Transcript Chronicling the Histories of Galaxies at Distances of 1 to

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
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)
Galaxy formation physics
Abadi et al. (2003)
320 kpc
•Gas cooling
•Star formation
•Feedback
•Merging
40 kpc
Dark matter
Gas
Stars
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)
The importance of star
formation and feedback
•Feedback inhibits rapid
collapse of gas
•Feedback regulates star
formation
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Robertson et al. (2004)
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.)
Stellar Evolution in a Composite Population:
M31
Model with constant star
formation rate and
stepwise increasing
metallicity
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AO-corrected 8-m
performance
Girardi et al. (2000) tracks
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.
V
I
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
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.
Issues
Photometric Issues:
Spatial variability of PSF
Time variability of PSF
Absolute calibration
Scientific Issues:
Sample size needed
Field size needed
Filters needed
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, 30m, 50-m, and 100-m telescopes
•
Sampled over 12-minute average intervals from hourlong “typical” observation with TMT MASS/DIMM
•
5 atmospheric profiles  4 filters  49 (10) positions 
4 telescopes = 3920 (800) PSFs
PSF Simulation
Courtesy of Richard Clare
30-m J PSF grid, profile 1
20-m to 100-m: Simulated
scenes (in progress)
•M31 Bulge
•M31 Disk
•NGC 3379 effective radius
•NGC 3379 3x effective radius
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
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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