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Galaxy Color Matching in Catalogs

Galaxy Color Matching in Catalogs. Bryce Kalmbach University of Washington. What are we doing?. Finding best fit model SEDs for galactic catalog objects Need SEDs to provide observational catalogs Link between cosmological simulations and working science groups. Matching Algorithm.

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Galaxy Color Matching in Catalogs

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  1. Galaxy Color Matchingin Catalogs Bryce Kalmbach University of Washington

  2. What are we doing? • Finding best fit model SEDs for galactic catalog objects • Need SEDs to provide observational catalogs • Link between cosmological simulations and working science groups

  3. Matching Algorithm • Calculate colors for model SEDs we want to match • Use tools in sims_photUtils • Find best least-squares fit across all colors for each catalog object • See readGalfast in sims_photUtils for example

  4. Sample Matching Result

  5. Current SED Models • Bruzual and Charlot (2003) with Chabrier (2003) IMF • 4 different Star Formation Histories: • Burst • Constant • Exp • Instant • Age grid from 1.585 Myr to 12.5 Gyr • Metallicity from .5% to 250% Z_Solar using Padova (1994) isochrones

  6. B & C Model Coverage

  7. B & C Model Coverage

  8. B & C Model Coverage

  9. Galacticus Catalog • Currently working with galacticuscatalogs • Developed by Andrew Benson (see Benson 2010) • Does not seem to match well with B&C SEDs

  10. Comparing Galacticus

  11. Comparing Galacticus

  12. Comparing Galacticus

  13. Need Better Coverage • Should we get new SEDs? • FSPS (Conroy, Gunn & White 2009)

  14. Comparing with FSPS

  15. Comparing with FSPS

  16. Comparing with FSPS

  17. Need Better Coverage • Should we get new SEDs? • FSPS (Conroy, Gunn & White 2009) • Refine the coverage of our grid?

  18. Changing Grid Coverage

  19. Current Issues • Bluer catalog objects than can currently match to SEDs • Single Star Populations?

  20. Individual Stars 10Myr (J. Dalcanton)

  21. Current Issues • Bluer catalog objects than can currently match to SEDs • Single Star Populations? • Need more statistics from galaxy catalog • Will be provided in next run

  22. Future Work • PCA (Principal Component Analysis) • Determine axes of maximum variance and use these as new basis vectors • Reduce Dimensionality • Storage Savings

  23. Capture Information in Few Components

  24. Capture Information in Few Components

  25. Capture Information in Few Components

  26. 99.8% Information in 10 Principal Components…but…

  27. Now 99.99999%, unfortunately with 2x components, but good color match

  28. Future Work • PCA (Principal Component Analysis) • Determine axes of maximum variance and use these as new basis functions • Reduce Dimensionality • Storage Savings • Challenge: What is the minimum number of components we can get the maximum amount of accuracy from?

  29. Future Work • PCA (Principal Component Analysis) • Continuous coverage of sample space rather than grid

  30. PCA will provide continuum

  31. Future Work • PCA (Principal Component Analysis) • Continuous coverage of sample space rather than grid • Challenge: How do we sample to get the best set of eigenspectra? • Challenge: How do we find the eigenvalues that generate an SED that best matches the object color? • Will need new methods that can combine linear combinations of eigenspectra

  32. Thank You!Contact: brycek@uw.edu

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