File formats

Format of a file with a list of models and properties:

Description:

  • The first line is a header. It contains the root folder (including the final slash) with the grid of models and optionally, a suffix for the names of the files with the theoretical pulsation frequencies. For example:

    /home/dreese/models_inversions/Grid_mesa_MS/  .freq

  • Each of the following lines correspond to one model in the grid. They are composed of 9 or more columns with the following information:

    1. The second part of the path for the given model. When concatenated with the prefix on the first line, this should give the full path to the model. If, furthermore, the suffix from the first line is appended to it, it gives the name of the file with the frequencies.

    2. The stellar mass in \(\mathrm{g}\)

    3. The stellar radius in \(\mathrm{cm}\)

    4. The stellar luminosity in \(\mathrm{g.cm^2.s^{-3}}\)

    5. The metallicity

    6. The hydrogen content

    7. The stellar age in \(\mathrm{Myrs}\)

    8. The effective temperature in \(\mathrm{K}\)

    9. A dimensionless age parameter

    10. (user-defined) This and the following columns correspond to the parameters specified in the user_params variable given in AIMS_configure.py.

  • Except for the first line, the order of the lines does not matter. AIMS will construct evolutionary tracks based on the parameters selected in the grid_params variable given in AIMS_configure.py, and sort them according to age.

Example:

Here’s an example of a file read by AIMS (via the model.Model_grid.read_model_list() method):

/home/dreese/models_inversions/Grid_mesa_MS/  .freq
M0.80/LOGS_M0.80/M0.80Z0.0028Y0.2536/m0.80Y0.2536Z0.0028a1.8ovh0.2ovhe0_n1.profile.FGONG  1.59136E+33  5.02248266E+10  2.33097993E+33  0.0028  0.7436  1.00000000E-04  6000.94326  0.00000  7.432106E-01
M0.80/LOGS_M0.80/M0.80Z0.0028Y0.2536/m0.80Y0.2536Z0.0028a1.8ovh0.2ovhe0_n2.profile.FGONG  1.59136E+33  4.86716596E+10  1.94640636E+33  0.0028  0.7436  2.09332874E+02  5827.26021  0.01383  7.319698E-01
M0.80/LOGS_M0.80/M0.80Z0.0028Y0.2536/m0.80Y0.2536Z0.0028a1.8ovh0.2ovhe0_n3.profile.FGONG  1.59136E+33  4.89176532E+10  1.97545563E+33  0.0028  0.7436  4.34073185E+02  5834.15715  0.02868  7.188704E-01

It contains three models. The structure of the first model can be found in the following file:

/home/dreese/models_inversions/Grid_mesa_MS/M0.80/LOGS_M0.80/M0.80Z0.0028Y0.2536/m0.80Y0.2536Z0.0028a1.8ovh0.2ovhe0_n1.profile.FGONG

and its frequencies in this file:

/home/dreese/models_inversions/Grid_mesa_MS/M0.80/LOGS_M0.80/M0.80Z0.0028Y0.2536/m0.80Y0.2536Z0.0028a1.8ovh0.2ovhe0_n1.profile.FGONG.freq

The tenth column corresponds to the central hydrogen content, as specified by the contents of the user_params variable from AIMS_configure.py:

user_params = (("Xc", r'Central hydrogen, $%sX_c%s$'),)

Format of a file with theoretical frequencies:

AIMS is able to read files with the theoretical frequencies in various different text formats (see example below) as well as in the grand summary format from ADIPLS. This is a FORTRAN binary format described on pages 32 and 33 of the ADIPLS documentation. What follows is a description of one the text formats used by AIMS for files with frequencies:

Description:

  • the first line is a header (and is skipped)

  • the following lines contain five columns which correspond to l, n, frequency, a_value, inertia

    • the a_value column is ignored, so it could contain anything. InversionKit will typically put the difference between the numerical and variational frequencies in that column.

