Abundances, Stellar Parameters, and Spectra From the SDSS-III/APOGEE Survey

25.2 MB·English

by  Jon A. Holtzman

Draft version January 20, 2015
PreprinttypesetusingLATEXstyleemulateapjv.5/2/11
ABUNDANCES, STELLAR PARAMETERS, AND SPECTRA FROM THE SDSS-III/APOGEE SURVEY
Jon A. Holtzman1, Matthew Shetrone 2, Jennifer A. Johnson3, Carlos Allende Prieto4,5, Friedrich Anders6,7,
Brett Andrews8, Timothy C. Beers9, Dmitry Bizyaev 10, Michael R. Blanton11, Jo Bovy12,13, Ricardo Carrera4,
Katia Cunha14,15, Daniel J. Eisenstein17, Diane Feuillet1, Peter M. Frinchaboy17, Jessica Galbraith-Frew18,
Ana E. Garc´ıa Pe´rez4, D. Anibal Garc´ıa Herna´ndez4, Sten Hasselquist1, Michael R. Hayden1, Fred R.
Hearty19, Inese Ivans18, Steven R. Majewski20, Sarah Martell21, Szabolcs Meszaros22, Demitri Muna3, David
Nidever23, Duy Cuong Nguyen24, Robert W. O’Connell20, Kaike Pan10, Marc Pinsonneault3, Annie C. Robin25,
Ricardo P. Schiavon26, Neville Shane20, Jennifer Sobeck20, Verne V. Smith14, Nicholas Troup20, David H.
Weinberg3, John C. Wilson20, W. M. Wood-Vasey8, Olga Zamora4, Gail Zasowski27
5
Draft version January 20, 2015
1
0
ABSTRACT
2
The SDSS-III/APOGEE survey operated from 2011-2014 using the APOGEE spectrograph, which

n collectshigh-resolution(R∼22,500),near-IR(1.51−1.70µm)spectrawithamultiplexing(300fiber-
a fedobjects)capability. Wedescribethesurveydataproductsthatarepubliclyavailable,whichinclude
J
catalogs with radial velocity, stellar parameters, and 15 elemental abundances for over 150,000 stars,

6 aswellasthemorethan500,000spectrafromwhichthesequantitiesarederived. Calibrationrelations
1 for the stellar parameters (T , logg, [M/H], [α/M]) and abundances (C, N, O, Na, Mg, Al, Si, S, K,
eff

Ca, Ti, V, Mn, Fe, Ni) are presented and discussed. The internal scatter of the abundances within
]
clusters indicates that abundance precision is generally between 0.05 and 0.09 dex across a broad
A
temperature range; within more limited ranges and at high S/N, it is smaller for some elemental
G
abundances. Weassesstheaccuracyoftheabundancesusingcomparisonofmeanclustermetallicities
. with literature values, APOGEE observations of the solar spectrum and of Arcturus, comparison
h
of individual star abundances with other measurements, and consideration of the locus of derived
p
parameters and abundances of the entire sample, and find that it is challenging to determine the
-
o absolute abundance scale; external accuracy may be good to 0.1-0.2 dex. Uncertainties may be larger
r atcoolertemperatures(T <4000K).Accesstothepublicdatareleaseanddataproductsisdescribed,
t eff
s and some guidance for using the data products is provided.
a
[

1 1New Mexico State University, Las Cruces, NM 88003, USA
v ([email protected],[email protected],[email protected])
0 2UniversityofTexasatAustin,McDonaldObservatory,Fort
1 Davis,TX79734,USA([email protected])
1 3Department of Astronomy, The Ohio State University,
4 Columbus, OH 43210, USA ([email protected],
[email protected])
0
4Instituto de Astrof´ısica de Canarias, 38205 La Laguna,
.
1 Tenerife,Spain
0 5Departamento de Astrof´ısica, Universidad de La Laguna,
38206LaLaguna,Tenerife,Spain([email protected],[email protected])
5
6Leibniz-Institut fur Astrophysik Potsdam (AIP), An der
1
Sternwarte16,14482,Potsdam,Germany 18PhysicsandAstronomy,UniversityofUtah,SaltLakeCity,
v: 7Laborat´orio Interinstitucional de e-Astronom´ıa (LIneA), UT84112,USA([email protected])
Xi Ru8aPGITalT. JoPsA´eCCCri,stiDnoep7a7r,tRmieondteoJfanPe´ıhroy,siRcsJ-a2n0d921A4s0t0r,onBormasyi,l van19iaDSetpaatretmUennivteorsfiAtys,tUronniovmerysitayndPaArkst,rPopAhy1s6i8c0s2,;TIhnestPiteuntnesfyolr-
University of Pittsburgh, Pittsburgh PA 15260, USA,
GravitationandtheCosmos,ThePennsylvaniaStateUniversity,
r [email protected]
a 9Dept. ofPhysicsandJINACenterforEvolutionoftheEle- UniversityPark,PA16802,USA([email protected])
20Dept. ofAstronomy,UniversityofVirginia,Charlottesville,
ments,UniversityofNotreDame,NotreDame,IN46556USA
10Apache Point Observatory, P.O. Box 59, Sunspot, NM VA22904-4325,USA(srm4n,aeg4x,[email protected])
88349-0059,USA([email protected]) 21SchoolofPhysics,UniversityofNewSouthWales,Sydney
11 CenterforCosmologyandParticlePhysics,Departmentof NSW2052
22ELTE Gothard Astrophysical Observatory, H-9704 Szom-
Physics, New York University, 4 Washington Place, New York,
bathely,SzentImrehercegst. 112,Hungary
NY10003,USA,[email protected]
12InstituteforAdvancedStudy,EinsteinDrive,Princeton,NJ 23DepartmentofAstronomy,UniversityofMichigan,AnnAr-
bor,MI48109,USA([email protected])
08540,USA,[email protected]
13JohnBahcallFellow 24DunlapInstituteforAstronomyandAstrophysics,Univer-
14National Optical Astronomy Observatories, Tucson, AZ sityofToronto,Toronto,Ontario,Canada
85719,USA([email protected],[email protected]) 25 Institut UTINAM, OSU THETA, University of Franche-
15Observat´orio Nacional, Sa˜o Cristo´v˜ao, Rio de Janeiro, Comt´e,Besan¸con,France([email protected])
Brazil 26Astrophysics Research Institute, Liverpool John Moores
16Harvard-Smithsonian Center for Astrophysics, 60 Garden University, Liverpool, L3 5RF, United Kingdom (rpschi-
St.,Cambridge,MA02138,[email protected] [email protected])
17Texas Christian University, Fort Worth, TX 76129, USA 27DepartmentofPhysicsandAstronomy,JohnsHopkinsUni-
([email protected]) versity,Baltimore,MD21218,USA([email protected])
2
1. INTRODUCTION The APOGEE spectrograph was shipped to Apache
Point Observatory in the spring of 2011 and was com-
The third phase of the Sloan Digital Sky Sur-
missioned during the period April-June of 2011. During
vey (SDSS-III) included the Apache Point Observatory
the commissioning period, it was recognized that there
Galactic Evolution Experiment (APOGEE) as one of its
was some astigmatism within the instrument, so modifi-
key components. APOGEE made use of a new, multi-
cations were made during summer 2011 to address this.
