Transcript An Overview of the Gaia

The Gaia-ESO Survey
C. Allende Prieto
Instituto de Astrofísica de Canarias
NGC 7331
IR Spitzer
Smith et al. (2004); image courtesy NASA/JPL – Caltech/STScI
LMU, October 8, 2007
The Milky Way
blue: 12 m
green: 60 m
red: 100 m
LMU, October 8, 2007
IRAS – ipac/CalTech
Formation of the Milky Way
• Cold dark matter simulations predict a
bottom-up scenario for galaxy formation.
• There is secular evolution as well.
• Galaxies evolved chemically, under the
right conditions, since each generation of
stars progressively enriches the gas.
Galaxy assembly
• Small galaxies merge to build larger and
larger galaxies
• Central black holes grow in that process
• Feedback mechanisms can even stop star
formation
Chemical evolution
• Big bang nucleosynthesis
• Stellar nucleosynthesis:
hydrostatic equilibrium,
AGB
• Explosive
nucleosynthesis
• ISM spallation
QuickTime™ and a
decompressor
are needed to see this picture.
• Also destruction…
Chemical evolution
• Star formation (t, m)
• SFR
• IMF
McWilliam 1994
•  elements primarily contributed from massive stars
and Type II SNe
• Type Ia start to contribute >~1 Gyr
• Direct indicator of early star formation rate (SFR))
• Accretion history: mergers, infalling gas
(outgoing too, enough mass to retain gas?)
Reddy et al. 2006
Thick disk
Thin disk
Chemical evolution
• Secular evolution: stellar migration, inside out
formation
Schoenrich & Binney 2009
Chemical evolution
• ISM mixing
Pan, Scannapieco, Scalo 2009
Structure of the Milky Way
-Thin Disk
-Thick Disk
-Bulge (+bar)
-Stellar Halo
-Dark Halo
Picture from Gene Smith’s astron. tutorial
Thin and thick disk
Reddy et al. 2003
Thick-disk and halo: SDSS
Bulge and bar
• Old and metal-rich populations
• Most spectroscopic studies to date in Baade’s window
(extinction is a big problem)
• 2MASS, WISE provided extensive data sets in the IR
(photometry)
• Recent VLT and and AAT spectroscopic surveys at low
resolution show a wide range of metallicities
• APOGEE/SDSS providing massive spectroscopy (1e5
stars) at high resolution (R=22,500) in the IR (1.5-1.7 µm)
Observational tools
• Astrometry: parallax, proper motion
• Photometry: brightness, space distributions
• Spectroscopy: radial velocity, chemical
composition
Gaia will do the three
Spectroscopy
Low-resolution
1. Spectral typing
2. Coarse Radial
velocities
3. Parameters,
especially logg and
Teff -- but beware of
E(B-V)
High-resolution
1. Parameters
2. Very precise radial
velocities
3. Detailed chemical
compositions
Gaia spectroscopy
• BP/RP: spectrophotometry (very low
resolution)
• RVS: high resolution, but limited
wavelength range (847-874 nm) and, more
important, low signal-to-noise
Gaia
Blue photometer:
330 – 680 nm
Red photometer:
640 – 1000 nm
Figure courtesy EADS-Astrium
1050
18
650
35
1000
16
30
950
Blue photometer
wavelength (nm)
600
550
25
500
20
450
15
400
10
350
5
300
0
0
5
10
15
20
AL pixels
25
30
35
wavelength (nm)
40
spectral dispersion per pixel (nm) .
700
14
Red photometer
900
12
850
10
800
8
750
6
700
4
650
2
600
0
0
5
10
15
20
25
30
35
AL pixels
RP spectrum of M dwarf (V=17.3)
Red box: data sent to ground
White contour: sky-background level
Colour coding: signal intensity
Figures courtesy Anthony Brown
spectral dispersion per pixel (nm) .
