1. Surveying the Galaxy: classical methods applied to topical science and the role of the ING Gerry Gilmore Institute of Astronomy Cambridge University

2. Our requirement is really very simple

3. Stellar populations: 4.5 `types’ Kinematically cold, high-J (angular momentum), wide age- range, narrow abundance range - dG-`problem’  late gas accretion? -- thin disk POP I Probably discrete? intermediate PopII, thick disk I.5 Hot, low-J, old?, metal-rich, related to SMBH – bulge Hot, low-J(???), old(??) metal-poor(?), late accretion(?), perhaps itself 2-component – halo classical POPII The first early stars POPIII? +POPIV +.... PLUS, most important of all?: All these in an evolving dark-matter potential: POP 0? Justification is observed relation to physics What stars we have available to study

4. Complexity, richness All these “populations” means complexity,  large samples  no single “answer”  no single approach is sufficient  many types of survey are needed: Imaging, kinematic, spectroscopic (various R), and Gaia. R~5000 most science, most critical, cf other talks

5. Stellar orbit-abundance correlation: yellow band is ELS relation A simple example of the power of large samples, with understood biasses

6.      8,600 faint F/G dwarfs, several kpc above the plane, spectroscopic metallicities from AAOmega/AAT data [Fe/H] (g-r) o Wyse, Gilmore, Norris 2008: Thick disk is OLD Old stars here Young stars would be here 12Gyr turn-off points Power of huge samples, low dispersion

7. Chemical evolution models allow x100 element ratio scatter – not seen The scatter is 2-3 orders less than the range. This implies efficient large-scale mixing, or uniform sub-units Element ratio data from Fuhrmann, see also Nissen. Local volume-complete sample The OUTER halo is only 10% of the halo, perhaps 1% of the stellar Galaxy MOST of the halo POPII is old, and has very clearly defined chemical element ratios Not at all like the surviving dSph

8. Renzini 2008 Current data show the power of chemistry to measure `history’, fig from Renzini Standard IMF (#1) Well-mixed (#2) Fast recycling (#3)

9. Carretta etal 2009 A&A 505 117 There are very many complications, which need lots of data to “understand” – eg, why do giants show different element patterns/ratios in star clusters than in the field?? Yet again, this is suggesting our survivor structures are not the important building blocks. Why not??

10. Plateau  universal IMF Plateau  efficient mixing Sharp break  narrow time Small scatter  good mixing.......... Lots and lots of physics Blue=outer halo Red = inner halo How much of that scatter is real?? How were those stars selected for analysis?

11. Nissen & Schuster 2010 : 1002.4514 Our current samples are small, and very biased: there is obviously vastly more to learn before we are sure we are even asking the right questions. Majority of data sets suggest remarkably small scatter in low-abundance stars BUT: one recent survey provides a quite different result – two low-scatter relations And much increased richness/complexity for [m/h]-1

12. The thick disk/halo state of the art: many more metal-poor stars at bright magnitudes, high angular momentum extends to low metallicity (pace ELS). But standard IMF (#1) Well-mixed (#2) Fast recycling (#3) Ruchti, Fulbright, Wyse, GG, in prep, based on RAVE survey

13. What role does ING have to play in these big challenges? Very many exciting science cases are presented here. Very interesting, but “all of the above” and “people want us” is not an intelligent reaction. One needs to look at sharing, coordinating, off-loading SDSS and RAVE are an excellent example of a practical way forward: Focussed use of a cheap facility (UKST) to do a very few things very well, while focussing on big science questions. The Gaia-related follow-up projects are a superb example of optimal science use of a wide-field 4m telescope, with data serving a huge range of science and a huge science community. Coordination inside the GREAT initiative (coordinated by Nic Walton and the GAIA project DPAC community) is an obvious way forward.  Scale of people, scale of science, focus of technical demands.  And this is highly likely to be a non-negotiable requirement for continuing financial support

14. What role does ING have to play in these big challenges? Very many exciting science cases are presented here. Attempting all (or even many) of them is foolish, and will fail We are an age of big statistics We have many small telescopes: all of them doing everything is stupid 2-4m telescopes are like clever students:  doing a very small number of things well is a smart future Coordinated major Gaia-related science is smart, to be somewhere. There is a wide-field spectroscopic AstroNet review panel underway  Likely outcome: share the 3 top priorities around the best facilities,  Each doing 1-2 things only, and optimally Make a clear choice, then commit to do it well.

16. dSphs vs. halo abundances Shetrone et al. (2001, 2003): 5 dSphs Letarte (2007): Fornax Sadakane et al. (2004): Ursa Minor Koch et al. (2007, 2008, 2009): Carina Monaco et al. (2005): Sagittarius Koch et al. (2008): Hercules Shetrone et al. (2008): Leo II Aoki et al. (2009): Sextans Frebel et al. (2009): Coma Ber, Ursa Major Tolstoy et al. (2009): Sculptor Cohen & Huang (2009): Draco Feltzing et al. (2009): Boo I Frebel et al. (in prep.): Sculptor

17. Looking again near the Sun: the RAVE survey –UKST – 1million bright stars, to see what is really there. Kinematic `populations’ and chemistry follow-up