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Published byTheresa Howard Modified over 9 years ago
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SDSS photo-z with model templates
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Photo-z Estimate redshift (+ physical parameters) –Colors are special „projection” of spectra, like PCA
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LIGHT Spectrum 1M objects BROADBAND FILTERS MAGNITUDE SPACE 270M objects REDSHIFT PHYSICAL PARAMETRS age, dust, SFH, etc. GALAXY early, late 3000 DIMENSIONAL POINT DATA 5 DIMENSIONAL POINT DATA 5-10? DIMENSION 3-10 DIMENSION PCA
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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
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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
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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
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Spectro training set Local linear fit 150 NN Δz=0.0294 Average 150 NN Δz=0.0306
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100k Stochastic library Average 150 NN Δz=0.1429 Local linear fit 150 NN Δz=0.2275
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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
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Best subset: 1st step Average of 150 NN Δz =0.05477
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Best subset: final iteration Average of 150 NN Dz = 0.0467 Local linear fit of 150 NN Dz = 0.1937
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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)
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