Photometry Measurement Concept
Ideal tests
• Shot, electronics (readout) noise
• Synthetic spectra
• Logg fixed (parallaxes will constrain
luminosity)
G=18.5
G=20
Bailer-Jones 2009
GAIA-C8-TN-MPIA-CBJ-043
S/N
per
pixel
(Spectro-)photometry
• ILLIUM algorithm (Bailer-Jones 2008). Dwarfs:
G=15 σ([Fe/H])=0.21
σ(Teff)/Teff=0.005
G=18.5 σ([Fe/H])=0.42
σ(Teff)/Teff=0.008
G=20 σ([Fe/H])=1.14
σ(Teff)/Teff=0.021
G=20
Radial Velocity Measurement Concept
Spectroscopy:
847–874 nm
(resolution
11,500)
Figures courtesy EADS-Astrium
Radial Velocity Measurement
Concept
Field of view
RVS spectrograph
CCD detectors
RVS spectra of F3 giant (V=16)
S/N = 7 (single measurement)
S/N = 77 (40x3 transits)
Figures courtesy David Katz
RVS S/N ( per transit and ccd)
• 3 window types: G<7, 7<G<10 (R=11,500),
G>10 (R~4500)
• σ ~ (S + rdn2)
• Most of the time RVS is working with S/N<1
• End of mission spectra will have S/N > 10x
higher
G magnitude
Allende Prieto 2009, GAIA-C6-SP-MSSL-CAP-003
RVS produce
• Radial velocities down to V~17 (108 stars)
• Atmospheric parameters (including overall metallicity)
down to V~ 13-14 (several 106 stars)
(MATISSE algorithm, Recio-Blanco, Bijaoui & de Laverny
06)
• Chemical abundances for several elements down to V~1213 (few 106 stars)
• Extinction (DIB at 862.0 nm) down to V~13 (e.g. Munari
et al. 2008)
• ~ 40 transits will identify a large number of new
spectroscopic binaries with periods < 15 yr (CU4, CU6,
CU8)
Atmospheric parameters (Ideal tests)
Solid: absolute flux
Dashed: absolute flux, systematic errors (S/N=1/20)
Dash-dotted: relative flux
MATISSE algorithm
to be used on these
data
(Recio-Blanco+ 06)
Allende Prieto (2008)
Observational tools
• Astrometry: parallax, proper motion
• Photometry: brightness, space distributions
• Spectroscopy: radial velocity, chemical
composition
Gaia will do the three, but additional data
are needed on spectroscopy, due to very low
resolution for BP/RP and limited spectral
coverage, S/N, and depth for RVS
The Gaia-ESO Survey
• Homogeneous spectroscopic survey of 105 stars in
the Galaxy
• FLAMES@VLT: simultaneous GIRAFFE +
UVES observations
• 2 GIRAFFE spectral settings for 105 stars
• Unbiased sample of 104 G-type stars within 2 kpc
• Target selection based on VISTA (JHK)
photometry
• Stars in the field and in ~ 100 clusters
High-resolution: UVES
High-resolution: UVES
High-resolution: UVES
High-resolution: UVES
Hill et al. 2002: An r-element enriched metal-poor giant
Low-resolution: GIRAFFE
Low-resolution: GIRAFFE
MEDUSA
mode
Low-resolution: GIRAFFE
100 stars
Low-resolution: GIRAFFE
Relevant parameters
• Atmospheric parameters: those needed for
interpreting spectra, sually: Teff, logg, [Fe/H]
(Sometimes: R, micro/macro, E(B-V), v sin i)
• Chemical abundances
Li, Be, B, C, N, O, F, Na, Mg, Al, Si …
Basics: radiative transfer
dI/dτ = I – S
S (and τ) includes microphysics
(S includes an integral of I)
T, P, ρ
Basics: Model atmospheres
• Hydrostatic equilibrium (dP/dz = -gρ)
• Radiative equilibrium (or energy
conservation)
• Local Thermodynamical equilibrium
(source function = Planck function)
• Scaled solar composition
Teff
• F = σTeff4
• F R2 = f d2
• Can be directly determined from bolometric flux
measurements f and angular diameters (2R/d)
hard but spectacular progress recently
• Photometry: model colors, IRFM
• Spectroscopic: line excitation, Balmer lines
• Spectrophotometric: model fluxes
Teff • IRFM
• Multiple implementations
Oxford (Blackwell+) 80s, Alonso+ 90s, Ramírez& Meléndez /
González-Hernández+ / Casagrande+
• Fairly model independent
• Scales in fair agreement on the metal-rich end but conflicts for
halo turn-off stars
• Issues know for cool (K and beyond) spectral types
(see Allende Prieto+ 04, S4N)
• Now in good shape based on solar-analog calibrations
• Multiple implementations
Oxford (Blackwell+) 80s, Alonso+ 90s, Ramírez& Meléndez /
González-Hernández+ / Casagrande+ 00s