object, near-IR spectrograph to obtain spectra of more
Becauseoftheopticalissuesandmodificationsaftercom-
than 150,000 stars across portions of the Milky Way vis-
missioning, the commissioning data have lower spectral
ible from the Apache Point Observatory (APO). The
resolution that does not meet the strict survey require-
main goal of the APOGEE survey is to obtain high-
ments, especially in the detector covering the longest
resolutionspectratomapoutthekinematicalandchem-
wavelengths. Commissioning data have been released in
ical structure of the Milky Way.
the APOGEE data releases as these data provide useful
TheoveralldesignandgoalsoftheSDSS-IIIsurveyare
radialvelocityinformation,butabundanceanalysisisnot
described in Eisenstein et al. (2011) and of APOGEE in
currently provided for them because of the reduced res-
Majewskietal.(2015). Thesurveyhadaperiodofcom-
olution and quality. However, a number of the sources
missioning in spring 2011 and operated in survey-mode
observed during commissioning were reobserved during
from fall 2011 until summer 2014. Data from the first
the main survey.
yearofoperationwasreleasedaspartoftheSDSSDR10
After the summer 2011 modifications, the instrument
(Ahn et al. 2014) and included measurements of basic
was pumped down and cooled in August 2011 and re-
stellarparametersaswellasoverallmetalandα-element
mained unopened, operating continuously without any
abundances. An SDSS-III internal data release (DR11)
large changes in pressure or temperature, for the dura-
includedthefirsttwoyearsofsurveydatawiththesame
tion of the SDSS-III/APOGEE survey. Over the period
analysis as was used for DR10. The final SDSS-III data
of the main survey, the instrument met survey require-
release(DR12;Alametal.2015)includesthefull3years
ments and was remarkably stable, with essentially no
of data from the SDSS-III/APOGEE survey. For DR12,
changes in optical and detector performance.
all of the data have been reprocessed, and the parame-
At the shorter end of the wavelength range, the detec-
ter and abundance analysis have been significantly up-
tor pixels do not quite adequately sample the spectro-
dated: APOGEE DR12 includes measurements of indi-
graph point-source function. To ensure adequate spec-
vidual chemical abundances for 15 different elements.
tral sampling, the instrument includes a dither mecha-
The operation of the APOGEE instrument will con-
nismformovingtheentiredetectorarray,andexposures
tinue in the APOGEE-2 survey, which is part of the
are taken in pairs, with the detectors shifted 0.5 pixels
fourth phase of the Sloan Digital Sky Survey (SDSS-IV)
between the two observations in a pair.
that plans to operate from 2014 to 2020. APOGEE-2
One of the detectors (at the short wavelength end)
will consist of additional observations in the northern
used in the SDSS-III/APOGEE survey exhibits strong
hemisphere from APO but will also include observations
persistence, where a significant fraction of accumulated
of the southern Milky Way that will be obtained with
charge is released over a long period of time (Nidever
the 2.5m du Pont telescope at Las Campanas Observa-
et al. 2015). This affects roughly a third of the chip
tory. Future data releases for this survey may include
and is distributed in such a way that about a third of
reprocessing of APOGEE-1 data.
the fibers are affected at all wavelengths on that chip
This paper provides a general overview of the data
(1.51µm-1.58µm),whiletheothertwo-thirdsofthefibers
provided by the SDSS-III/APOGEE survey. §2 briefly
are unaffected. The behavior of the persistence is chal-
summarizes the survey. §3 and §4 summarize how the
lenging to characterize, and its relevance depends both
reduction and spectroscopic analysis is performed (but
on the brightness of a spectrum as well as the history of
described in greater detail in Nidever et al. 2015 and
previous exposures. The current reduction analysis does
Garc´ıa P´erez et al. 2015) and introduce the reader to
not attempt to correct for this; we present some infor-
the associated data products. §5 discusses the calibra-
mation on the impact on derived stellar parameters and
tionandvalidationofthederivedstellarparametersand
abundancesin§5.7. Theoffendingdetectorwasswapped
abundances. Options for accessing the data are pre-
out in summer 2014 after the completion of the SDSS-
sentedin§6. §7describestheAPOGEEcatalogdataand
III/APOGEE survey.
presentssomeimportantconsiderationsforusersofthese
data, and §8 has a similar presentation for the spectra. 2.2. Survey operations
The primary source of the light to the spectrograph
2. THESDSS-III/APOGEESURVEY
is the SDSS 2.5m telescope (Gunn et al. 2006), which
2.1. The APOGEE instrument
uses a plug-plate system to collect light from a circular
TheAPOGEEsurveyutilizesafiberspectrographthat 3-degree diameter field. For the vast majority of the
records 300 spectra simultaneously. The spectrograph observations, 230 of the fibers were placed on science
usesavolumephaseholographic(VPH)gratingandcam- targets, 35 fibers on hot star targets used for correction
era optics to deliver spectra in the H band between 1.51 of telluric absorption (although these also enable some
and1.70µmataspectralresolutionofR∼22,500. The scienceprojects),and35fiberson“blank”skyregionsto
spectraarerecordedonthreeHawaii-2RGdetectorswith enable sky subtraction.
small gaps in spectral coverage between the detectors. Standardexposuresequencesconsistof8separate500s
The instrument was constructed largely at the Univer- exposures, with 4 at each of the two dither positions, in
sityofVirginiaandisdescribedindetailinWilsonetal. an ABBAABBA pattern. The 4000s integration time
(2015). yields a S/N of about 100 per half-resolution element (a
3
typical pixel) on a source with H = 11, although the 2.4. Target selection
sensitivity varies across the spectral region recorded on
TargetselectionisdescribedindetailinZasowskietal.
