Knut Olsen, Brent Ellerbroek, and Steve Strom Presentation to GSMT SWG, October 20, 2005

1. Chronicling the Histories of Galaxies at Distances of 1 to 20 Mpc: Simulated Performance of 20-m, 30-m, 50-m, and 100-m Telescopes Knut Olsen, Brent Ellerbroek, and Steve Strom Presentation to GSMT SWG, October 20, 2005

2. Context Hierarchical structure formation: Primordial fluctuations + CDM +  collapse of DM halos, starting with the smallest • Explains: • Large-scale structure (e.g. White & Rees 1978) • The morphologies of galaxies (e.g. Kauffmann et al. 1993; Steinmetz & Navarro 2002) • The globular cluster systems of elliptical galaxies and of the Milky Way halo (Beasley et al. 2002; Searle & Zinn 1978) Mathis et al. (2002)

3. 320 kpc 40 kpc Dark matter Gas Stars Galaxy formation physics Abadi et al. (2003) • Gas cooling • Star formation • Feedback • Merging

4. The angular momentum problem of disk galaxy formation • Early gas cooling in simulations leads to compact gas disks inside dark matter halos • Every merger has the opportunity to transfer L outwards, so that baryons lose L to dark matter Abadi et al. (2003)

5. The importance of star formation and feedback • Feedback inhibits rapid collapse of gas • Feedback regulates star formation Robertson et al. (2004)

6. ELT Stellar Populations Science • Near IR photometry of resolved stars in nearby galaxies provides a way to extract their entire star formation histories • Crowding, and hence aperture size, is the limiting factor • Spectroscopy of individual stars supplements the photometric data with more accurate chemical abundance measurements • Sensitivity and crowding can both be limiting factors M31 observed with Gemini N+NIRI/Altair (Olsen et al., in prep.)

7. AO-corrected 8-m performance Stellar Evolution in a Composite Population: M31 Model with constant star formation rate and stepwise increasing metallicity Girardi et al. (2000) tracks

8. Modeling crowding effects Crowding introduces photometric error through luminosity fluctuations within a single resolution element of the telescope due to the unresolved stellar sources in that element. I V

9. To calculate the effects of crowding on magnitudes and colors, we need only consider the Poisson statistics of the luminosity functions (e.g. Tonry & Schneider 1988) For magnitudes: hi For colors: 8 8

10. 30-m vs. 100-m: Analytical results Magnitudes at which 10% photometry is possible in regions of surface brightness SV=22, SK=19 for galaxies at the indicated distances.

11. Issues Photometric Issues: Spatial variability of PSF Time variability of PSF Absolute calibration Scientific Issues: Sample size needed Field size needed Filters needed

12. PSF Simulation • AO Error sources included • Finite number of guide stars and DMs • Finite spatial resolution of wavefront sensors and DMs • Sampled on 49x49 20” wide grid in IJHK for 20-m, 30-m, 50-m, and 100-m telescopes • Sampled over 12-minute average intervals from hour-long “typical” observation with TMT MASS/DIMM • 5 atmospheric profiles  4 filters  49 (10) positions  4 telescopes = 3920 (800) PSFs

13. 30-m J PSF grid, profile 1 PSF Simulation Courtesy of Richard Clare

14. 20-m to 100-m: Simulated scenes (in progress) • M31 Bulge • M31 Disk • NGC 3379 effective radius • NGC 3379 3x effective radius

15. Simulation procedure • Select appropriate population mix • Pick stars from stellar isochrones and place in image, making sure to simulate stars well below crowding limit • Convolve image with PSFs (495 convolutions, combine through weighted average) • Add sky background and noise • Perform PSF-fitting photometry • Correct photometry for Strehl ratio using profile 1 or average of profiles 1 and 5 • Derive best-fit population mix

16. Coming results • Demonstrate ability of suite of ELTs to measure the formation epoch of disks vs. bulges vs. ellipticals • Show effect of likely calibration errors on end results • Quantify observing strategies • Recommend instrument FOV, filters, and necessary sample sizes