ASPCAP Review Progress report and schedule ASPCAP status and goals

1. October 3, 2012 ASPCAP ReviewProgress report and scheduleASPCAP status and goals

2. ASPCAP Goals • Extract atmospheric parameters (Teff, logg, [M/H]) and their error covariances, and chemical abundances (C N O Na Mg Al Si S K Ca Ti Cr Mn Fe Co Ni) and their uncertainties from APOGEE spectra • Other • Coarse characterization • Identify double-lined spectroscopic binaries • Provide optimal templates for RV measurement • Provide hot-star templates for telluric-line removal • Breaking new ground. Never done before automatically.

3. Science requirements • Must-have (top-priority) elements: C N O Mg Al Si Ca Fe Ni • Important-to-have (medium-priority) elements: Na S Ti Mn K • If-at-all-possible elements: V Cr Co • 0.1 dex internal precision; 0.2 dex external accuracy • Top priority elements to be measured for >80% of the stars with 2 or more lines • Medium priority elements to be measured for >50% of the stars For cool/metal-rich stars requires use of blended/overlapping features

4. DR10 Scope • Stellar parameters: Teff, log g, [M/H], for apStar • Open issues: • Abundances: [α/Fe], [C/Fe], and [N/Fe] • Parameters for apVisit? • S/N limit • Uncertainties • Zero point errors (e.g., log g) • Repeatability • Noding • Systematic effects (e.g., [M/H] vs Teff correlations)

5. Personnel • Carlos Allende Prieto: team leader, software development and testing, ASPCAP runner • Szabolcs Meszaros (IAC): PD, model atmospheres, ASPCAP runner, testing using cluster and Kepler stars • Ana Elia Garcia-Perez (UVa): PD, ASPCAP runner, software development and testing • Jon Holtzman (NMSU): software development and testing, ASPCAP runner, testing using Kepler stars • Matthew Shetrone (HET): Line lists, “manual” abundance analysis of cluster stars • Lars Kosterke: software development • Verne Smith (NOAO/ON): Abundance analysis of FTS stars • Katia Cunha (NOAO/ON): “Manual” abundances of field stars • New additions: Maria Bergemann (postdoc, MPIA), Nick Trout (graduate student, UVa)

6. Priorities • Select “official” version of ASPCAP and tag it • Establish content of DR10 • Uncertainties • Repeatability • Verify reality of [α/Fe], [C/Fe], [N/Fe], using cluster, FTS stars • Systematics: log g mismatch, Teff-[M/H] correlation – Kepler stars • Noding • Define test/verification sample • Combination of Kepler, cluster, standard stars • Representative sample, covering full range of parameter space • Define next batch of elemental abundances for DR11 • Balance scientific interest with easiness • Define spectral regions for elemental abundances • Plan incorporation of additional effort (Bergemann, Trout)

7. October 4-5, 2012 ASPCAP ReviewProgress report and scheduleSoftware packages

8. ASPCAP SOFTWARE PACKAGES PRE-PROCESS AND CLASSIFY APOGEE STELLAR SPECTRA FOR STELLAR PARAMETERS DETERMINATIONS ESTIMATE STELLAR PARAMETERS ORGANISE RESULTS PRE-PROCESS DATA FOR STELLAR ABUNDANCE DETERMINATIONS ESTIMATE THE STELLAR CHEMICAL ABUNDANCES ORGANISE RESULTS in develop.

9. SOFTWARE PACKAGES • FERRE and the IDL wrapper • Two of the three main ASPCAP’s component. • The third component is a multidimensional library of synthetic spectra. • FERRE is a Fortran code that fits a set of input data (e.g., spectra) to a grid of models (e.g, synthetic spectra) through a chi-2 minimization and returns the model parameters. • The code performs NRUN searches starting from random starting points (developed at IAC) and has several algorithms to find the minima.