the detectors, primarily as a result of grating sensitivity
(2013). The “main” sample is selected using a simple
changes.
color cut with (J −K) > 0.5, where colors are dered-
0
dened using a combination of near-IR and mid-IR pho-
2.3. 1m telescope fiber feed tometry (Majewski et al. 2011), but there are also other
targetclasses,includingcalibrationandancillaryscience
Partway through the survey, a fiber feed was con-
program targets, as discussed in more detail in Zasowski
structed from the nearby NMSU 1m telescope for 10
et al. (2013).
fibers, which are located in a fixed linear pattern in the
Formostofthestars,dataarecollectedinmultiplevis-
focal plane of that telescope. This configuration enables
its to allow detection of radial velocity variables. Stan-
observations of single objects, with accompanying sky
dard survey targets are observed in multiple visits until
spectra,andhasbeenusedtoprovideasampleofmostly
a S/N of 100 per half-resolution element is achieved for
bright stars, useful for both calibration and science, as
a target of H=12.2. A minimum of three visits is re-
discussedin§5.5. Tousethe1mobservationstovalidate
quired for most targets, with restrictions on the cadence
those obtained with the 2.5m, the spectra from the two
so that a broad range of RV variables can be detected.
telescopesmustmatchclosely. Theinputbeamfromthe
However, survey software tracks accumulated S/N, and
2.5m telescope is f/5, while that of the 1m telescope is
additionalvisitsareaddedasneededtoachievetheS/N
f/6. However, the fiber run from the 1m telescope adds
requirement. Somefaintertargets(mediumandlongco-
significant fiber to the standard 2.5m configuration and
horts; see Zasowski et al. 2013) were observed for more
includes several additional couplings, so the focal ratio
than three visits. A subset of survey targets towards the
degradation is expected to be a bit worse for the 1m
Galactic Bulge was observed only in single visits (with a
feed, which goes in the direction of making the beam
brighter limit for sample selection), because of the chal-
more closely match that obtained with the 2.5m.
lengeassociatedwithobservingsuchtargetsfromApache
Point Observatory, where it is always at a high airmass.
2.5. Scope of APOGEE data releases
The APOGEE data in DR10 (Ahn et al. 2014) in-
cluded the first year of APOGEE observations. The
SDSS/APOGEE DR11 release added the second year of
data, but was initially only released internally to SDSS
collaborationmembers;itisincludedinthepublicDR12.
Calibration of the DR10 and DR11 data is described in
M´esz´aros et al. (2013).
The SDSS DR12 (Alam et al. 2015) includes all data
taken with the APOGEE instrument between April
2011 and July 2014. A total of 163,281 stars are in-
cluded. Of these, 151,141 stars were observed during
the main survey period that provided survey-quality
spectra. Hot stars for telluric correction account for
15,343 of the survey-quality targets, and 17,116 of all
targets (including commissioning data). Of the 135,798
survey-quality non-telluric sources, 110,087 come from
the “main-survey” sample, which does not include spe-
cialtargets,targetsfromancillaryscienceprograms,and
stars observed with the NMSU 1m (883 stars).
3. BASICDATAREDUCTIONANDASSOCIATEDDATA
PRODUCTS
The basic data reduction procedure is described in
Figure 1. Comparison of sky emission lines as taken with the Nidever et al. (2015). We provide a brief synopsis as
2.5mand1mtelescopes. an introduction to the data products that are included
in the data releases and described here.
Figure 1 shows the profile of two bright sky lines, one The raw data consist of data cubes for each individ-
inthelongwavelengthchip(chip0)andoneintheshort ual exposure, where each layer of the cube is a read of
wavelength chip (chip 2), for several 1m and 2.5m ob- the three detectors every 10.7 seconds. As a result, the
servations. This demonstrates that the line profiles as cubes are large, with a standard 500s exposure consist-
obtained with APOGEE from the two telescopes match ing of 47 reads of 3 detectors, with each detector having
extremely well. Comparison of spectra obtained for the 2560x2048 pixels, since a “reference” array (intended to
samestarfrombothtelescopesalsoshowsthesamegood help with tracking bias variations) of 512x2048 pixels is
match, although the different S/N of the spectra make included. The raw data cubes are compressed using the
detailed comparison difficult. Both sky and stellar spec- standard FPACK routine (Seaman et al. 2010), but, to
trasuggestthattheNMSU1mdeliversextremelysimilar provide maximum compression, the raw data are con-
spectra to those delivered by the 2.5m telescope. verted to a series of differences between adjacent reads
4
before compression. The resulting files are saved as apR Stellarparametersandindividualchemicalabundances
files. are derived from the combined APOGEE spectra with
The reduction procedure reduces these image cubes the APOGEE Stellar Parameters and Chemical Abun-
to individual 2D images. For each read, dark current dances Pipeline (ASPCAP), which is described in detail
is subtracted using a calibration product constructed in Garc´ıa P´erez et al. (2015). The basic idea is that a
from multiple dark images. Up-the-ramp sampling is grid of synthetic spectra is searched (with interpolation
then used to derive count rates that are less affected by inthegrid)tofindthebestmatchtoeachobservedspec-
readout noise than a simple doubly-correlated sampling trum, andtheparametersofthebestmatchareadopted
would provide. The resulting image is flat fielded using as the best parameters for that star.
acalibrationproductconstructedfrommultipleflat-field
4.1. Synthetic spectra
images and is stored in an ap2D file.
