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The Evolution of the Luminosity Function in Semi-Analytic models

The Evolution of the Luminosity Function in Semi-Analytic models. Bruno Henriques. Simon White, Gerard Lemson, Peter Thomas, Roderick Overzier, Qi Guo, Claudia Maraston. Henriques, Maraston, Monaco, et al., Astro-ph: 1009.1392. Henriques, et al., 2011, in prep. Guo et al. 2010. Stars.

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The Evolution of the Luminosity Function in Semi-Analytic models

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  1. The Evolution of the Luminosity Function in Semi-Analytic models Bruno Henriques Simon White, Gerard Lemson, Peter Thomas, Roderick Overzier, Qi Guo, Claudia Maraston Henriques, Maraston, Monaco, et al., Astro-ph: 1009.1392 Henriques, et al., 2011, in prep.

  2. Guo et al. 2010 Stars Reincorporation Hot Gas Reheating Cooling Stars Ejected Gas Star Formation Recycling Cold Gas Ejection

  3. Supernovae Feedback Cold Gas Reheating Energy Released by a Supernovae Gas Reincorporation

  4. Mergers 0 0 1 2 0 0 2 1 1 2 0 2 3 2 1 – Two isolated Galaxies 2 – Gas Striping and Disruption on type 1 3 – Merger Clock and Stellar Disruption triggered on type 2, no gas left

  5. Mass to Light Stellar Population Synthesis Knowing: Metallicity + Age +IMF Luminosity of a given mass of stars Dust Model ISM + Birth Clouds

  6. The semi-analytic is built on top of the dark matter distribution and has outputs only at given snapshots. (despite galaxy properties being computed in smaller steps ~ 6 Myr ) z=5.7 z=1.4 From snapshot/box output z=0 to lightcones

  7. Full Emission Spectra Direct comparison to observed frame apparent magnitudes Test SED fitting / K-corrections Reliability of assumed star formation histories Test determinations of mass

  8. Different Stellar Populations Test the impact of stellar populations modelling in the observed galaxy properties. In past evolutionary population synthesis codes, the K-band was mostly determined by old populations (e.g. Bruzual & Charlot 2003, PEGASE, Starburst99) i z The inclusion of the TP-AGB phase means that intermediate-age populations will contribute significantly to the near infra-red emission from galaxies (Marasto 2005, Charlot & Bruzual 2007) J K (see Henriques et al. (2010), Tonini et al. (2008, 2009), Fontanot & Monaco 2010)

  9. 10x 5x 0.5 1.0

  10. M05 CB07 BC03 Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392)

  11. Redshift Distribution K-band selected Galaxies Guo10 + M05 Guo10 + BC03 From previous plot: z<1.0 - two stellar populations are identical z=1.5 - bright galaxies are brighter fro M05 z>2.0 - all galaxies are brighter for M05 0.74 0.55 1.11

  12. Redshift Distribution 8.0μm-band selected Galaxies Around z=2.0 the observed 8.0μm starts receiving light from the “JHK region” TP-AGB stars significantly increase emission J K 4.0 2.0 2.67

  13. TP-AGB stars re-emission by dust z>0.75 z<0.75

  14. Number Counts in Redshift Intervals K-band At low z galaxies are dominated by intermediate to old stellar populations – M05 and BC03 converge. At high z the observed K-band receives flux from rest-frame optical where M05 and BC03 also agree. At low z (rest-frame 8.0μm), BC03 gives brighter galaxies than M05. 8.0μm At high z (rest-frame K-band), the emission from TP-AGB stars means that M05 gives higher number counts

  15. The Ages of Galaxies Light – Weighted Ages Mass – Weighted Ages BC03 M05 Average!!! M05 TP-AGB Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392)

  16. Summary Light cones will soon be available at: http://gavo.mpa-garching.mpg.de/MyMillennium3/ Wide range of filters and multiple stellar populations for observed frame magnitudes + self-consistent model of galaxy evolution. The model matches the mass and luminosity evolution of galaxies over the cosmic history reasonably well. Understand the interplay of different physics at different epochs Test Evolutionary Populations Synthesis models, SED fitting, K-corrections, mass determinations.

  17. 10x 5x 0.5 1.0

  18. Van der Wel, Franx, Wuyts, et al. 2006 Chandra Deep Field - South ACS+IRAC+J&H filters

  19. Maraston, Daddi, Renzini, et al. 2006 Older then the Universe! Undetected in MIPS! What are the implications for galaxy formation models?

  20. The Stellar Mass Function – Marchesini et al. 2009 Optical to mid-infrared data Goods – Giavalisco et al. 2004 Musyc – Gawiser et al. 2006

  21. K-band Luminosity Function The contours follow the MCMC sampling in parameter space The colours represent the maximum likelihood projected along the hidden dimensions Less gas available to form stars in dwarfs Higher ejection Lower reincorporation Lower virial velocity cut off Constant amount of cold gas available

  22. Bulge – Black Hole Mass The maximum likelihood region is incompatible with previous test There is a region with lower likelihood that is bigger, so more likely in a Bayesian sense 17 August, 2014

  23. 3 - Tidal Disruption

  24. Original De Lucia 2007 with Disruption K-band Luminocity Function Fraction of Red Galaxies ICL fraction Metallicity

  25. Likelihood Distribution Model Likelihood from 0.037 to 0.15 !!!

  26. Predictions for the Best Fit with Disruption K-band LF Metallicity ICL

  27. Predictions for the Best Fit with Disruption Best Fit Model with Disruption Observations

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