10. SOFTWARE PACKAGES • There are two tagged versions (v3.5.8 and v3.7.0). The last version has the possibility of evaluate the chi2 value either in the compressed or in the flux space when using PCA compression. • The IDL WRAPPER does pre- and post-processing, and produce FERRE jobs • Pre-processing: read data, resample (if necessary), normalize and remove bad points (several versions exists). • Create and submit FERRE jobs in a serial or queue mode (using PBS). • Post-processing: create plots and bundle ASPCAP results into FITS tables with parameters, spectra, best-fits etc.

11. Software packages • The code has three branches: Carlos’ (qaspcap), Jon’s and Ana’s version. • qaspcap, only pre-processing and stellar parameter determination for experimental purposes (developed at IAC). • Jon’s, stellar parameter (developed at NMSU). • Ana’s, stellar parameters (developed at UVa). • Ana’s covers many cases (different resampling options, different errors) and has been developed to have more functionality (both stellar parameter and abundances, avoid multiple pre-processing in the case of analysis of several classes), much of which is not in use at the moment.

12. Software packages • Jon’s pre-processing and pipeline structure has been simplified and optimized to work with stellar parameters in the actual setup. • Multiple versions are very helpful for detailed comparisons of effects of minor differences in pre-processing, will converge shortly to a scheme: • continuum normalization (with/without masking) matters • handling of errors matters • Jon’s and Ana’s are svn controlled and Ana’s has been tagged. Carlos’ exists as a tar ball in the wiki. • https://trac.sdss3.org/browser/repo/apogee/aspcap/idlwrap • https://trac.sdss3.org/attachment/wiki/APOGEE/ASPCAP/Tools/

13. Software packages • Documentation: • A README file to install the code. • Manual describing the code (needs update). • Data model for apVisit files (needs update), missing for apStar and libraries. • Pipeline status: • Easy to install with sdss3install (tested at NMSU, IAC had some issues installing ExtUPS). • Handles products version. • Completely functional for stellar parameters, either with apVisit or apStar files.

14. Software packages • Analysis: original thought was to use combined files (apStar), which have higher S/N, however analysis started with individual visits (apVisit files), since they were available earlier. • Control sample defined using a set of standard and Kepler stars, and globular clusters covering a range of metallicity (see q3). • Analysis status: • First year of observations has been analyzed for apVisit data, ~ 684 observations sets (plateID+MJD).

15. Software packages • v0.3 processed at UVa (old linelist, 7 parameters, pre- and post-shutdown) (not strictly tagged, but can be tracked back) • http://www.astro.virginia.edu/apogee/data/apogee/aspcap/aspcap_v0.3/plates/results • Problem: evaluate chi2 in PCA space • (demonstrated to be less effective) • v0.4 processed at IAC (a more recent linelist, 6 parameters with vmicro fixed at 2 km/s, post-shutdown, not strictly tagged) • Best results • http://astronomy.nmsu.edu/holtz/apogee/data/aspcap/current/plates/results/

16. Software packages • Test suite of data processed at IAC and NMSU with apStar (also at UVa) and apVisit files: M5, M13, M67 covering a range of metallicities, and Kepler stars (only at IAC and NMSU). • Performance at UVa: 4 nodes, 4x16 processors (2x96 GB, 2x48 GB), 60 IDL licenses • for 6 parameters and 270 spectra (a typical plate) with 2 processor takes ~ 5 hrs. • same but for 7 parameters takes ~ 2 days. • One year survey • 684 plates ~ 5 days • 3x less for all the apStar files

17. Q2. Software packages • Improvements • Update documentation, including data model. • Modify coarse characterization routines. • Complete the routines for a clever threading. • Test that works with multiple classes. • Update abundance modules and testing.

