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SDSS photo-z with model templates

SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

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Page 1: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

SDSS photo-z with model templates

Page 2: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Photo-z

• Estimate redshift (+ physical parameters) – Colors are special „projection” of spectra, like

PCA

Page 3: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

LIGHT Spectrum1M objects

BROADBAND FILTERS

MAGNITUDE SPACE270M objects

REDSHIFT

PHYSICAL PARAMETRSage, dust, SFH, etc.

GALAXYearly, late

3000 DIMENSIONALPOINT DATA 5 DIMENSIONAL

POINT DATA

5-10? DIMENSION 3-10 DIMENSION PCA

Page 4: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Photo-z techniques• Empirical

– Polyfit– Neural net– Nearest neighbor

• Tempate fitting– Empirical templates

• Repair – Model templates

• All the same: – generate a reference set (from observed photometry, synthetic

photometry of observed or model spectra) – Linear (weighted sum) or nonlinear function of neighbors’ redshift

• The key issue: get a good reference set– Easy to get good results for a good reference set– Extrapolation: only hope is better models

Page 5: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Catalogs

• Test set: DR6 spectro set , 666697 galaxies (few outliers removed)

• Charlot et al.: 100k stochastic SFH model library – u,g,r,i,z synthetic magnitudes, 200 redshift

bins in z=0-1

• Using colors only for redshift estimation

Page 6: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Fast kd-tree based NN in SQL server

• Index the color space with a search tree– Find k-nearest neighbors quickly

• Implemented in SQL server (SQL+CLR)– Local polyfit, average, weighted

(photo errors) sum • Time to calculate photoz for

DR5 (200M object)– Tempate fitting: 150 processor-

day– Kd-fit: 10 processor-day

Page 7: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Spectro training set

• Local linear fit 150 NN• Δz=0.0294

• Average 150 NN• Δz=0.0306

Page 8: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

100k Stochastic library

• Average 150 NN• Δz=0.1429

• Local linear fit 150 NN• Δz=0.2275

Page 9: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Best subset

• Conclusion: too rich reference set with probably un-physical templates

• Subset 100 of 100k– Iteration

• Closest templates in color+redshift space• Removing templates those cause systematic

errors

Page 10: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Best subset: 1st step

• Average of 150 NN

• Δz=0.05477

Page 11: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Best subset: final iteration

Average of 150 NNDz = 0.0467

Local linear fit of 150 NNDz = 0.1937

Page 12: SDSS photo-z with model templates. Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA

Open questions

• Cannot reach as good estimation as with the empirical reference set – Are the templates in the best set „physical”?

• Systematic calibration mismatch– SDSS filter curves?– Model: dust ?

• Different sampling – How to compare the sets and find the „offset” in colors– Correcting for the average difference vector

• Δ(ug,gr,ri,iz)= (-0.0649,0.0036,-0.0109,0.0212)• does not improve the results

• Find optimal subset with matching models and observations in spectral space (Laszlo)