ASPCAP: Derivation of Atmospheric Parameters and Abundances Carlos Allende Prieto

1. APOGEE Science and Software Preliminary Design Review ASPCAP: Derivation of Atmospheric Parameters and Abundances Carlos Allende Prieto Instituto de Astrofísica de Canarias December 15-16, 2009 Johns Hopkins University Baltimore, MD

2. To extract the most fundamental information contained in the APOGEE spectra: stellar parameters and chemical compositions of the APOGEE targets (late-type stars) In addition: ASCAP will perform a basic characterization of the sources, flagging oddities such as emission-line stars and undesired back/foreground interlopers purpose

3. Input 1D spectra combined- visit- exposure + cal. Products + RVs Input 1D spectra combined- visit- exposure + cal. Products + RVs Basic Structure Coarse characterization Coarse characterization Determ. [Si/Fe] Determ. [Na/Fe] pre-processing (radial velocity correction resample, combine, filter, mask…) pre-processing (radial velocity correction resample, combine, filter, mask…) Data-base output Data-base output Determ. [Ca/Fe] Determination of principal parameters (Teff, logg, [Fe/H], [C/Fe], [O/Fe] Determination of principal parameters (Teff, logg, [Fe/H], [C/Fe], [O/Fe] … Determ. [Mn/Fe]

4. Coarse characterization: identification of spectra that are not suitable for a chemical analysis as planned: hot stars, emission-line objects, multiple-line systems, fast rotating stars, galaxies, etc. Data pre-processing: placing the input spectra on a uniform (rest) wavelength scale when needed, continuum normalization Determination of atmospheric parameters: 2 minimization using a pre-computed grid of synthetic spectra Abundance determination Let’s see more details… Basic ASPCAP structure Ana’s talk

5. Phase I. Determination of fundamental atmospheric parameters: Teff, logg, [Fe/H], C/Fe, O/Fe. Using a large fraction of the APOGEE window (1.51-1.68 m) Phase II. Derivation of other elemental abundances: Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Co, Ni. From narrower spectral windows (masks) optimally targeted for each element. 3. ASPCAP 2 optimization

6. Abundant elements which have a profound impact on the equation of state need to be considered consistently on model atmospheres and spectral synthesis (e.g. C and O) While elements such as carbon, nitrogen and oxygen are everywhere in the H-band (CN, CO, CH), others such as Ca only affect narrow windows of the spectrum Two-phase analysis Ca

7. Direct comparison between observations and synthetic spectra. An optimization code will constrain relevant parameters. Algorithm to be chosen from several existing codes based on a series of blind tests (selection criteria will be accuracy and speed -- see below). LSF info provided by calibration module. Synthetic spectra degraded on-the-fly. LSF variations with x, , t considered. Two types of model atmospheres will be computed: Kurucz (only 1-D plane-parallel) and MARCS (spherical or plane-parallel) model atmospheres. Final choice will depend on the results for a few well-studied bright stars. Spectral synthesis will use detailed continuum and line opacities, and include scattering Line opacities refined by: literature compilation laboratory work + theoretical calculations empirical matching of Arcturus/solar spectra ASPCAP 2 optimization

8. Late-type stars, mainly giants, as broad as possible Approach will be to first secure the analysis for most giants (3000<Teff<5000 K), then deal with warmer star, including calibrators Minimum target coverage is 3000<Teff<7000 K, but aspirations to handle nearly all objects that will fall on fibers Focusing on single stars, later will worry about double-lined binaries Targets

9. Mountain Software (real time quality assurance) Raw data Data Products Spectral products 1-D calibrated spectra error vector PSF vector RV products RV, errorRV variability, errorv sin i, error Chemical productsTeff , log g, [Fe/H], [X/Fe], associated uncertainties. APOGEE 2D software (AP2D) 1D Software (AP1D) APOGEE Radial VelocityPipeline (APRV) APOGEE Stellar Parameters and Chemical Abundance Pipeline (ASPCAP) External Calibration