18. ASPCAP ReviewProgress report and scheduleInterpolations tests, cluster and Kepler field calibrations of ASPCAP October 4-5, 2012

19. Introduction • log g (pages 4-6): • 0.1-0.2 dex higher at [Fe/H] = 0 than Kepler stars, 1 dex higher than cluster isochrones at [Fe/H] = -2 • It is a function of [Fe/H] (clusters), [Fe/H] and Teff (Kepler) (???) • [Fe/H] (pages 9-10): • Agrees well with literature at [Fe/H] = 0, overestimating by 0.1-0.3 dex at [Fe/H] = -2 depending on literature used • Teff (pages 11-13): • Agrees well with Teff from (J-Ks) using Gonzalez et al. 2009 calibrations for some cluster (examples: M13, M3) • Linear shifts and trends show up in others (examples: M5, M2) • Agrees well with FTS stars • C, N, alpha (pages 15-19): • noding in C, and maybe N, alpha • [A/Fe]-[Fe/H] degeneracy visible

20. Basic parameters • Library used: p_psd0121_w123.dat, 6 params, vmicro = 2 km/s • [Fe/H]: -2.5 to 0.5, 0.5 • [C/Fe], [α/Fe]: -1 to 1, 0.25 • [N/Fe]: -1 to 1, 0.5 • Teff: 3500K to 5000K, 250K • log g: 0 to 5, 0.5 • V1.0 reductions, ASPCAP v0.4 (no fits output, no tagging) • Stars examined: S/N > 70, Teff < 4980K • Multiple apVisit results were averaged for each star

21. ASPCAP log g vs. Isochrones • Gravities derived from isochrones, linear dependence on metallicity • 0.1 dex shift at [Fe/H]=0, 1 dex shift at [Fe/H]=-1 • Isochrones + Kepler • 0.2-0.3 dex shift at [Fe/H]=0

22. ASPCAP log g vs. Kepler log g • Depends on effective temperature because of the RC stars • apVisit apStar

23. ASPCAP log g vs. Kepler log g • Inconsistency between Kepler and cluster calibrations

24. Examples of clusters • All clusters: • https://trac.sdss3.org/wiki/APOGEE/Publications/CalibrationClusters

25. Examples of clusters • All clusters: • https://trac.sdss3.org/wiki/APOGEE/Publications/CalibrationClusters

26. ASPCAP [Fe/H] vs. literature • Good agreement at [Fe/H] = 0, slight shifts at [Fe/H] = -2

27. ASPCAP [Fe/H] vs. literature • Second order polynomial fits with Teff-[Fe/H] cross-term

28. ASPCAP Teff vs. (J-Ks) Teff • Examples for clusters which agree well with photometric Teff • M13 M67 • M3 NGC 188

29. ASPCAP Teff vs. (J-Ks) Teff • Examples for clusters where obvious shifts/trends are visible • M5 M2 • NGC 6819 NGC 2420

30. FTS stars • 4 stars with (FTS) higher quality observations, smoothed and processed with ASPCAP as if they were APOGEE observations • ASPCAP values Verne’s analysis • Teff logg [Fe/H] micro • βAnd 3825 0.9 -0.2 2.2 • μLeo 4540 2.1 0.3 1.8 • αBoo 4280 1.7 -0.4 1.85 • δOph 3850 1.2 0.0 1.9 • Comparison with Verne’s analysis of these stars shows offset (and σ) of about -92 K (24 K) in Teff, +0.5 dex (0.2 dex) in log g and -0.13 (0.07 dex) in [Fe/H]

31. Conclusions • log g (pages 4-6): • 0.1-0.2 dex higher at [Fe/H] = 0 than Kepler stars, 1 dex higher than cluster isochrones at [Fe/H] = -2 • may result from inaccurate treatment of H lines • Inconsistency between Kepler and cluster calibrations • [Fe/H] (pages 9-10): • Agrees well with literature at [Fe/H] = 0, overestimating by 0.1-0.3 dex at [Fe/H] = -2 depending on literature used • Scatter in [Fe/H] tipically is between 0.08 and 0.13 • Outliers: M92 (0.19), M15 (0.15), N188 (0.27, only 5 stars), M107 (0.28, one outlier) • Teff (pages 11-13): • Agrees well with FTS stars and Kepler simulations • Inconsistency with (J-Ks) temperatures, probably wrong reddening is used • Slight dependence on [Fe/H], but only in apVisit results

32. [α/Fe] abundances • Slight dependence on [Fe/H] but not on Teff • M13 M13 • M67 M67