Spectra are optimally extracted from the 2D images Central to ASPCAP is the construction of a synthetic
using an empirical PSF derived from an exposure of il- spectralgridthatcoverstherangeoffundamentalstellar
luminated telescope mirror covers taken after the sci- parametersoverwhichmainsurveystarsareexpectedto
enceexposuresequencehasbeencompleted. Anapprox- befound. Giventhesignificanceandfrequencyofmolec-
imate relative flux correction is also applied. These ex- ular features — in particular, from CO, CN, and OH
tracted, flux-calibrated spectra are saved as ap1D files, — in the region of the H-band where APOGEE spec-
and the 2D models used for the extraction are saved as tra are recorded, the dimensions of this grid include not
ap2Dmodel files. only the usual effective temperature (T ), surface grav-
eff
Themultipleexposures(ap1D files)arethencombined ity (logg), microturbulent velocity (v ), and overall
micro
to make an individual visit spectrum. To do this, the scaled-solar chemical abundance ([M/H]), but also the
relative shifts between the different exposures, arising carbon ([C/M]), nitrogen ([N/M]), and α-element abun-
from the motion of the detector dither mechanism used dances ([α/M], which includes oxygen) relative to the
to provide better sampling, are determined. Position- solar abundance ratio. The resulting 7D grid comprises
dependentskysubtractionisperformedonindividualex- several million individual synthetic spectra.
posures, as is a correction for telluric absorption. Expo- The fidelity of the synthetic spectral library is criti-
sures at different dither positions are paired, matching cal to the success of the method. The structure of the
as best as possible exposures of comparable S/N, and underlying stellar atmospheres were computed with the
combined into a spectrum with a sampling correspond- ATLAS9 code (Kurucz 1979). For DR10/DR11, a grid
ing to half the original pixel sampling. The combination ofatmosphereswasusedthatadoptedsolar-scaledabun-
technique reduces to simple interleaving (after adjusting dances (i.e., the carbon, nitrogren, and α abundances
the levels of the two exposures to match) in the case were varied only in the spectral synthesis, but not in
of perfect 0.5 pixel dithers but properly handles dithers theunderlyingatmosphericmodelstructure). ForDR12,
that are imperfect. The combined pairs are then coad- a self-consistent set of atmospheres were used: carbon
ded to provide the final visit spectra. An approximate and α abundances were varied across the grid for the
absolute flux calibration is made to the spectrum using atmosphere calculation (the nitrogen abundance has lit-
the 2MASS H magnitude. An initial measurement of tle effect on the structure of the atmosphere, so this
the radial velocity for each star in the visit is made by was not varied). Details are given in M´esz´aros et al.
cross correlating each spectrum with the best match in (2012). These calculations assume plane-parallel atmo-
a template library, and these are stored against the visit spheres (but see Zamora et al. 2015 for some discussion
spectra in apVisit files. ofthechangesresultingfromusingasphericalgeometry)
All of the visits are then combined after resampling in local thermodynamic equilibrium (LTE).
them to a common, log-lambda wavelength scale; in all The synthetic spectra were generated using the LTE-
data products, the wavelength scale is in vacuum wave- 1DbranchoftheASS(cid:15)Tsynthesiscode(Koesterke2009).
lengths. Relative radial velocities of each visit are itera- AcustomlinelistwascompiledasdiscussedinShetrone
tively refined by cross-correlation of each visit spectrum et al. (2015). The atomic and molecular data for the
withthecombinedspectrum(whichshouldprovideaper- line list were taken from various literature sources, but
fect template match). The absolute radial velocity scale the atomic data transition probabilities were adjusted,
is then set by cross-correlating the combined spectrum within limits, by matching synthetic spectra for the Sun
with the best match in a template library. The result- andArcturuswithavailableobservedhigh-resolutionat-
ing combined spectrum, as well as the resampled visit lases (Livingston & Wallace 1991; Hinkle et al. 1995) as
spectra, are stored in apStar files. describedinShetroneetal.(2015). Thesolarabundances
A summary of the spectral data products is presented assumed for these adjustments are those from Asplund
in Table 1. The DR12 Science Archive Server (http: etal.(2009)andAsplundetal.(2005)(forDR10/11and
//data.sdss3.org/sas/dr12) makes both raw data, as DR12, respectively) and those for Arcturus from consid-
well as various stages of reduced data, available to the eration of a number of sources; the adopted values for
community, as discussed in §6. More details on the files thevariousAPOGEElinelistsarepresentedinShetrone
are presented in §8. et al. (2015). As described in Shetrone et al. (2015),
Thereductionpipelinesoftwareisversion-controlledin thelinelisthasevolved, andfuturedevelopmentislikely.
the SDSS-III software repository. Additional details on the spectral grids and a compari-
son of the ATLAS9/ASS(cid:15)T synthetic spectra with those
from other atmospheres and syntheses are discussed in
4. SPECTROSCOPICANALYSISOFAPOGEEDATAAND Zamora et al. (2015).
ASSOCIATEDDATACATALOGS The high-resolution synthetic spectra are then
5
Table 1
APOGEESpectraldataproducts
Name Contents
apR-[abc]-ID8.apz Rawdatacubes(2560x2048x47),APOGEEcompressedformat
ap2D-[abc]-ID8.fits 2D(2048x2048)darksubtracted,flatfielded
ap1D-[abc]-ID8.fits 1Dextracted(2048x300),roughwavelengthcalibration(notzero-pointedusingsky)
apCframe-[abc]-ID8.fits 1Dspectrawithskysubtraction,telluriccorrection,wavelengthzeropointadjustment
apVisit-APRED VERS-PLATE-MJD-FIBER.fits Individualvisitspectra,dither-combined,butotherwisenativepixelscale,
oneperobjectvisit
apStar-APRED VERS-APOGEE ID.fits Spectraresampledtoconstant∆logλ,alongwithcombinedspectrum,oneperobject
aspcapStar-RESULTS VERS-APOGEE ID.fits Normalizedspectratomatchthoseofspectralgrids,
withbest-matchinggridspectra,oneperobject
matched to the APOGEE resolution and pixel sam-
pling. This is accomplished by smoothing the high- Table 2
Syntheticspectralgridparametervalues
resolution spectrum with the line-spread function (LSF)
of APOGEE data. However, the APOGEE LSF varies
gridname Dimension Range Stepsize
across the detector as a consequence of the optical de-
sign. To some extent, this variation is reduced when GKgrid Teff 3500to6500 250
multiple visits are combined, since stars will generally logg 0to5 0.5
land in a different location on the detector (from a dif- logvmicro -0.301to0.903 0.301
[M/H] -2.5to0.5 0.5
ferentfiberinthetelescopefocalplane)indifferentvisits. [C/M] -1to1 0.25
In DR10, a Gaussian LSF with R=22500 was adopted, [N/M] -1to1 0.5
but we subsequently recognized that the APOGEE LSF [α/M] -1to1 0.25
Fgrid T 5500to8000 250
is not well-represented by a Gaussian. For DR12 we eff
logg 0to5 0.5
have taken a approach of using an LSF made by av- logvmicro -0.301to0.903 0.301
eraging five different empirically-measured LSFs across [M/H] -2.5to0.5 0.5
the detector to make a single library for use with all of [C/M] -1to1 0.25
[N/M] -1to1 0.5
the SDSS-III/APOGEE spectra. As demonstrated be-
[α/M] -1to1 0.25
low, this yields results that are validated by observa-
tions of objects of known abundances, but it is an area
for potential improvement in subsequent data releases. result that provides the better match. In addition, the
Garc´ıa P´erez et al. (2015) present a more detailed inves- librariesarecompressedusingaprincipalcomponentsde-
tigationofthepotentialimpactofassuminganimperfect composition, in which we split the spectra into 30 pieces
LSF. and PCA-compress each of those, keeping only the first
In addition to smoothing the high-resolution synthetic 30 principal components and the amplitudes of these for
spectrawithanAPOGEELSF,inDR12wealsoinclude each location in the grid; additional details are provided
broadeningtoaccountformacroturbulentvelocityinthe in Garc´ıa P´erez et al. (2015).