• Fairly model independent
• Scales in fair agreement on the metal-rich end but conflicts for
halo turn-off stars
• Issues know for cool (K and beyond) spectral types
(see Allende Prieto+ 04, S4N)
• Now in good shape based on solar-analog calibrations
Teff • IRFM
• Multiple implementations
Oxford (Blackwell+) 80s, Alonso+ 90s, Ramírez& Meléndez /
González-Hernández+ / Casagrande+ 00s
• Fairly model independent
• Scales in fair agreement on the metal-rich end but conflicts for
halo turn-off stars
• Issues know for cool (K and beyond) spectral types
(see Allende Prieto+ 04, S4N)
• Now in good shape based on solar-analog calibrations
Teff • IRFM
• Multiple implementations
Oxford (Blackwell+) 80s, Alonso+ 90s, Ramírez& Meléndez /
González-Hernández+ / Casagrande+ 00s
• Fairly model independent
• Scales in fair agreement on the metal-rich end but conflicts for
halo turn-off stars
• Issues know for cool (K and beyond) spectral types
(see Allende Prieto+ 04, S4N)
• Now in good shape based on solar-analog calibrations
Teff • IRFM
• Multiple implementations
Oxford (Blackwell+) 80s, Alonso+ 90s, Ramírez& Meléndez /
González-Hernández+ / Casagrande+ 00s
• Fairly model independent
• Scales in fair agreement on the metal-rich end but conflicts
for halo turn-off stars
• Issues know for cool (K and beyond) spectral types
(see Allende Prieto+ 04, S4N)
• Now in good shape based on solar-analog calibrations
Teff • IRFM
• Multiple implementations
Oxford (Blackwell+) 80s, Alonso+ 90s, Ramírez& Meléndez /
González-Hernández+ / Casagrande+ 00s
• Fairly model independent
• Scales in fair agreement on the metal-rich end but conflicts for
halo turn-off stars
• Issues know for cool (K and beyond) spectral types
(see Allende Prieto+ 04, S4N)
• Now in good shape based on solar-analog calibrations
Teff • IRFM
• Multiple implementations
Oxford (Blackwell+) 80s, Alonso+ 90s, Ramírez& Meléndez /
González-Hernández+ / Casagrande+ 00s
• Fairly model independent
• Scales in fair agreement on the metal-rich end but conflicts for
halo turn-off stars
• Issues know for cool (K and beyond) spectral types
(see Allende Prieto+ 04, S4N)
• Now in good shape based on solar-analog calibrations
Teff • weak-line excitation
• Classical method
lines of different formation
depth (excitation energy)
are very sensitive
• Model dependent: <T(τ)>,
turbulence, NLTE
• Observationally friendly
Teff • Balmer lines
• Perfected by Fuhrmann+ in the 90s
Teff • Balmer lines
• Perfected by Fuhrmann+ in the 90s
• Applied to echelle spectra by Barklem+
Teff • Balmer lines
• Perfected by Fuhrmann+ in the 90s
• Applied to echelle spectra by Barklem
• Improved theoretical broadening calculations -- see poster and
a recent paper by Cayrel+
Main remaining issue
is the effect of convection
on the thermal atmospheric
structure -- need 3D or an
external calibration
Teff • spectrophotometry
• Combines photometry and spectroscopy
• Hard to get very high-quality spectra (<2-3%).
Need space observations to access the UV
• Great progress in the last decade (Bohlin+
Cohen+)
• HST flux calibration based on Oke V scale plus
hot DA WD models. Consistency all around with
Vega and solar analogs
• ACCESS (Kaiser+ 2011)
Teff • spectrophotometry
• Combines photometry and spectroscopy
• Hard to get very high-quality spectra (<2-3%).
Need space observations to access the UV
• Great progress in the last decade (Bohlin+
Cohen+)
• HST flux calibration based on Oke V scale plus
hot DA WD models. Consistency all around with
Vega and solar analogs.
Solar analogs observed
With STIS compared with solar-like Kurucz models
Teff • spectrophotometry
• Combines photometry and spectroscopy
• Hard to get very high-quality spectra (<2-3%).
Need space observations to access the UV
• Great progress in the last decade (Bohlin+
Cohen+)
• HST flux calibration based on Oke V scale plus
hot DA WD models. Consistency all around with
Vega and solar analogs.