10. Overview of Abundance Pipeline Personnel APOGEE PI Steven Majewski Project/Business ManagerFred Hearty SDSS3 Employee Science Team Target Selection/Field Placement/ Survey Strategy Johnson/Frinchaboy/Soft. developer Science WorkingGroup Survey Scientist Ricardo Schiavon Spectrum synthesis Model atmospheres Allende/Asplund 2 Optimization Allende Coarse Characterization Allende Line list Lawler, Smith Shetrone Allende Calibration stars Frinchaboy Shetrone Johnson Software Postdoc 2Ana Garcia Perez Software Postdoc 1David Nidever Pipeline Operator (BS1)Not hired yet 10 May 18, 2009

11. Overview of Abundance Pipeline Personnel Carlos Allende Prieto Martin Asplund Dmitry Bizyaev Katia Cunha Jon Holtzman James Lawler Young Sun Lee Kaike Pan Ivan Ramírez Ricardo Schiavon Matthew Shetrone Verne Smith APOGEE PI Steven Majewski Project/Business ManagerFred Hearty SDSS3 Employee Science Team Target Selection/Field Placement/ Survey Strategy Johnson/Frinchaboy/Soft. developer Science WorkingGroup Survey Scientist Ricardo Schiavon Spectrum synthesis Model atmospheres Allende/Asplund 2 Optimization Allende Coarse Characterization Allende Line list Lawler, Smith Shetrone Allende Calibration stars Frinchaboy Shetrone Johnson Software Postdoc 2Ana Garcia Perez Software Postdoc 1David Nidever Pipeline Operator (BS1)Not hired yet 11 May 18, 2009

12. Carlos Allende Prieto Martin Asplund Dmitry Bizyaev Katia Cunha Ana Elia García Pérez Jon Holtzman James Lawler David Nidever Young Sun Lee Kaike Pan Ivan Ramírez Ricardo Schiavon Matthew Shetrone Verne Smith Current work load distribution • coordination, 2Optimization, spectral synthesis……………………. • model atmospheres, spectral synthesis……………………………….. • linelist development, simulations ……………………………………. • linelist development …………………………………………………. • coarse characterization, pre-processing ……………………………… • simulations ………………………………………………………… • linelist development …………………………………………………. • 2optimization ………………………………………………………. • 2optimization ………………………………………………………. • linelist development………………………………………………….. • model atmospheres ………………………………………………….. • 2optimization ………………………………………………………. • linelist development …………………………………………………. • linelist development ………………………………………………….

13. Carlos Allende Prieto Martin Asplund Dmitry Bizyaev Katia Cunha Ana Elia García Pérez Jon Holtzman James Lawler David Nidever Young Sun Lee Kaike Pan Ivan Ramírez Ricardo Schiavon Matthew Shetrone Verne Smith Ongoing activities The underlined entries below have already seen activity • coordination, 2Optimization, spectral synthesis……………………. • model atmospheres, spectral synthesis……………………………….. • linelist development, simulations ……………………………………. • linelist development …………………………………………………. • coarse characterization, pre-processing ……………………………… • simulations …………………………………………………………… • linelist development …………………………………………………. • 2optimization ………………………………………………………. • 2optimization ………………………………………………………. • linelist development………………………………………………….. • model atmospheres ………………………………………………….. • 2optimization ………………………………………………………. • linelist development …………………………………………………. • linelist development ………………………………………………….

