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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
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
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
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
Spectro training set
• Local linear fit 150 NN• Δz=0.0294
• Average 150 NN• Δz=0.0306
100k Stochastic library
• Average 150 NN• Δz=0.1429
• Local linear fit 150 NN• Δz=0.2275
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
Best subset: 1st step
• Average of 150 NN
• Δz=0.05477
Best subset: final iteration
Average of 150 NNDz = 0.0467
Local linear fit of 150 NNDz = 0.1937
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)