HD 201091 (Observations from STIS NGSL)
Teff • spectrophotometry
• Combines photometry and spectroscopy
• Hard to get very high-quality spectra (<2-3%).
Need space observations to access the UV
• Great progress in the last decade (Bohlin+
Cohen+)
• HST flux calibration based on Oke V scale plus
hot DA WD models. Consistency all around with
Vega and solar analogs.
HD 10780 (observations from STIS NGSL)
logg
• Gravitational field compresses the gas giving a nearly
exponential density structure (pressure)
• Hard to get with accuracy: the spectrum is only
weakly sensitive to gravity
• Photometry: ionization edges (Saha), molecular
bands, or damping wings of strong metal lines
• Spectroscopy: ionization balance (e.g. Fe/Fe+) or
colisionally-dominated line wings
• Stellar structure models (luminosity)
Logg • Photometry
• Intermediate or narrow band filters
(Strömgren, Mg 520 nm) taking advantage
Majewski + 2000
of pressure-sensitive features
Image: Michael Richmond
Logg • Spectroscopy
• Ionization balance: model dependent
• Strong lines (Na D, Mg b, Ca II IR
triplet…)
Ramirez+ 2006
Logg • Stellar structure
• Need good luminosity determination (i.e. distance)
• Relies on interior models, fairly reliable but with caveats
(solar conumdrum, convection recipes, difusion)
• Need M and R, not age
• Now statistically solid (Reddy+ 03, Jørgensen &
Lindegren 05, Pont & Eyer …)
Logg • Stellar structure
• Need good luminosity determination (i.e. distance)
• Relies on interior models, fairly reliable but with caveats (solar
conumdrum, convection recipes, difusion)
• Need M and R, not age
• Dominated by errors in parallaxes for Hipparcos (V<9, d<100
pc) stars, but likely not the case for Gaia
• Now statistically solid (Reddy+ 03, Jørgensen & Lindegren 05,
Pont & Eyer …)
Logg • Stellar structure
• Need good luminosity determination (i.e. distance)
• Relies on interior models, fairly reliable but with caveats (solar
conumdrum, convection recipes, difusion)
• Need M and R, not age
• Dominated by errors in parallaxes for Hipparcos (V<9, d<100
pc) stars, but likely not the case for Gaia
• Now statistically solid (Reddy+ 03, Jørgensen & Lindegren 05,
Pont & Eyer …)
[Fe/H]
• An oversimplification
• High sensitivity of the spectrum (can also
be derived from photometry including
blue/UV), but highly model dependent
• Need many weak lines, good atomic data,
good spectra, and a good model
More… R, micro/macro
E(B-V), v sin i
• R needed for spherical models
• Micro- macro-turbulence needed for hydrostatic
models
• E(B-V) needed in photometry/spectrophotometry
data are involved
• Rotation cannot be ignored, but hard to
disentangle from other broadening mechanisms in
late-type stars
Finally, chemical abundances
• UV Atomic continuum opacities
• Line absorption coefficients: damping
wings
• Atomic and molecular data
Lawler, Sneden
& Cowan 2004
Spectral line formation
• UV Atomic continuum opacities
• Line absorption coefficients: damping
wings
• Atomic and molecular data
• NLTE
Na I
Allende Prieto, Hubeny & Lambert 2003
MISS
Multiline Inversion of Stellar Spectra
3 Observation/Analysis
• Ø (8m VLT), Coverage (broad UVES coverage, at
least 2 GIRAFFE setups), multiplexing (~100
objects on GIRAFFE and ~10 on UVES), R (low
and high)
• Data Reduction (ESO pipelines, completed with
software at CASU/Univ. of Cambridge and
ARCETRI)
• Analysis: From Ews to line profiles (classical)
• Neural networks, genetic algorithms and other
optimization schemes (some teams)
Using the chemical
abundance information
The Golden Rule
The Surface Composition of a star reflects that of the
ISM at theTime the star formed
Golden rule applies? yes
• Galactic structure and chemical evolution
Golden rule applies? yes
• Galactic structure and chemical evolution
• Solar Structure
Golden rule applies? yes
• Galactic structure and chemical evolution
• Solar Structure
• Cosmology: 1H, 2H, 3He, 4He, 7Li, 6Li
BBN
Figure from Edward L. Wright
Golden rule applies? yes
•
•
•
•
Galactic structure and chemical evolution
Solar Structure
Cosmology: 1H, 2H, 3He, 4He, 7Li, 6Li
SN yields
R-process is universal
Sneden et al.