stellar atmosphere. We were motivated to do this not The range and spacing of the ATLAS9/ASS(cid:15)T grid
only because it is a known physical phenomenon, but parameters used for DR12 are given in Table 2 (see also
also because this additional broadening brings our de- Zamora et al. 2015). Garc´ıa P´erez et al. (2015) discuss
rived surface gravities closer to those suggested by other why this grid spacing is adequate and how interpolation
techniques (see below). For DR12, we have adopted a within the grid is performed.
simple scheme of broadening with a Gaussian of FWHM We note that for SDSS-III/APOGEE, stellar rotation
6 km/s for the macroturbulent velocity, motivated by is not included in the synthetic spectral grids. For stars
typicalvaluesfromCarneyetal.(2008). Adoptingadif- with rotational velocities (≥ 5 km/s) that broaden the
ferent functional form, along with possible variations in lines significantly beyond the instrument resolution, the
macroturbulentvelocityasafunctionofotherstellarpa- libraries will not be able to obtain a good match to the
rameters, is another area for potential improvement in observed spectra, and the resulting stellar parameters
the future. canbeaffectedsystematically. Stellarrotationisnotex-
The synthetic spectra are calculated relative to a true pected to be significant for most of the red giants that
continuum. However, it is challenging, if not impossi- make up the APOGEE sample. However, a significant
ble, to identify a true continuum in the observed spectra fraction of the dwarfs observed in the survey may have
in cool, metal-rich stars. To account for this, both the detectablerotation,andresultsforthesearesuspect. As
synthetic spectra and the observed spectra are “pseudo- discussed below, we do not provide calibrated parame-
continuum”normalizedusingthesamealgorithm,asdis- ters for dwarfs, partly because of the lack of treatment
cussed in Garc´ıa P´erez et al. (2015). ofrotation. Forallsurveystars,weprovideaflagthatin-
Thesmoothed,resampled,andpseudo-continuumnor- dicates whether rotation is likely to be important, based
malizedspectraarebundledintoalargelibrary. Tomin- onacomparisonofthewidthoftheautocorrelationfunc-
imize the (substantial) computing resources required for tion of the spectrum relative to the autocorrelation of
such a large grid, we separate the grid into two grids the best-matching RV template. Future improvements
overlapping in temperature (which we denote GK and are planned with the inclusion of rotation when needed.
F); we find the best match in each grid, and use the We also note that the current analysis is limited to
6
[M/H] C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, and
Ni — that have features in the APOGEE wavelength
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5
4 coverage. For each element, the regions in the spectrum
that are sensitive to the abundance of that element, and
lesssensitivetotheabundanceofotherelements,arede-
3
termined from model spectra. The spectral “windows”
)
s identified in this way are weighted by their relative sen-
m/
sitivitiesandalsobyhowwellasyntheticspectrumwith
k 2
(o the parameters appropriate to Arcuturus match those of
micr an observed Arcturus spectrum. Details of this proce-
v
1 dure, and the specific locations of the derived windows,
are discussed in Garc´ıa P´erez et al. (2015).
Given the windows for each element, each spectrum
0
is then run through the FERRE search code separately
0 1 2 3 4 5
for each element, using the same grid as used for the
log g
determination of the stellar parameters. However, for
the individual element abundances, the search is done
holding the stellar parameters T , logg, v fixed,
Figure 2. Microturbulencerelation,asderivedfroma7Drunof eff micro
andjustvaryingasinglemetallicityparameter,sothisis
thecalibrationsubsample.
a one-dimensional search. For C and N, the [C/M] and
stars with T > 3500 K because the Kurucz grid of [N/M] dimension is varied, for the α elements (O, Mg,
eff
model atmospheres does not extend to cooler temper- Si, S, Ca, and Ti), the [α/M] dimension is varied, while
atures. Subsequent work is planned to extend results to for all others (Na, Al, K, V, Mn, Fe, Ni), the [M/H]
cooler temperatures using MARCS model atmospheres dimension is varied. Apart from C and N, this means
and the Turbospectrum synthesis code (Zamora et al. that more than just the single element abundance being
2015). soughtisbeingvaried,butASPCAPattemptstoaccount
forthatbyusingwindowswithmaximalsensitivitytothe
4.2. Deriving stellar parameters and abundances element being derived and minimal sensitivity to other
abundances. This allows us to avoid having to create
For each APOGEE spectrum, a best match within
a series of 15 different 7D grids where pure individual
the synthetic grid is found. To do this, interpolation
element variations are calculated.
within the grid, as well as an efficient search method,
are required. This is accomplished using the FOR- 4.3. FERRE output
TRAN95 code FERRE (Allende Prieto et al. 2006)281,
The full ASPCAP pipeline produces an initial set of
which yields a best set of stellar parameters. The com-
six parameters for each star from the initial 6D fit, plus
putational resources to perform the full 7D search for
15additionalabundancemeasurements. Therawoutput
> 100,000 stars are substantial (> 50,000 CPU hours).
from the 6D fit are stored in an array called FPARAM,
Because of this, we use a calibration subsample (see §5)
while the output from the 15 separate 1D “window” fits
to derive a relation for the microturbulent velocity as a
are stored in an array called FELEM.
function of surface gravity. Figure 2 shows the derived
FERRE also produces a covariance matrix from the
microtubulent velocity as a function of surface gravity
initial 6D fit, which we store in a matrix called
forthecalibrationsubsample,color-codedbymetallicity.
