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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,

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Presentation on theme: "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,"— Presentation transcript:

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

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

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

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: For colors: hi 8 8

10 30-m vs. 100-m: Analytical results Magnitudes at which 10% photometry is possible in regions of surface brightness  V =22,  K =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 1.Finite number of guide stars and DMs 2.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 x 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


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