1. Full Spectral Analysis of Galaxies - Are we there yet? Ben Panter, Edinburgh bdp@roe.ac.uk

2. Full Spectral Analysis? Emission lines give information on current star formation, but tell us nothing about the history. Good for redshifts too! Continuum shape can reveal generics of populations, and perhaps a mass to light ratio. Spectral features can help reveal the average metallicity when stars formed, and perhaps their mean age = + +

3. Pixels vs. Parameters Models Observations Comparison Method 3000 pixels Need a clever way to perform this analysis for any non-trivial parameterisation

4. How does MOPED solve this? Short version: Pick the pixels which tell you most about the parameters you wish to extract from the data Create a weighting vector, b, for each parameter which gives a projection y (scalar) of the dataset x. y m =b m.x If model is good, then these y numbers encapsulate all the information about the parameters – the compression is lossless Can use these y values to determine likelihood of a parameter set, rather than x – hence computation time  n params not n pixels Long version: Heavens et al. 2000, Reichardt et al. 2001, Panter 2005. Massively Optimized Parameter Estimation and Data Compression

5. MOPED2 (Mathis 06) STARLIGHT (Cid Fernandez 05) BLV (Nolan 06)MOPED (Panter 03) VEPSA (Tojeiro 07)STECMAP (Ocvirk 03) Not the only show in town…

6. So why bother? z High redshift sources Limited to bright galaxies Subject to IMF (high end) Dependent on models Uncertain dust correction Low redshift sources Subject to IMF (low end) Dependence on models Dust correction easier to manage Many, many more galaxies... z

7. Cosmic SFR to z~2 from z~0.1 sources (DR3)

8. Downsizing – Not Offset!

9. Downsizing

10. Build up of Stellar Mass

11. Metallicity History Maps Preliminary result, please do not redistribute

12. So are we there yet? Lots of results that reassuringly agree with other indicators Lots of information still to be untangled…

13. Looking at the models Missing Ingredients: Alpha enhancement –Models available Variable abundances –Infancy UV Continuum IR Continuum Need to separate… Models Observations Comparison Method

14. How can we differentiate features? Two groups of high signal to noise galaxies: –Telescope features fixed in observed frame, Modelling features fixed in rest frame. –Very tight redshift range for clear observed frame (W) –Wider range of redshift to smooth observed frame (T) –Subtract residuals of W from T

15. Where do the models fail? Average Tight z Wide z T - W

16. Incorporating Alpha Panter / Coelho in prep. Preliminary result, please do not redistribute

17. How different are the models?

18. Modelling all the way - UV to IR Nolan et al. 06

19. Future Surveys – what is needed? Models Observations Comparison Method S/N in sloan is good, but other problems appear to dominate –Spectrophotometry Redshift range –Moderately higher redshift helps a lot Database methods essential –GAVO

20. 2SLAQ/DEEP2/GDDS/GOODS... Higher redshift allows us to probe early epoch populations High z SF from mid-z sources Present data does not support reliable analysis (?)

21. Wish list for higher z FSA Resolution of ~ 1A UV + Visible + IR with overlap SDSS or better S/N 10,000 galaxy spectra z ~ 1 Accurate calibration of spectrophotometry –Better than sloan?

22. Conclusions Methods are in place and tested Models need –Wider range at high res (observations!) –  / Var. Abundances –Uniform format Data needed –Moderately higher z –Spectrophotometry (more important than numbers?) Models Observations Comparison Method