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Stellar Cosmology and Virtual Observatory
Joss Bland-Hawthorn
Anglo-Australian Observatory
CDM is a successful but incomplete prescription which breaks down
in the non-linear regime. Present effort is to insert complex physics
which will always need to be validated by observation.
2005-2015: multi-million stellar surveys at moderate to high
resolution — these will require a radically new approach to
data analysis.
Ho = 68
disk
halo
Goals of near-field cosmology
To tag or associate individual stars with parts of the
protocloud. Dynamically this is impossible in general although some integrals of the motion may be
preserved, others will be scrambled by dissipation,
torques, violent relaxation, scattering.
Kinematic tagging is proven for unravelling halo
Chemical tagging is essential to unravelling disk,
i.e. only way to unravel dissipation
Analogy to CMB and problem of seeing beyond the surface
of last scattering. For the disk, we can only recover
dynamical properties back to the epoch of last dissipation
or last scattering.
Near-field cosmology:
future surveys
UKST Echidna = Ukidna:
up to 50 million stars at R=10,000
Gemini Wide-Field = KAOS:
up to 1 million stars at R=20-40,000
GAIA: up to 300 million stars at R=10,000
sphere
10 as = 10% distances at 10 kpc
10 as/yr = 1 km/sec at 20 kpc
Comprehensive inventory of surviving inhomogeneities
requires vast catalog of stellar properties - kinematics,
ages, abundances - that is now out of reach
In the next decade, we expect GAIA to give phase
space data for about a billion stars within 10 kpc
(the Gaiasphere). Comparable samples of stellar ages
and abundances can be contemplated with multi-object
high resolution spectrometers on large telescopes technically possible.
Detail will be important - a small example of what we might
expect is the discovery of the Sgr dwarf from a survey
of stellar velocities in the galactic bulge: Sgr was
discovered only as a velocity inhomogeneity
Sgr dwarf
L ~ 108 Lo
Ibata, Irwin & Gilmore 1995 ff
M31
Ibata et al. (2001)
L < 108 Lo
Zaritsky & Rix (1997) from
m=1 photometry conclude
rate of infall is
0.07 - 0.25 dwarf mass / Ga
Shang et al (1998)
Low surface brightness
structures in galaxies
NGC 4594
M83
Tidal Streams in the Galactic Halo
(simulation of accretion of 100 satellite galaxies)
x (kpc)
RGC (kpc)
The Spaghetti Survey (Morrison et al)
Washington system; halo stars out to 100 kpc over 100 deg2
Chemical Tagging
All galactic components show abundance dispersion
The goal of tagging is to associate stars with elements of the protocloud.
May be possible kinematically with some stars of halo and thick disk.
For the thin disk, much information was lost by dissipation and dynamical
evolution, so kinematical tagging can only be partly successful.
Consider chemical tagging: see a large dispersion in element ratios for
(neutron capture elements)/Fe in stars of lower metallicity. °
Some elements show up to ~ 3 dex dispersion (Sr, Ba, Eu)
Light s
Heavy s
r
Scatter in element
ratios at lower
[Fe/H]
 elements have less
scatter; Mg,Ti,Al not
rigidly coupled to Si,Ca
Wallerstein et al 1997
Previous record:
CD -38 245
[Fe/H] = -4.0
Now shattered:
HE0107
[Fe/H] = -5.3
1
TDO
0
[Fe/H]
-1
B
TDG
-2
YHG
-3
OHG
0
7
Age (Gyr)
14
Signatures of Galaxy Formation
We seek signatures or fossils from the epoch of Galaxy
formation, to give insight into processes that took place
as the Galaxy formed.
These signatures survived the loss of information that
occurred during galaxy formation
Long-term goal: chemical clustering in chemical abundance space, Z
Short-term goal: chemical trajectories in chemical abundance space, Z
Long term goal: reconstructing ancient star clusters
Ideally, what we would like to know is a time and
a place for the birth of all stars.
A starting point is to attempt to reconstruct
ancient star clusters from their abundances...
Primary requirements:
almost all stars born in large clusters

certain elements must reflect progenitor
cloud abundances, i.e. immune to stellar evol. 
most stars within a cluster must share
identical abundances in a set of elements
?
some elements must not be rigidly coupled
?
How many distinct enrichment sites might there
have been for the thick disk ?
• dissipative collapse
– 1010 Mo protocloud; Larson 1976; Jones & Wyse 1983
• gradual heating
– 109 Mo protoclouds; Noguchi 1998
• rapid heating
– “conventional” disk of 103 Mo star clusters heated by merger
– 106 Mo star clusters; Kroupa 2002
– 106 Mo protoGCs; Jehin et al 1999
• merger scenarios (e.g. a few infalling systems)
– Quinn et al 93; Huang & Carlberg 1997; Sellwood et al 1998
– new 2dF observations (Gilmore, Wyse & Norris 2002) show evidence of a
minor merger event
Establishing millions of independent chemical signatures may
appear out of reach
However ... more than 60 of the elements arise from
neutron capture processes.