2003
Golden rule applies? NO
• Diffusion (Sun, CPs, accretion, SN yields
again)
Secondary stars in BH/NS binary
systems
Centaurus X-4
Gonzalez-Hernandez
et al. 2005
Golden rule applies? NO
• Difusion (Sun [M/H]-0.07 dex, CPs,
accretion, SN yields again)
• Mixing and destruction (Li, Be)
Golden rule applies? NO
• Difusion (Sun [M/H]-0.07 dex, CPs,
accretion, SN yields again)
• Mixing and destruction (Li, Be)
• RV Tauri stars
Giridhar et al.
2005
Gaia-ESO main
Science Objectives
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Galactic phase-space substructure
Chemical evolution
Star migration
Disk gradients and their time evolution
Cluster evolution (formation, dissolution,
self-polution)
The field stars
• Mid-resolution GIRAFFE spectra (R~12,000) for
105 stars to V < 20 (mostly in the Gaia RVS gap)
• GIRAFFE HR21 (Ca II IR triplet) + HR10 (~540
nm) with 10<S/N<30 to yield atmospheric param.,
radial velocities, limited chemistry
• UVES spectra for 104 G-type stars to V<15 with
S/N>50 to yield detailed atmospheric parameters ,
high-precision radial velocities and 11+ elemental
abundances
Breakdown by population
• Bulge: bright (I~15) K-giants with 2 GIRAFFE
settings at 50<S/N<100
• Halo/Thick disk: F-type turn-off stars (SDSS
17<r<19)
• Outer thick disk: F-type turnoff (75%) and K-type
giants at intermediate galactic latitude
• Thin disk (I~19) from 6 fields in the plane with
HR21-only data (+ UVES sample)
The cluster stars
• Cluster selection from Dias et al. (2002),
Kharchenko et al. (2005), WEBDA catalogues,
supplemented by exploratory program at Geneva
• Only clusters with membership information
considered
• Nearby (<1.5 kpc; down to M-dwarfs) and distant
clusters (giants only) will be observed, sampling a
wide range in age, [Fe/H], galactocentric distance
and mass
• 6 GIRAFFE settings (HR03/05A/06/14A/15N/21)
down to V~19
Open clusters: *
• +http://ircamera.as.arizona.edu
UVES sample down to V~16
Source:
The cluster stars
• Cluster selection from Dias et al. (2002),
Kharchenko et al. (2005), WEBDA catalogues,
supplemented by exploratory program at Geneva
• Only clusters with membership information
considered
• Nearby (<1.5 kpc; down to M-dwarfs) and distant
clusters (giants only) will be observed, sampling a
wide range in age, [Fe/H], galactocentric distance
and mass
• 6 GIRAFFE settings (HR03/05A/06/14A/15N/21)
down to V~19
• + UVES sample down to V~16
Observations and Calibration
• Visitor mode observations
-- started December 2011
• 300 nights over 5 years (~1500 pointings)
• Target selection will be largely based on VISTA
VHS photometry + additional information for
clusters
• ESO Archive (on-going analysis)
• Calibration fields to control/match
parameter/abundance scale across surveys
Data reduction/analysis
• Data reduction performed at Cambridge
and Arcetri likely based on ESO pipeline
• Radial velocity derivation
• Object classification
• Spectral analysis: atmospheric parameters
and abundances
• Gaia-ESO archive
Spectral analysis
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•
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UVES spectra of normal FGK stars
GIRAFFE spectra of normal FGK stars
Pre-MS and cool stars
Hot (OBA-type) stars
Funny things
Survey parameter homogenization
Automation
• Classical analysis methods can be coded in the
computer
• These will have limitations: need to reliably
measure equivalent widths (EW)
• Ultimately, the use of EW is related to simplify
the calculations (scalar quantities instead of
arrays) but is also somewhat blind, I.e. full
spectral analysis preferred
Automation II
• Optimization methods: local (gradient, NelderMead…), global (metropolis, genetic algorithms…)
• Projection methods (ANN, MATISSE, PCA, SVM…)
• Bayesian methods
• But many combinations possible
• Spectral model can be calculated on the fly or
interpolated
• Issues are sometimes continuum normalization,
complicated PSF, large number of dimensions,
degeneracies
An example, the IAC node
• FERRE optimization with interpolation on a pre-computed
grid
• N-dimensional f90 code
• Various algorithms: Nelder-Mead (Nelder & Mead 1965),
uobyqa (Powell 2002), Boender-Rinnooy Kan-StrougieTimmer algorithm (1982)
• Linear, quadratic, cubic spline interpolation
• Spectral library on memory or disk
• PCA compression
• Handling of complex PSF w/o compression
• Flexible: SDSS/SEGUE, WD surveys, APOGEE,
STELLA, Gaia-ESO…
Abundances
Stellar Parameters
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•
•
•
•
3 (Teff, log g, [Fe/H])
• For many/most targets (disk cool giants):
4 (Teff, log g, [Fe/H], [C/Fe])
- Teff, log g, Fe/H, C/Fe, N/Fe, O/Fe, maybe .
