1. Determining Ages of APOGEE Giants with Known Distances Diane Feuillet New Mexico State University Jon Holtzman, Jo Bovy, Leo Girardi The APOGEE Team

3. Thick disk: Old Kinematically hot Metal poor Alpha rich Thin disk: Younger Kinematically cold Metal rich Alpha poor Metallicity gradients Age-metallicity relation Nidever+ 2014 Galactic Chemical Evolution Frinchaboy+ 2013

4. Apache Point Observatory Galactic Evolution Experiment Explore Galactic evolution through detailed chemical abundances R~23,000, 1.51-1.70μ spectrograph 130,000 red giants, ~400,000 in APOGEE-2

5. Abundances Across the Disk Hayden+ 2015

6. Abundances Across the Disk Hayden+ 2015 Direct comparisons of different radial bins is difficult SFR, inflow, mixing, etc

7. Absolute Ages of Stars Not Easy! Empirical o Gyrochronology o Chromospheric activity Model-dependent o Isochrone model matching o Asteroseismology Recently, CN abundances Age adds important third dimension to traditional [Fe/H] vs [α/Fe] space

8. Ages and Abundances Haywood+ 2013 Age adds crucial evolutionary information and population identification

9. Ages and Abundances ? ? ? Haywood+ 2013

10. Ages of Red Giants Age can be determined from mass Seismic masses are good, uncertainty ~15% CN masses L, T eff, log g mass Uncertainty depends on log g, ~0.11 dex or 30% PARSEC Bressan+ 2012 Feuillet+ 2016 0.38 dex uncertainty in age

11. Ages of Red Giants Use Bayesian isochrone matching with all parameters M V adds age resolution to giant branch PARSEC Feuillet+ 2016

12. Test Sample Isochrone points with APOGEE-like uncertainties imposed Bayesian Assume flat SFH in age Chabrier IMF Take mean of age PDF [Fe/H], T eff, M V, log g σ = 0.1807 Feuillet+ 2016

13. Local Sample 700 local giants within 400 pc Observed with 1m+APOGEE Reduced and analyzed with APOGEE software Hipparcos distances Apache Point Observatory Feuillet+ 2016

14. Local Sample 324 RC stars identified Feuillet+ 2016

15. Local Sample Reasonable age distribution Suggests age-alpha relation Observed Expected Feuillet+ 2016

16. Hierarchical Modeling Find a more informed prior for the SFH Use the full age PDF to constrain a model SFH α-dependent Gaussian SFH Feuillet+ 2016

17. Hierarchical Modeling Feuillet+ 2016

18. Age Trends Strong relation between α abundance and mean age of Gaussian model Age-metallicity relation consistent with other work Velocity dispersion consistent with GCS Feuillet+ 2016

19. Age Trends Strong relation between α abundance and mean age of Gaussian model Age-metallicity relation consistent with other work Velocity dispersion consistent with GCS Feuillet+ 2016

20. Age Trends Strong relation between α abundance and mean age of Gaussian model Age-metallicity relation consistent with other work Velocity dispersion consistent with GCS Feuillet+ 2016

21. Age Trends Strong relation between α abundance and mean age of Gaussian model Age-metallicity relation consistent with other work Velocity dispersion consistent with GCS Power law indices UVWTotal Feuillet+ 20160.300.390.440.36 GCS (Holmberg+ 2009)0.390.400.530.40 Feuillet+ 2016

22. Future Work Need large samples with distance measurements Apply to APOGEE RC sample Test monoabundance subsamples Expand hierarchical modeling method With Gaia distances APOGEE APOGEE-2 GALAH Gaia-ESO

23. Future Work Need large samples with distance measurements Apply to APOGEE RC sample Test monoabundance subsamples Expand hierarchical modeling method With Gaia distances APOGEE APOGEE-2 GALAH Gaia-ESO QUESTIONS?