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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 • • • • • 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 • • • • • • 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 • • • • • 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 • • • • 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 • • • • • • • 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!