FPARAM COVAR, and individual abundance uncer-
The stars of higher metallicity that make up the bulk of
tainty estimates, which we store in a FELEM ERR ar-
the APOGEE sample (but not the calibration sample)
ray. Unfortunately,wehavefoundthattheformalerrors
define a fairly tight sequence. We a fit a linear relation
returned by FERRE seem to be significantly underesti-
to the subset of stars from these data with logg< 3.8
mated when they are calculated using standard fit tech-
and[M/H]>−1andderivedthefitthatwasadoptedfor
niques(i.e.,inverseofthecurvaturematrix);thisisplau-
DR12:
sibly caused because systematic errors (e.g., in the syn-
v =2.478−0.325logg, (1)
micro theticcalculations,LSF-matching,etc.) dominatestatis-
which is shown as a solid line in Figure 2. We then use ticalones(seeGarc´ıaP´erezetal.2015formoredetails).
this relation to construct a 6D grid, interpolating within Asaresult,wealsodetermineandstoreamoreempirical
the 7D grid to the microturbulent velocity appropriate estimate of uncertainties, as discussed below.
for the surface gravity of each grid point. The utility of Foreachparameterandelementalabundance,westore
thisisthatitreducesthesearchtoa6Done,ratherthan abitmaskflaggingpossibleconditionsencounteredinthe
7D, with a corresponding reduction in the time required process, such as a solution falling near the edge of the
(by a factor of three or more) to analyze the full sample. grid, and consequently deemed to be less reliable.
The full DR12 sample is then analyzed using the 6D The data products are discussed in more detail in §6.
grid to provide the initial set of parameters: T , logg, TheASPCAPsoftwareisversioncontrolledintheSDSS-
eff
[M/H], [C/M], [N/M], and [α/M]. III software repository in the speclib, idlwrap and ferre
Given the stellar parameters, the spectra are then an- products.
alyzedforabundancesofeachof15differentelements—
4.4. Versions and data releases
1 http://www.sdss3.org/svn/repo/apogee/aspcap/ferre, There are four main stages of APOGEE data
http://hebe.as.utexas.edu/ferre processing: visit reduction, star combination, ASP-
7
[M/H] The calibrated temperature provided for all giants
-2.1 -1.7 -1.4 -1.0 -0.6 -0.2 0.2 in the DR12 release uses the calibration from equa-
600
tion 2. For giants with effective temperatures out-
400
side the range of those used to derive the calibration
M 200 (3500 <T < 6000), the correction at the edge of the
RF eff
ASPCAP-I-2000 rbaenegneswetasinadthoeptPeAd,RaAnMdFthLeACGAfLoRrAteNmGpEeraWtuArReN(sebeit§h7a).s
An upper limit on the temperature uncertainty is de-
-400 rived from the scatter around this relation, which gives
-600 σ(T ) = 91.5 K. This is an upper limit because there
6000 5500 5000 4500 4000 eff
ASPCAP temp areuncertaintiesassociatedwiththephotometry. Onthe
other hand, they may be some systematic uncertainties
related with the photometric temperature relation. This
Figure 3. Temperature calibration, derived from low-reddening
samplewithphotometrictemperatures. scatter around the calibration relation was adopted as
theempiricaltemperatureuncertaintyforstarsinDR12.
CAP/FERRE processing, and final calibration. The Thecolor-temperaturerelationofGonz´alezHern´andez
software is set up such that these four stages can be & Bonifacio (2009) is also used to flag objects that may
runwithindependentsoftwareversions/configurationsso havepoorstellarparameters. Foreachobject,atemper-
that, for example, a later stage can be run without hav- atureisdeterminedusingthecolor-temperaturerelation,
ingtorerunanearlierstagewithanewsoftwareversion. the2MASSJ−K color, andtheestimateoftheredden-
The output from the four stages is saved in a series of ingusedbythetargetingpipeline. Thisiscomparedwith
nested subdirectories, as described in §6. The names of the ASPCAP-derived temperature. When these deviate
the different versions are tied to software version num- by more than 1000 K, the COLORTE WARN bit is set
bers of the different products. Table 3 gives the names in the ASPCAPFLAG (see §7); if the temperature differ
and versions used for the APOGEE data releases. bymorethan2000K,theCOLORTE BADbitissetand
the ASPCAP results are flagged as bad. Most of these
5. CALIBRATIONANDVALIDATIONOFDR12 are cool dwarfs that are not fit well (in retrospect, we
PARAMETERSANDABUNDANCES might have chosen less conservative values to flag).
Resultsforthestellarparametersandabundanceshave
5.2. Surface gravities
been calibrated and validated based on observations of
a calibration sample. This sample includes stars in the Surface gravity is a key parameter for a number of
Kepler field that have well-characterized effective tem- reasons: (1) it is important for estimating distances to
peratures and surface gravities, samples of stars in a set survey stars, (2) it can be used to identify AGB stars,
of globular and open clusters that span a wide range of and (3) it can have a significant effect on line strengths.
metallicity, and a set of stars with previously-measured The APOGEE survey includes observations of giants
parameters and abundances. thathavetimeseriesphotometrywithKepler thatallows
asteroseismic analysis. In principle, such analysis very
5.1. Effective temperatures accuratesurfacegravities,withanestimateduncertainty
of <<0.05 in logg (Hekker et al. 2013).
We compare the ASPCAP spectroscopically-
A calibration relation for surface gravity has been de-
determined temperatures with those obtained from the
rived using a subset of stars from version 7.3 of the
calibrated photometric relation of Gonz´alez Hern´andez
APOKASC catalog (Pinsonneault et al. 2014). To cover
& Bonifacio (2009). We chose a sample of giants (logg<
a wide range of properties, we took all stars that had in-
3.8) in the Kepler field and adopted E(B-V) estimates
ferrable evolutionary states from Stello et al. (2013), all
asdeterminedfromtheSAGAsurvey(Casagrandeetal.
stars with logg<2, and all of the small number of stars
2014). We convert E(B−V) to E(J −K) using
in the APOKASC catalog with [M/H] <-1.
