1. APOGEE-2 and Data Infrastructure Jon Holtzman (NMSU) APOGEE team (Steve Majewski, PI)

2. 22 Top Level Science Requirements First large scale, systematic, uniform spectroscopic study of all major Galactic stellar populations to understand: chemical evolution at precision, multi-element level (including preferred, most common metals CNO) tightly constrain GCE and dynamical models (bulge, disk, halo) access typically ignored, dust-obscured populations grey areas of map have A V > 1

3. 33 Top Level Science Requirements First large scale, systematic, uniform spectroscopic study of all major Galactic stellar populations to understand: chemical evolution at precision, multi-element level (including preferred, most common metals CNO) tightly constrain GCE and dynamical models (bulge, disk, halo) access typically ignored, dust-obscured populations Galactic dynamics/substructure with very precise velocities order of magnitude leaps: ~2-3 orders larger sample than previous high-R GCE surveys ~2 orders more high S/N, high R near-IR spectra ever taken

4. APOGEE Spectrograph Garrett Ebelke, Mike Skrutskie, John Wilson & Fred Hearty with opened APOGEE-2N. By G. Damke. APOGEE: cryogenic mulit-object, near-IR, fiber-fed, high resolution spectrograph (-->John Wilson, UVa) 300 fibers R~22000 1.51- 1.7 microns across 3 Hawaii-2RG 2Kx2K near-IR detectors

5. APOGEE Overview T eff ~ 3900, log g ~ 0.7, different metallicities APOGEE wavelength coverage includes lines of 15+ elemental abundances Significant molecular contributions from CO, OH, CN Abundances derived from modeling spectra: automated matching against multidimensional spectral libraries

6. SDSS-IV/ APOGEE-2: 2014-2020 Dual Hemisphere Observations

7. APOGEE-2 Overview

8. Core Goal Reserve Ancillary: North 80% 10% 5% 5% South 90%5% 2.5%2.5% “Core” = science driving SRD requirements “Goal” = valued science not in “core”. “Reserve” = unallocated time, allows for evolution in understanding of priorities/opportunities + contingency. “Ancillary” = program modeled on successful APOGEE-1 program. Ancillary science program (+ reserve?) remain viable routes for new science opportunities. APOGEE-2 Survey Plan

9. Core science will be centered on: Bulge Disk Halo In addition, we will be doing programs on Eclipsing binaries (fibers on existing plates) M dwarfs (fibers on existing plates) Young star clusters (specific plates) Substellar companions (specific plates) KOIs + KOI control sample (specific plates) Open clusters Globular clusters Satellites (dSph, MCs) Stellar ages and physics through asteroseimology & gyrochronology APOGEE-2 Goal Science

10. Targeting Main survey targets selected using relatively simple color cuts Most targets selected from 2MASS catalog Reddening estimated using mid-IR photometry Main survey targets selected via dereddened color cut (J-K)_0 > 0.5 (J-K)_0 > 0.8 Calibration, goal, and ancillary targets selected as needed

11. Observing APOGEE fields use standard SDSS plug plates Typical field includes 230 science targets, 35 hot stars for telluric absorption measurement, 35 sky fibers for sky subtraction Fields are observed for ~1 hour of exposure per visit, to achieve S/N~100 per half-resolution element at H=12.2 if observed for three visits; this is usually split into 8 exposures, with detector dithering for exposure pairs Fields are observed multiple times to identify RV variables and to build up S/N For APOGEE-2, there will be MaNGA “piggy-back” observing for massive halo coverage in the north NMSU 1m telescope also feeds APOGEE-N when available, in single-object mode (bright and calibration targets)

12. – Data infrastructure for APOGEE-2 will be similar to that of APOGEE-1, generalized to multiple observatories – APOGEE raw data and data products are stored on the Science Archive Server (SAS) – Reduction and analysis software is (mostly) managed through the SDSS SVN repository – Raw and reduced data described through SDSS datamodel – Data and processing documented via SDSS web pages and technical papers Data infrastructure

13. – APOGEE instrument reads continuously (every ~10s) as data are accumulating, 3 chips at 2048x2048 each Raw data are stored on instrument control computer (current capacity is several weeks of data) Individual readouts are “annotated” with information from telescope and stored on “analysis” computer (current capacity is several months). These frames were archived to local disks that are “shelved” at APO (currently 20 x 3TB disks) for APOGEE-1; currently NOT doing this for APOGEE-2 – “quick reduction” software at observatory assembles data into data cubes and compresses (lossless) for archiving on SAS Maximum daily compressed data volume ~ 60 Gb Raw data

14. Does not include NMSU 1m + APOGEE data LCO data will be concurrent Total 2.5m raw data to date: ~22 TB

15. “quick reduction” software estimates S/N (at H=12.2) which is inserted into plate database for use with autoscheduling decisions APOGEE-1 – Data transferred to SAS next day, transferred to NMSU later that day, processed with full pipeline following day, updated S/N loaded into platedb, initial QA inspection APOGEE-2 proposal: – Process data at SAS location (Utah) and/or – Improve “quick reduction” S/N Initial processing

16. Three main stages (+1 post-processing) – APRED : processing of individual visits (multiple exposures at different detector spectral dither positions) into visit-combined spectra, with initial RV estimates. Can be done daily – APSTAR: combine multiple visits into combined spectra, with final RV determination. For APOGEE-1, has been run annually (DR10: year 1, DR11: year 1+year2, DR12: years 1-3); TBD for APOGEE-2 – ASPCAP: process combined (or resampled visit) spectra through stellar parameters and chemical abundances pipeline For APOGEE-1, has been run several times – ASPCAP/RESULTS: apply calibration relations to derived parameters, set flag values for these Pipeline processing