14. Linelist compilation Baseline lists created by Shetrone and Allende Prieto: completed months ago Additional resources provided by Cunha and Smith from literature + their own + 3rd party: completed Enhancements fromliterature search and quantum calculations (for light elements) by Jim Lawler: ongoing, completed for C, Na, S, K, Ca, Mn and neutron-capture elements Code to adjust line f-values to improve agreement with observed spectra of the Sun and Arcturus written by Bizyaev (with Pan and Shetrone): ongoing, beta version available Ongoing activities  details

15. Comparison of calculations with real starsArcturus (baseline linelists) Observed Synthesis R=100,000 Hinkle et al. (1995)

16. Comparison of calculations with real starsSun (baseline linelists) Observed Synthesis R=300,000 Livingston & Wallace (1991)

17. Selection of the optimization algorithm First test data set created with 3 atmospheric parameters: results available for two (2/4) codes (see next slide) but very promising  gives confidence that the atmospheric parameters can be disentangled based solely on APOGEE spectra Second data set created with 5 atmospheric parameters: completed Tests with 5 atmospheric parameters available for one code: completed Implementation/testing of add-ons to baseline linelists: ongoing Ongoing activities II  details

18. S/N=100 (~APOGEE’s case) S/N=10 (robustness test) Simple tests adding Gaussian noise

19. S/N=100 per pixel[Fe/H], Teff, logg

20. 3-parameter tests Residuals S/N=100

21. S/N=10 per pixel[Fe/H], Teff, logg

22. 3-parameter tests

23. 5-parameter tests

24. 2 optimization is basically written we are using existing software. Some modifications needed, ranging from very small to significant (LSF handling). Codes under consideration are all FORTRAN(90) or IDL. They have all been written by members of the team (or close partners): specfit (Genetic algorithm), ferre (Nelder-Mead), ngs (robust average best-fitting node parameters), MAX (weighted compression), EZ_Ages (sequential grid inversion algorithm) coarse characterization, and pre-processing chain will be written by APOGEE postdoc García Pérez, most likely in IDL (assisted by Nidever and Allende Prieto) Writing the software

25. July 2009: beginning construction of 1st model grid (Allende Prieto, Ramírez, Asplund); 2nd iteration for linelist improvement begins September 2009: postdoc García Pérez started in Virginia, and so does pre-processing chain development December 2009: first model grid completed, 5-D blind tests for optimization algorithm March 2010: pre-processing chain written and tested, PSF on-the-fly handling dev. stars June 2010 (through fall): pipeline integration and testing December 2010: delivery of 2nd (last) model grid Tentative schedule

26. Validation of results first internally using simulations with realistic noise models Tight speed constraints imposed to enable a re-analysis of the entire APOGEE database over just a few days. This implies extensive monte carlo is feasible External validation using well-studied clusters and individual stars; coordination with SEGUE Coordination with HERMES will provide abundances for additional elements, but also a consistency check against optical spectra of the same stars Testing

27. Deliverables Atmospheric parameters for each star, with error covariance matrix Measurements of a set of elemental abundances (e.g., CNO, a series, Fe-peak, odd-Z, perhaps neutron capture) for each star, with uncertainties Doppler-corrected average APOGEE spectra re-sampled into a uniform wavelength scale and the corresponding best-fitting model

28. Thank you!

30. Discarded slides to follow…

31. Main APOGEE targets are late-type stars, and in particular K and early-M giants. ASPCAP will target a limited range of spectral types, where atmospheric parameters and chemical abundances can be well-constrained using a single methodology A basic algorithm will be in place to classify targets based on their H-band spectra and available photometry, flagging stars that are outside of the ASPCAP domain, and preventing them from entering the ASPCAP pre-processing and optimization algorithms (see next). Doubled-lined spectroscopic binaries to be identified from the analysis of the cross-correlation function 1. Coarse characterization

32. Data must be in a homogeneous wavelength scale, after accounting for Doppler shifts: interpolation and resampling Spectra should be continuum normalized for abundance analysis: polynomial least-squares fitting Spectral PSF may vary with time and fiber: spectra could be homogenized to a common (fatter) PSF, but preferred procedure involves tracking the PSF variations and making the model fluxes adapt: non-linear least-squares, convolution 2. Pre-processing