E(J −K)=(0.56+0.06(J −K) )∗E(B−V) Figure 4 shows the difference between ASPCAP spec-
0
troscopy logg and the asteroseismic logg from Pinson-
(Besselletal.1998). WeusethedereddenedJ−K color neault et al. (2014), as a function of surface gravity. It
to derive the temperature, and adopt the raw ASPCAP is clear that results for stars at higher surface gravity
metallicity for the color-temperature relation. We re- (logg ≥ 4) are poor. We expect that this is caused to a
strict the comparison to stars with ASPCAP metallicity largeextentbythelackoftreatmentofrotationdiscussed
[M/H]> −1. Figure 3 shows the difference between the above.
spectroscopicandphotometrictemperaturesforthissub- At lower surface gravities, there are systematic offsets
sample. This comparison suggests that there is an offset betweengravitiesdeterminedforRGBandRCstars;the
in the derived effective temperature in that the raw AS- difference between ASPCAP and asteroseismic gravities
PCAP temperature is about 90 K cooler than the pho- islargerforRCstars,evenforRGB/RCpairsatthesame
tometric temperatures. gravity and metallicity. This has already been noted in
Wederivedacalibrationbasedonalinearfitwithtem- previous APOGEE samples (Pinsonneault et al. 2014;
perature to these data, which yields: M´esz´aros et al. 2013), but the effect is still currently not
well understood. If one considers just the RGB sam-
T =T −0.0034(T −4500)+87.9 (2)
eff,corr eff,raw eff,raw ple,itappearsthatthedifferencebetweenASPCAPand
although the dependence of the difference on tempera- asteroseismicgravityincreasestolowergravities. Node-
ture is negligibly small. pendence on metallicity was found.
8
Table 3
Softwareversionandproductnames
Datarelease APRED VERS(apogeereduce) APSTAR VERS(apogeereduce) ASPCAP VERS(idlwrap) RESULTS VERS(idlwrap)
DR10 r3(v2 11) s3(v2 11) a3(v7) v304(v15)
DR11 r4(v2 14) s4(v2 15) a4(v16) v402(-)
DR12 r5(v3 04) stars(v3 10) l25 6d(v22) v603(v27)
1.0
Table 4
Calibrationclusters
P log g - Kepler log g 00..05 MMMc915lu253ster Literature[F---e222/...H330536] HHHaaarrrrrriiisss(((111999999666R)))eference
A
C N5466 -2.01
P
AS-0.5 N4147 -1.78 Harris(1996)
M2 -1.66 Harris(1996)
M13 -1.58 Harris(1996)
M3 -1.50 Harris(1996)
-1.0
0 1 2 3 4 5 M5 -1.33 Harris(1996)
ASPCAP log g M107 -1.03 Harris(1996)
M71 -0.82 Harris(1996)
Figure 4. Surface gravity calibration, derived from a sample of
Be29 -0.44 Carraroetal.(2004)
stars in the Kepler field with gravities from asteroseismic analy-
N2243 -0.35 Jacobsonetal.(2011a)
sis. This analysis also provides information on the evolutionary
M35 -0.14 BarradoyNavascu´esetal.(2001)
state: red points are hydrogen shell burning (RGB) stars, while
N2420 -0.13 Jacobsonetal.(2011b)
bluepointsareheliumcoreburning(RC)stars.
Pleiades 0.03 Soderblometal.(2009)
We have chosen to adopt a surface gravity calibration N188 0.04 Jacobsonetal.(2011b)
using a linear surface gravity fit to the RGB sample. If N2158 0.04 Jacobsonetal.(2009)
M67 0.06 Jacobsonetal.(2011b)
one assumes that the asteroseismic gravities are correct,
N7789 0.09 Jacobsonetal.(2011b)
this would imply that the RC stars in the DR12 sample N6819 0.16 Bragagliaetal.(2001)
willhavecalibratedsurfacegravitiesthataretoolargeby N6791 0.37 Carraroetal.(2006)
∼0.2inlogg. Wehavechosenthisapproachratherthan
“splittingthedifference”betweentheRGBandRCsam- of specific elements. We choose to do this because (1)
ples because it is possible to distinguish the bulk of the using calibrated parameters would lead to significantly
RC stars from RGB stars based on cuts in temperature pooreroverallspectrumfits,and(2)itispossiblethatthe
and surface gravity (see Bovy et al. 2014). Because RC raw,“spectroscopic,”parametersaccounttosomeextent
stars are expected to have well-constrained luminosities, foruncertaintiesandapproximationsthatareimplicitin
it is possible to use these for distance estimates (Bovy the synthetic spectra, and so using the raw parameters
et al. 2014) instead of needing to rely on the ASPCAP provides a more self-consistent set of abundances.
surfacegravities. Wethuschoosetoadoptthecalibration On the other hand, it is also possible that the raw pa-
relation that we hope will give the best results for RGB rameters are offset for other reasons, for example, if the
stars, and recommend treating the RC stars separately. adopted LSF leads to incorrect surface gravities. If this
The derived calibration relation is given by: were the case, then one might argue that using the cal-
ibrated parameters for individual elemental abundances
might be more accurate, at least for features where the
logg =logg +0.14logg −0.588. (3)
corr raw raw strength depends strongly on surface gravity. Future
For uncertainties in logg, we adopt the observed scat- work is planned to investigate this.
ter around this calibration relation, again ignoring the
RC stars. This gives an empirical uncertainty of 0.11 in 5.4. Chemical Abundances
logg. However, the true error is probably more likely Ourprimarycalibrationsampleforabundancesisaset
to be dominated by systematics, given the Kepler differ- of globular and open clusters spanning a wide range of
ences. We have also determined that the surface gravity metallicity,fromM92andM15onthemetal-poorendto
issensitivetothedetailsoftheLSFusedintheanalysis, NGC 6791 on the metal-rich end. The set of calibration
so this may lead to systematics depending on the exact clusters is presented in Table 4.
LSF combination of each spectrum. We have approached calibration in two steps: an in-
ternal calibration where we look for homogeneity of ele-
5.3. Calibrated parameters and abundance mental abundances withinclusters, andanexternal cali-
determination bration where we investigate the agreement of the abun-
Ourproceduretodetermineelementalabundancesuses dances with previously measured values.
therawderivedstellarparameterswhenfittingsynthetic
5.4.1. Internal calibration
spectratothewindowsinthespectrathatcoverfeatures
9
For the internal calibration, we examine the ASPCAP
abundancesundertheassumptionthatclustersarechem- Table 5
Internaltemperature
ically homogeneous for all elements except carbon and
calibrationslopes
nitrogen, whichareexpectedtohavevariationsingiants
because of potential mixing of CNO-processed material.