Example:

Here’s an example of a file with theoretical pulsation frequencies which can be read by AIMS (via the model.Model.read_file() method):

#l  n         nu_theo (muHz)  nu_var-nu_theo (muHz)                Inertia
0  15  3.225852209451052e+03  1.312960435370769e-03  3.233628965187502e-09
0  16  3.421699035498995e+03 -2.482639610207116e-03  2.229252226305757e-09
0  17  3.615805033992529e+03  3.993051574070705e-03  1.618154348529283e-09
0  18  3.809740380503104e+03  9.650666734160040e-04  1.250359548964621e-09
0  19  4.003716857281849e+03 -7.991676880010345e-03  1.033914933206195e-09
0  20  4.198691419457581e+03  1.742711681799847e-03  8.866985261874711e-10
1  15  3.316007619955153e+03  5.056100344972947e-03  2.715966891128009e-09
1  16  3.511258977705781e+03  1.855844971032639e-04  1.902147334986236e-09
1  17  3.705576731149742e+03 -2.505276897409203e-03  1.424266453221534e-09
1  18  3.899485457373566e+03  5.212276555539575e-03  1.134594720287415e-09
1  19  4.094401244305849e+03  6.020260397235688e-03  9.579611596023003e-10
1  20  4.289716814475406e+03 -1.019475706561934e-02  8.344804874142957e-10
2  15  3.399280335063532e+03 -8.466318249702454e-04  2.315947651745295e-09
2  16  3.594141943503532e+03  4.712417365681176e-03  1.665322627996223e-09
2  17  3.788792185755381e+03 -1.167229517704982e-03  1.277569745555387e-09
2  18  3.983271067684743e+03 -6.187409578615188e-03  1.048757367028520e-09
2  19  4.178866833517976e+03  6.893199766636826e-03  8.963691946280509e-10
2  20  4.374959711016754e+03  3.274638356742798e-03  7.911508926344487e-10
3  15  3.476224140192640e+03 -2.524210208321165e-03  2.009476926536794e-09
3  16  3.671438520072859e+03  2.351724720028869e-04  1.485336526791650e-09
3  17  3.866350877376991e+03  5.643782460992952e-03  1.167619144668003e-09
3  18  4.061929209725198e+03 -1.552865011490212e-03  9.789648655155361e-10
3  19  4.258077196700047e+03 -8.629839649984206e-03  8.472972126693386e-10
3  20  4.455063887754256e+03  1.484804296796938e-02  7.528069568152023e-10

Format of a file with observational constraints:

Description:

  • a collection of lines with frequency data with either (l, freq, error_bar) or (l, n, freq, error_bar) (depending on the value of read_n in the AIMS_configure.py file). For example:

    0 1503.5 0.16

    or the following if specifying the radial order:

    0 15 1503.5 0.16

  • a collection of lines with classical constraints. These start with the name of the relevant parameter (see possible options in model.Model.string_to_param()) followed by a description of its probability distribution function. This probability distribution function is specified in two possible ways:

    • it is implicitly assumed to be Gaussian. In this situation it is only necessary to specify the mean value and the one sigma error bar. For example:

      Teff 6100 80

    • it is explicitly specified (different options are given in AIMS.Distribution):

      Teff Uniform 6000 6200

  • anything following a # is a comment

  • the order of the lines does not matter

Examples:

  • example of a file where n is not specified:

    0 1582.20 0.13  # this is a (useless) comment
0 1684.02 0.16
0 1785.57 0.15
1 1526.55 0.29
1 1628.90 0.30
1 1730.45 0.17
2 1575.49 0.82
2 1676.25 0.51
2 1777.62 0.27
Teff 6060.00 84.00
Fe_H -0.20 0.09

  • example of a file where n is specified:

    0 15 1582.20 0.13
0 16 1684.02 0.16
Teff 6060.00 84.00 # AIMS doesn't worry about the order of the lines
0 17 1785.57 0.15
1 14 1526.55 0.29
1 15 1628.90 0.30
1 16 1730.45 0.17
2 14 1575.49 0.82
2 15 1676.25 0.51
2 16 1777.62 0.27
Fe_H -0.20 0.09
Differences with AMP:

  • the number of frequencies does not need to be specified (if this line contains supplementary parameters, than AIMS.py may confuse it with frequency data)

  • there are no flags (one should adjust the parameters in AIMS_configure.py instead)

  • the order of the lines is not important (one can mix the classic and seismic observables)

  • it is possible to specify radial orders (depending on the value of read_n in the AIMS_configure.py file)

  • the treatment of non-seismic constraints is more flexible

    • a larger variety of non-seismic constraints can be included (see possible options in model.Model.string_to_param())

    • full parameter names are allowed (and preferred); for compatibility with AMP, the same one letter abbreviations are also allowed

    • it is possible to specify the probability distribution function