Say only 30 of these elements were detectable
and we could measure
only two distinct abundance levels for each.
That would already give more than 109 independent cells in
the chemical abundance space
(if many of the element abundances are found not to be independent,
then the number of independent cells is reduced)
Short term goal: Chemical trajectories
Z( Fe/H, 1/Fe, 2/Fe, s/Fe, r1/Fe, r2/Fe, ... )
Each generation of SN gives stellar population with progressive
enrichment. Stars lie along a trajectory in an n-dimensional
space, where we are looking at n elements including isotopes.
Trajectories affected by basic processes like star formation
efficiency and timescale, mixing efficiency and its timescale
and lengthscale, infall rate ...
As [Fe/H] –> 0, trajectories converge and power of method is
reduced. The chemical n-space will contain a lot of information
on the chemical evolution history. May be able to detect evolution
of the cluster mass function, and epochs of satellite infall and
star-bursts.
Chemical tagging
Z( Fe/H, 1/Fe, 2/Fe, s/Fe, r1/Fe, r2/Fe, ... )
• Need 10 elements measurable to 0.1 dex or better (which
do not vary in lock-step) at R~20,000 to V=16,18,20
• Relative abundance accuracy at R~25,000, SNR~100,
[Fe/H] to <0.05 dex (Edvardsson), 0.02 dex (Jehin)
• This may greatly improve with 3D atmospheric models,
huge database, self calibration!
Challenge 1: to achieve 0.1 dex for , r elements
Challenge 2: to find 600A window of 3-6 lines per element
A new era of precision measurement
Self calibration via huge stellar surveys:
more stars  finer cells  better differential abundances
Log (g/go)  log(L/Lo) + log(M/Mo) + 4 log(T/To)
Missing dimensions: microturbulence, hyperfine splitting...
GAIA window 8400-8800A
 Many Fe lines,  elements
If we go after this window,
the same grating could pass
4200-4400A in higher
order — expensive option!
 Many r, s elements
Note: you could certainly
invent a `genesis machine’,
but probably need dual beam
R ~ 40,000 at U,B and
R ~ 20,000 in red
[Fe/H] = -0.5
Important (, r) windows
5490: MgI(1), SiI(3), CaI(4), ScII(1), TiI(2), TiII(2),
FeI(18), FeII(2), YII(1), NdII(3), MnI(5)
6270: OI(1), NaI(2), CaI(5), ScII(1), TiI(4), VI(9), CrI(2),
FeI(20), FeII(2), NiI(4), YII(1), LaII(2), CeII(1)
6520: CaI(7), ScII(1), TiI(3), TiII(3), VI(2), FeI(19),
FeII(4), NiI(3), YI(1), BaII(1), LaII(1)
6780: LiI(1), AlI(2), CaI(1), TiI(1), TiII(1), VI(1), FeI(9),
NiI(4), YII(2), LaII(1), NdII(1), Eu(1)
All windows 200A wide, R=20,000, optimally 3-6 lines
per element
Powerful methods required to:
Find clustering in abundance space, Z
Find trajectories in abundance space, Z
Extract information from echelle data, I()
Compare to stellar atmospheric models, S
Starting point:
Consider spectrum I() as 1-D array of
intensities in 2048 contiguous energy bins
 coordinate in 2048-D data space
Conventional PCA
such that
var(Y1)  var(Y2)  var(Y3) …
li12 + li22 + … + lip2 = 1, all i
cov(Yi, Yj,) = 0, i  j
Principal Components Analysis
PCA in a curvilinear space
orthogonal
orthonormal (i.e. orthogonality in a
curved geometry)
biT bj = 1 (i = j)
biT bj = 0 (i  j)
biT C bj = 1 (i = j)
biT C bj = 0 (i  j)
C = covariance matrix
(cf. metric tensor in GR)
Need differential form to
emphasize weak features...
VO must provide tools for
n-space analysis
• Will be of general use to any waveband or
comparison of wavebands, or data set cf. to
numerical model, etc.
• These codes will need to be parallelized for
CPU clusters
Epilogue
Understanding galaxy formation is mainly about understanding
baryon dissipation within CDM - this means understanding the
formation of disks.
Can this ever be unravelled, kinematically or chemically ?
Much information has been lost.
We should look for the preserved signatures
In the near field, we have the advantage that individual stars
can be investigated in detail
Maybe one day, we can identify the Solar Family,
i.e. all stars born with the Sun