5 (Teff, log g, [Fe/H], [C/Fe], micro)
• Simplify for metal-poor stars ([Fe/H] < -1 or -2):
5 (Teff, log g, [Fe/H], [C/Fe], [O/Fe])
- Teff, log g, Fe/H, O/Fe, maybe .
6 (Teff, log g, [Fe/H], [C/Fe], [O/Fe], E(B• Simplify for warmer types (G-F):
V))
- Teff, log g, Fe/H, C/H, maybe .
• 6 (Teff, log g, [Fe/H], [C/Fe], [C/Fe], [N/Fe])
…
A minute/star/processor
(3.5 days on 20 processors for 100,000 stars)
S/N=80
[Fe/H]
97
[C/Fe]
[O/Fe]
E(B-V)
Teff
logg
Abundances
Stellar Parameters
Teff=4408 K
logg=2.13
Logmicro=0.33
[Fe/H]=-0.56
[C/Fe]=+0.44
[N/Fe]=+0.02
[O/Fe]=+0.50
98
ASPCAP Fitting the Arcturus spectrum (Hinkle et al.)
smoothed to R=30,000
Automated analysis: GIRAFFE
• Tests with MILES spectra (R~2000) from
the INT (Sanchez Blazquez et al. 2006)
• The same code (FERRE)
• Fitting data calibrated in flux and
continuum-normalized
Software
•
•
•
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Gaussian LSF (fiber, wavelength)
Quadratic interpolation of fluxes
Normalization by blocks
Successful tests performed on MILES
library
Continuum on
This
Work
MILES parameters (Cenarro et al. 2009)
[Fe/H]
Teff
logg
Distributions of residuals
Continuum off
This
Work
MILES parameters (Cenarro et al. 2009)
[Fe/H]
Teff
logg
Distributions of residuals
Consortium
•
•
•
•
Over 300 people involved (90+ centers)
2 co-Pis (G. Gilmore and S. Randich)
A steering committee
17 working groups
Steering Committee
Working
groups
Data Release
• All raw data immediately public
• 3-level data products with different time scales
• Level-1: 1D spectra, associated photometry, object
classification and RVs (release every 6 months)
• Level-2: RV variability info, atmospheric
parameters and abundances (yearly releases)
• Level-3: all of the above for final co-added data
and mean cluster metallicities (end of survey)
Competition
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SDSS, SEGUE1/2
BOSS
SDSS-III APOGEE
HERMES
HETDEX
After Sloan 3 (STREAMS, APOGEE-II/S)
[BigBOSS, 4MOST, MOONS, WEAVE]
Recent trends in
spectroscopic studies
• 3D model atmospheres: a beginning
• full NLTE: good progress for hot stars, but …
• Data archival: survey projects going on with massive
archives that become public (low-res: SDSS, SEGUE,
GALEX) (high-res: Elodie, S4N)
• Analysis automation: a beginning
• Breaking the Z barrier
The Desirable future
• 3D model atmospheres
• full NLTE
• A pending observational test for solar-type stars: center-tolimb variation of the solar spectrum
• Data archival: VOs (including both observations and
models)
• Stronger efforts to measure/compute atomic data
• Stronger efforts to use the newly available atomic data
• Full analysis automation
• R – an ignored variable?
Gaia-ESO Summary
• 100,000 stars at mid-resolution (x2 GIRAFFE
settings) and 10,000 stars at high-resolution: 300
VLT nights over 5 yr
• Field stars and open clusters
• Uniform composition and radial velocity
information across the Galaxy complementing
Gaia’s data
• Large european consortium
• Swift schedule for data
reduction/processing/analysis/delivery
• But serious competition!