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).
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.
- 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.
1.2 Team B: Introducing noise and biases
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.
Reddened population synthesis data can also be found on our Google Drive ("extinctedmags" files). Progress on these is ongoing.
- Add reddening biases
- Add target selection biases
Provisional header of mock catalogues:
Galaxia (for more info check the online Galaxia documentation http://galaxia.sourceforge.net/)
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.
Mock catalogues generated by Teams A and B:
- 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.
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.
||Generate artificial datasets
||T0 = 1 March 2014
||First set of unbiased artifical data received
||Introducing noise and biases
||T1 = October 2014
||Noise and biases added
||Retrieving the stellar parameters
||T2 = July 2015
||Results from: Thaise, Pisa, Aldo, Victor, Dennis, Orlagh, Sarbanii
||Retrieving the galactic parameters
||T3 = T2 + 6 months
||Daisuke Kawata interested in pushing this forward
||Assessing the different methods and codes used
||T4 = started September 2015