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# Medium-Band Photometric Redshifts - PowerPoint PPT Presentation

Medium-Band Photometric Redshifts. UCL Meeting 16 Sep 2008. Christian Wolf. flux / qef. 1000 nm. 400 nm. Redshift Errors & Resolution. Objects at different redshifts Filterset  's fixed. z = 0.843. G2 star vs. QSO z=3. z = 1.958. z = 2.828. E. colour. z=0.52. z=0.56. Sb.

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### Medium-Band Photometric Redshifts

UCL Meeting

16 Sep 2008

Christian Wolf

1000 nm

400 nm

Redshift Errors & Resolution

• Objects at different redshifts

• Filterset 's fixed

z = 0.843

G2 star vs.QSO z=3

z = 1.958

z = 2.828

colour

z=0.52

z=0.56

Sb

colour

Z-Bias & Calibration Offsets

• N = number of filters, i.e. independent data points

• Calibration offsets in N=3-D:

• 1-D normalisation

• 1-D z-bias

• 1-D restframe SED bias

• 1 out of N offset dimensions causes a photo-z bias z

• More filters  smaller z (proj. component ~1/√N)

• Narrow filters  small z (larger col/z on feature)

• Spectroscopy with N~102..3: z without flux calibration

• Few-filter photo-z’s limited by calibration accuracy

• Many-filter photo-z’s limited by number and resolution of filters

colour

z=0.52

z=0.56

Sb

colour

Redshift Error Regimes

• Three regimes in photo-z quality

• Saturation

• Model-data calibration offsets in  test causes p(z)-biases

• Transition

• Locally linear colour(z) grid

• Breakdown

• Globally nonlinear colour(z) grid

mag

R=20

R=22

R=23.7

Galaxies at z~0.45

R=22.9

R=23.8

QSOs: Saturation at R<24

rms 0.008

7%-20%outlier

Calibrationoffsetsz biases

Calibrationoffsetsline confusion

QSOs at z~2.8

2-SED Classification, COMBO-17

Classification ~98% complete at R<23

Stars (~3,000)

White Dwarfs (~30)

Ultra-cool WD (1)

Galaxies (~30,000)

QSOs (~300)

New Subject:Empirical 2 estimation?

• 2

• PDF  Ambiguity warning

• NN

• No PDF, no warning

• Template model

• Can be extrapolated in z,mag

• Calibration issues

• Priors’ issues

• Empirical model

• Good priors

• No calibration issues

• Can not be extrapolated

Code 2 NN

Model

Template 

Empirical 

ANN

2 template

2 empirical

Collister & Lahav 2004

~0% outliersz/(1+z)>0.1

rmsz/(1+z) = 0.023

Bias ~0.00

~4%outliersz/(1+z)>0.1

rmsz/(1+z) = 0.042

Bias -0.017

~0% outliersz/(1+z)>0.1

rmsz/(1+z) = 0.020

Bias ~0.00

ANN

2 template

2 empirical

Filipe A.

~12% outliersz/(1+z)>0.3

rmsz/(1+z) = 0.113

Bias ~0.00

~22%outliersz/(1+z)>0.3

rmsz/(1+z) = 0.056

Bias +0.015

~1% outliersz/(1+z)>0.3

rmsz/(1+z) ~ 0.04

Bias ~0.000