our simulated 1300 galaxies whose photo-zs fall in z_phot = [3, 4]
To fight against catastrophic failure: spectroscopic calibrationTo fight against catastrophic failure: spectroscopic calibration
'
(one realization of calibration)
there is residual fcata = - ' , so still bias the parameter estimate!
If Nspec is not enough :
for signal ‘S’
for model ‘M’
S
M
w0 w0
wa
To fight against catastrophic failure: spectroscopic calibrationTo fight against catastrophic failure: spectroscopic calibration
Sampling 100 spectra (with 100 realizations)
5 z-bins with all Cij: 5 z-bins with auto Cii only:
Scatter of bias is large: significant compared to statistical errors
Notable descrepancy between results of fit / Fisher when residual (f – f ‘) is large
w0 w0
wa
To fight against catastrophic failure: spectroscopic calibrationTo fight against catastrophic failure: spectroscopic calibration
Sampling 500 spectra (with 100 realizations)
5 z-bins with all Cij: 5 z-bins with auto Cii only:
Scatter of bias is small: getting insignificant
Descrepancy between fit / Fisher is vanishing since residual (f – f ‘) keeps small
To fight against catastrophic failure: spectroscopic calibrationTo fight against catastrophic failure: spectroscopic calibration
How many spectra is sufficient ?
A calibration size of 500-600 spetra at z ~ [3,4] is necessary
Might not be easy at such high-z but hopeful
(w0-wa)
2(w0-wa)
To fight against catastrophic failure: other methods To fight against catastrophic failure: other methods
fcata: ~1% ~ 0.1 %
But technical difficulty exists…
including u band :
To fight against catastrophic failure: other methodsTo fight against catastrophic failure: other methods
consider original 3 z-bins left re-define 5 narrower z-bins
But with notable statistical loss…
Cutting out galaxies at z < 0.5 & z>2.5 :
• Catastrophic error is frequently seen in photo-z catalogs and is an important source biasing the galaxy z-distribution.
• The bias induced by catastrophic errors on DE parameter estimate from cosmic shear:
SNAP with std-type filters: ~1 % fcata significant compared to statistical error in tomography 5-z
bins case
• To resist the bias by catastrophic errors: * spectroscopic calibration useful, needs a relatively large sample at high-z * Including u band useful, may be not easy for space-based telescope e.g. SNAPu : ~ 0.1% fcata bias much smaller than statistical error * Cutting out galaxies with suspicious z useful, with a price paid for
statistical loss
summarysummary