17. Raw data: data cubes (apR) Processed exposures (fairly specialized interest?) – 2D images (ap2D) – Extracted spectra (ap1D) – Sky subtracted and telluric corrected (apCframe) Visit spectra – Combine multiple exposures at different dither positions – apVisit files: native wavelength scale, but with wavelength array Combined spectra – Combine multiple visits, requires relative RVs – apStar files: resampled spectra to log(lambda) scale Derived products from spectra – Radial velocities and scatter from multiple measurements (done during combination) – Stellar parameters/chemical abundances from best-fitting template Parameters: Teff, log g, microturbulence (fixed), [M/H], [alpha/M], [C/M], [N/M] Abundances for 15 individual elements – aspcapStar and aspcapField files: stellar parameters of best-fit, pseudo- continuum normalized spectra and best fiitting templates Wrap-up catalog files (allStar, allVisit) APOGEE data products

18. APOGEE data volume Raw data: 2.5m+APOGEE: ~7 TB/year APOGEE-1  ~10 TB/year with MaNGA co-observing 1m+APOGEE: ~1 TB/year LCO+APOGEE: ~5 TB / year TOTAL APOGEE-1 + APOGEE-2 : ~100 TB Processed visit files: ~ 3 TB/year (80% individual exposure reductions) Processed combined star files: ~500 GB/100,000 stars Processed ASPCAP files: raw FERRE files ~500 GB/100,000 stars Bundled output: ~100 GB / 100,000 stars TOTAL APOGEE-1 + APOGEE-2 (one reduction!): ~ 50 TB Processing time: DR12 took 2-3 weeks for all stages

19. APOGEE data access “Flat files” available via SDSS SAS: all intermediate and final data product files summary ``wrap-up” files (catalog) described by SDSS datamodelSDSS datamodel “Catalog files” available via SDSS CAS: apogeeVisit, apogeeStar, aspcapStar, apogeeField, apogeeObject, apogeePlate Spectrum files available via SDSS API and web interface to SAS database (SASDB) Planning 3 public data releases in SDSS-IV (plus possible additional internal): DR15: July 2017 (data through July 2016) DR17: July 2019 (data through July 2018 – first APOGEE-S) DR19: Dec 2020 (all data)

20. APOGEE software products apogeereduce: IDL reduction routines (apred and apstar) aspcap speclib: management of spectral libraries, but not all input software (no stellar atmospheres code, limited spectral synthesis code) ferre: F95 code to interpolate in libraries, find best fit idlwrap: IDL code to manage ASPCAP processing apogeetarget: IDL code for targeting

21. APOGEE pipeline processing Software all installed and running on Utah servers Software already in pipeline form (few lines per full reduction step to distribute and complete among multiple machines/processors) Some need to improve distribution of knowledge and operation among team Some external data/software required for ASPCAP operation Generation of stellar atmospheres (Kurucz and/or MARCS) Generation of synthetic spectra (ASSET, but considering MOOG and TURBOSPECTRUM)

22. APOGEE-S processing Currently anticipated that raw data taken from APOGEE-S at LCO will be transferred daily to SAS Some bandwidth testing has been done, and sufficient bandwidth is anticipated Data processing will then proceed in ~identical fashion to APOGEE-N data, assuming that instrument delivers data of comparable quality APOGEE-N and APOGEE-S data will be stored/flagged separately, since they are not likely to be totally homogeneous

23. Personnel

24. APOGEE data personnel (not including targeting) Project management: Majewski, Sobeck, Hearty Key data collaboration members: Holtzman, Allende-Prieto Key external participants: Shetrone (pipeline coordinator), Nidever (reduction lead), (Meszaros, ASPCAP; Smith, ASPCAP) In-kind personnel contributions: Carrera (ASPCAP) Paid postdoc+ personnel: Ana Garcia-Perez (calibration lead, 1.0 FTE for one year), Neville Shane (database interfaces, 1.0 FTE for duration TBD), Duy Nguyen (reduction, 1.0 FTE for duration TBD), Paid grad student personnel: Michael Hayden (daily reduction, 0.5 grad student for project duration), Diane Feuillet (1m operation, 0.5 grad student for project duration), Nick Troup (ASPCAP, 1.0 grad student for 1 year, possibly more).

25. APOGEE software responsibilities apogeereduce developer: Nidever, Holtzman, (Nguyen) operation: Holtzman, (Hayden, Nidever, Nguyen) ASPCAP grids: ASSET: Allende Prieto / Koesterke Turbospec: Zamora, Garcia-Hernandez, Sobeck, Garcia- Perez, Holtzman MOOG: Shetrone, Holtzman, others speclib postprocessing: Allende-Prieto, Holtzman ferre: Allende Prieto idlwrap: Holtzman, Garcia-Perez Operation: Holtzman (Troup) Analysis: Garcia-Perez, Carrera, Meszaros, Garcia-Hernandez, Troup

26. Challenges / issues Data analysis: automated abundances still challenging, work needed Accommodation for variable LSF Cooler star grids and improvements Error analysis and propagation Data access: Support of multiple interfaces (CAS + SASDB) Software management Aging software (and developers?) / deep knowledge IDL based Personnel Need personnel who take initiative: adequacy of staffing level depends more on people than on FTE Southern hemisphere operations development

27. END