Hare and Hounds Exercises

Coordinators: Andrea Miglio, Luca Casagrande, Joris De Ridder

Contents:

1 Project description
1.1 Team A: Generating artificial datasets
1.2 Team B: Introducing noise and biases
1.3 Team C: Retrieving the stellar parameters
1.4 Team D: Retrieving the galactic parameters
1.5 Team E: Assessing the different methods and codes used
2 Timeline

Project Description

The goal of this project is to assess under which conditions and with what accuracy the properties of a stellar population can be recovered within the uncertainties of classical and seismic data (including target selection biases).


Team A: Generating artificial datasets

Members: Annie Robin, Sanjib Sharma, Leo Girardi

- Generate various sets of artificial data representative of populations of giants in the fields of CoRoT and Kepler (including the fields of the    2-wheel mission).
- Use parametrized models of the Milky Way (TRILEGAL, Besancon, Galaxia,...).
- The team's output will be artificial observational data such as:
          - seismic data (such as large frequency separation, nu_max, and the period spacing),
          - spectroscopic data (effective temperature, chemical abundances, radial velocity),
          - photometric constraints (apparent magnitudes, colours),
          - if possible: astrometric constraints (parallaxes and proper motions) as we will obtain them with Gaia.
The 'raw' population synthesis data can be found on our Google Drive. Extinction is not yet taken into account, so we'll do this first before we apply any biases.

1.2 Team B: Introducing noise and biases

coordinator: Luca
Members: Andrea, Joris, Bill, Gail, Rafa, Rob Farmer, Enda Farrell, Ulrih Kolb, Berry Holl


- Add random (possibly non-gaussian) and systematic uncertainties to the "unbiased stellar population" generated by Team A.
- Add reddening biases
- Add target selection biases
Reddened population synthesis data can also be found on our Google Drive ("extinctedmags" files). Progress on these is ongoing.

Provisional header of mock catalogues:

Trilegal

Gc,          logAge,          [M/H],          m_ini,          logL,          logTe,          logg,          m-M0,          Av,          comp. mbol,          Kepler g,          r,          i, z,          DDO51_finf,          J,          H,          Ks,          Mact,          ev_stage

Besancon

R,          B-V,          V-R,          V-I,          mux,          muy,          Vr,          UU,          VV,          WW,          Mv,          CL,          Typ,          Teff,          logg, Age,          Mass,          Mbol,          Radius,          [Fe/H],          l(deg),          b(deg),          RA2000.0,          DEC2000.0,          Dist,           x(kpc),    y(kpc),          z(kpc),          Av,          [alpha/Fe]

Galaxia (for more info check the online Galaxia documentation http://galaxia.sourceforge.net/)

popid,          satid,          partid,          fieldid,          smass,          mact,          mtip,          age,          lum,          teff,          grav,          feh,           alpha,  px,          py,          pz,          vx,          vy,          vz,          rad,          ra,          dec,          glon,          glat,          exbv_schlegel_inf,          exbv_schlegel, exbv_solar,          mag0,          mag1,          mag2,          ubv_b,          ubv_h,          ubv_i,          ubv_j,          ubv_k,          ubv_r,          ubv_u,          ubv_v

1.3 Team C: Retrieving the stellar parameters

Members: Victor, Dennis, Thaise/Leo, Benoit, Orlagh, Maurizio - Santi - Adriano, Sarbani, Josefina, Aldo, Marie Martig, Scilla Degl'Innocenti'

- Use stellar evolution and pulsation codes to model the "observed" stellar properties to estimate their age, distance, mass, etc.
- Carefully keep record of the assumptions you use, such as which opacities you use, mixing length, overshoot parameter, etc.
- No information from team A will be available.
Mock catalogues generated by Teams A and B:

- dataset1.txt.gz
- dataset2.txt.gz
- dataset3.txt.gz
- dataset4.txt.gz

Team D: Retrieving the galactic parameters

Members: D. Kawata, G. Gilmore, M. Smith, B. Anguiano Jimenez, A. Recio-Blanco, G. Kordopatis, F. Anders, I. Minchev, J. Bovy, J. Bland Hawthorn.

- Given the stellar properties derived by Team C, recover the global galactic population properties that constrain the chemical and    dynamical evolution of the galactic disk.
- Estimate the age-metallicity and age-velocity dispersion relations as a function of the position in the disk. Retrieve possible gradients.
- Estimate the initial mass function.
- Estimate the star formation rate as a function of the position in the disk.

Team E: Assessing the different methods and codes used

"Members: X, Y, Z"

- Given the input and output population parameters, compare the results of the different groups using different methods/codes.
- Establish the reliability of the error bars returned by team D.
- Assess how robust the results are as a function of the noise levels.
- Make recommendations for an optimized observation strategy for the Kepler, CoRoT and APOGEE teams.

Timeline

Who What When Notes
Team A Generate artificial datasets T0 = 1 March 2014 First set of unbiased artifical data received
Team B Introducing noise and biases T1 = October 2014 Noise and biases added
Team C Retrieving the stellar parameters T2 = July 2015 Results from: Thaise, Pisa, Aldo, Victor, Dennis, Orlagh, Sarbanii
Team D Retrieving the galactic parameters T3 = T2 + 6 months Daisuke Kawata interested in pushing this forward
Team E Assessing the different methods and codes used T4 = started September 2015