33. [C/Fe] and [N/Fe] abundances • Possible nodes are in carbon: -1 to 1 with steps of 0.25 • Possible nodes are in nitrogen: 1 to 1 with steps of 0.5 • Histograms • of all parameters • from apVisit files • Noding in C • is clearly visible

34. Noding and scatter in the data • All parameters as a function of averaged S/N • Noding clearly visible at low S/N in C and N • [C/Fe] • increases as • S/N decreases • Scatter in all • metallicities is • around 0.1-0.2 • above S/N 70

35. Noding and scatter in the data • Scatter increases in M67 • as S/N decreases • although all stars • have S/N>100 • All clusters: • https://trac.sdss3.org/rawattachment/wiki/APOGEE/Publications/CalibrationClusters/cl_sn.pdf

36. [C/Fe] and [N/Fe] abundances • General agreement with literature • [N/Fe] 0.2 dex lower • [C/Fe] 0.4 dex higher • Obvious noding in • C, and N at low S/N • Possible noding in • alpha • [A/Fe] depends on • [Fe/H] in almost all • clusters

37. Interpolations tests • Tests based on 400 random models generated • [Fe/H] from -2.5 to 0.5, Teff from 4500K to 6250K, log g from 1.5 to 4.5. All fully converged models. • Two spectral windows: optical 516.5 nm to 519.5 nm, and infrared from 1509.1 nm to 1699.5 nm • Linear interpolations of model atmospheres (MA) and flux F(L) • Cubic F(CS), cubic-Bezier F(CB), quadratic-Bezier F(QB) interpolations of flux • These are compared to real flux and average differences, min and max values are calculated

38. Interpolations tests • Examples of differences between real spectra and spectra based on interpolation • 1-10% maximum errors in optical and infrared, but the average error in infrared is small than in optical

39. Interpolations tests • Average differences as a function of [Fe/H], Teff, and log g • Best interpolations are • the cubic-Bezier and • quadratic-Bezier • Linear interpolations • of model atmospheres • is not recommended

40. ASPCAP ReviewProgress report and schedulenow and ahead October 4-5, 2012

41. Performance Overall, good performance for high metallicity ([Fe/H]>-1) stars with good S/N (>70) spectra: Teff to about 3%, logg to about 0.2 dex, and [Fe/H] to better than 0.1 dex. Small systematic offsets (0.1-0.3 dex) in surface gravity. Significantly poorer performance at low metallicity, with a systematic offset in gravity of up to ~ 1 dex.

42. Issues Systematic offsets in gravity Degeneracies for metal-poor stars Noding Speed

43. Uncertainties Straight mean (and std. deviation) for abundances in M13, M5 and M67 members (apstar results) M67 M5 M13 ________________________________ <[Fe/H]> +0.01 (0.04) -1.26 (0.11) -1.51 (0.13) <[C/Fe]> -0.06 (0.08) -0.23 (0.19) -0.02 (0.36) <[N/Fe]> +0.20 (0.11) +0.50 (0.32) +0.58 (0.29) <[α/Fe]> +0.02 (0.03) +0.11 (0.10) +0.20 (0.08)

44. Uncertainties Three types of sources Random errors Atmospheric/Instrumental distortions Systematic errors Model approximations

45. Random

46. random Random and systematic due to instrumental distortions may look random Random can be studied by adding purely random noise Instrumental distortions can be studied by looking at plate repeatability

47. systematic Systematic errors in models identified looking at accurate reference values and trends

48. [Fe/H] dispersion Includes three discussed sources of error σ ~ 0.04 dex for M67 σ ~ 0.12 dex for M5 and M13 Random noise should not be dominant source except for M13 at S/N<100

49. Purely random noise Random errors are VERY small σ ~ 0.03 for S/N≥ 20 at [Fe/H]=0 σ ~ 0.1 at S/N=40 for M13 Need S/N≥80 for σ~0.04 in this cluster

50. Purely random noise Random errors important for C, and especially N in metal-poor stars Small for α/Fe