Element slopeper1000K
Thesamplewasrestrictedtomoremetal-richclustersbe-
cause (1) multiple populations in some globular clusters C ···
N ···
are manifest in elemental abundances (e.g., Al, Mg, Na,
O +0.007
and O), and our cluster sample is dominated by globu- Na -0.001
lar clusters at [M/H]< -1; (2) at lower metallicity, the Mg -0.051
Al -0.046
lines are weaker, so trends are more challenging to dis-
Si -0.121
cern; and (3) the bulk of the main APOGEE sample is S +0.060
at higher metallicity, so we choose to provide an inter- K -0.046
nal calibration that is most accurate for the bulk of the Ca +0.059
Ti -0.162
sample. In practice, we inspected the spread of each in-
V -0.177
dividual element within clusters of various metallicities, Mn -0.206
andchosealowerlimitforeachelementtouseforthein- Fe -0.055
ternalcalibration;thislimitwasalwaysbetween[M/H]of Ni -0.124
[M/H] -0.056
-1 and -0.6.
[α/M] -0.028
For the internal calibration, we look for agreement
amongtheclusterstars,withoutanyconstraintsonwhat
the mean abundances are. Inspection of the raw ASP- We apply the temperature corrections as derived from
CAP abundances within each cluster suggests that the the cluster sample to the DR12 sample. Because the
abundancesofsomeelementsappeartohavesmalltrends clusters only provide stars with 3800 <Teff< 5250K, we
witheffectivetemperature. Toquantifythiseffectwefit, do not extrapolate the linear relation outside this range.
for each element, for mean abundances of each cluster Instead, we use the correction at Teff = 5250K for all
plus a linear temperature dependence of the abundance: stars with Teff > 5250K, and the correction at Teff =
3800K for all stars with T <3800K. For those giants
eff
outside the temperature range of the calibrator, we set
[X /H]=[X/H]+S (T −4500)/1000. (4)
ij i X ij the CALRANGE WARN bit in the abundance masks.
where i represents the cluster, and j the star within the In addition, because the calibration is derived only
cluster, and S the derived slope for element X. Note from giants, and because of other issues noted above for
X
that we used [X/H] for all abundances: for the elements dwarfs, we do not apply it to stars with logg >3.8. For
forwhichASPCAPusesagriddimensionthatisrelative thesehighersurfacegravitystars, wedonotprovidecal-
to the overall metal abundance (C, N, and α elements), ibrated abundances at all (the values are set to -9999)
we added the ASPCAP [M/H] to get [X/H]. This made and we set the CALRANGE BAD bit in the abundance
iteasiertodetectpotentialclusternon-membersasthose masks. The raw ASPCAP parameters are still provided.
stars with abundances significantly different from the
clusteraverages. Weattemptedtobeconservativeabout 5.4.2. Empirical abundance precision
clustermembership: westarted withstarsselectedtobe The individual abundances of stars in clusters provide
probableclustermembers(seeZasowskietal.2013),but anexcellentdatasettoassesstheprecisionofourderived
then adopted only stars with radial velocities within 5 abundances, by using the scatter within clusters as an
km/s of our cluster mean, and then finally rejected stars estimate of the abundance uncertainties. This scatter is
withabundanceresidualsmorethan0.2dexfromthefit. a function of both temperature and metallicity, which is
Results are shown in Figure 5, where abundances of asexpectedbecauselinesareweakeratlowermetallicity,
each cluster calibration star are shown relative to the and change strength with temperature. In addition, we
derived mean ([Xi/H]) of the cluster of which it is a seethatscatterdecreaseswithincreasingsignal-to-noise.
member. Points are color-coded by the metallicity of To derive empirical uncertainties, we calculated the
their respective cluster. The small temperature trends spread of the abundance within each cluster in bins of
could arise from a number of different factors: the as- width 250K in temperature and 50 in S/N (putting all
sumption ofLTE,issues with atomicdata sincedifferent observations with S/N> 200 into the highest S/N bin).
lines provide information about abundances at different To achieve a greater range in S/N, we included abun-
temperatures, etc. The data suggest that a linear tem- dance measurements for cluster stars derived from in-
peraturetermisadequatetodescribethetrendsformost dividual visits in addition to those from the combined
elements. spectra. We combined results for all clusters in bins of
For DR12, we have adopted the derived temperature 0.5 dex in metallicity, and then fit a simple functional
trends for an internal calibration of the abundances for form to the uncertainty in each element:
all elements except carbon and nitrogen. The slopes of
these temperature terms (S ) are given in Table 5. We
X
arbitrarily define the abundance of a given element as ln(σ )=A +B (T −4500)+
X X X eff
the abundance at T = 4500 K. While is it possible that
eff C [M/H]+D (S/N −100) (5)
thederivedabundancesofcarbonandnitrogenmayhave X X
temperaturedependencies, wecannotdiscernthesefrom wherewefitforthenaturallogtoensurethattheuncer-
the cluster data internally. tainty never goes negative (i.e., outside of the range of
10
0.4 O 0.4 Na
H] H]
O/ 0.0 Na/ 0.0
[ [
-0.4 -0.4
0.4 Mg 0.4 Al
H] H]
Mg/ 0.0 Al/ 0.0
[ [
-0.4 -0.4
0.4 Si 0.4 S
H] H]
Si/ 0.0 S/ 0.0
[ [
-0.4 -0.4
0.4 K 0.4 Ca
H] H]
K/ 0.0 Ca/ 0.0
[ [
-0.4 -0.4
0.4 Ti 0.4 V
H] H]
Ti/ 0.0 V/ 0.0
[ [
-0.4 -0.4
0.4 Mn 0.4 Fe
H] H]
n/ 0.0 e/ 0.0
M F
[ [
-0.4 -0.4
0.4 Ni 0.4 M
H] H]
Ni/ 0.0 M/ 0.0
[ [
-0.4 -0.4
α 4000 4500 5000
0.4
Temperature
H]
0.0
α/ Be29 N188 N6791
[
M67 N2158 N6819
-0.4
M71 N2420 N7789
4000 4500 5000
Temperature
Figure 5. Internal elemental abundance calibrations. Plots show deviations of individual elements relative to the cluster means as
a function of temperature; different symbols are for different clusters, as noted in lower right. Lines shows the adopted temperature
calibrations. Carbonandnitrogenarenotshownbecausewecannotassumehomogeneousabundanceswithinclusters.