astero_search_controls

$\chi^2$ weights

chi2_seismo_fraction

The optimization methods will use and report a total $\chi^2$ that is a weighted sum of seismic and “spectroscopic” (really “non-seismic”) parts. chi2_seismo_fraction is the weight given to the seismic part. i.e. the code internally evaluates something like

chi2 = chi2_seismo*chi2_seismo_fraction
     + chi2_spectro*(1 - chi2_seismo_fraction)

chi2_seismo_fraction = 0.667d0

Non-seismic (“spectroscopic”) observations

chi2_spectro is a sum of terms for different non-seismic observables (e.g. Teff, logg, [Fe/H]). These controls specify what quantities are added to this $\chi^2$ and, in some cases, how they are computed.

Each component is identified by setting its name to either a string matching a history column or one of the cases defined in run_star_extras.f90. If target value and uncertainty sigma are greater than zero, a $\chi^2$ component will be computed and output in the sample data.

If include_in_chi2_spectro is set to .true., that component will be included in chi2_spectro. The old fixed options were equivalent to:

Teff
log_g
log_L
log_R
M_H (was FeH)
surface_Z_div_X
surface_He
Rcz

where M_H, surface_Z_div_X, surface_He and Rcz are now implemented in the standard ``run_star_extras.f90`. The other options fall back to the history columns.

To implement your own constraints, create the appropriate case in set_constraint_value in run_star_extras.f90.

The age option is still treated separately because there are additional controls that determine how the age target is approached.

Target values must be set separately for PGstar. If you want targets to appear in the loggTe window, you need to set the following PGstar controls:

loggTe_target_logg
loggTe_target_logg_sigma
loggTe_target_Teff
loggTe_target_Teff_sigma

Similarly, if you want targets to appear in the HR window, you need to set the following PGstar controls:

HR_target_logL
HR_target_logL_sigma
HR_target_Teff
HR_target_Teff_sigma

constraint_name(:)

include_constraint_in_chi2_spectro(:)

constraint_target(:)

constraint_sigma(:)

constraint_name(:) = ‘’ ! change this to whatever you want constraint_target(:) = 0 constraint_sigma(:) = 0 include_constraint_in_chi2_spectro(:) = .false.

normalize_chi2_spectro

If .true., the total $\chi^2$ computed for the non-seismic components (defined above) is divided by the number of components used to define it.

normalize_chi2_spectro = .true.

Z_div_X_solar

Convenience parameter leftover from the old interface that can be used when calculating things relative to solar metallicity.

Z_div_X_solar = 0.02293d0

include_age_in_chi2_spectro

age_target

age_sigma

age is the star’s age in years. When you include_age_in_chi2_spectro, set min_age_for_chi2 and max_age_for_chi2, and set eval_chi2_at_target_age_only to .false.. In &control, set max_years_for_timestep but don’t set max_age or num_adjusted_dt_steps_before_max_age.

include_age_in_chi2_spectro = .false.
age_target = 4.5695d9 ! (see Bahcall, Serenelli, and Basu, 2006)
age_sigma = 0.0065d9

num_smaller_steps_before_age_target

num_smaller_steps_before_age_target = 50 ! only used if > 0

dt_for_smaller_steps_before_age_target

This should be much smaller than age_sigma.

dt_for_smaller_steps_before_age_target = 0.0065d8 ! 1/10 age_sigma

Seismic observations

chi2_seismo_delta_nu_fraction

chi2_seismo_nu_max_fraction

chi2_seismo_r_010_fraction

chi2_seismo_r_02_fraction

chi2_seismo is a weighted combination of the large separation $\Delta\nu$, the frequency of maximum oscillation power $\nu_\mathrm{max}$, ratios of frequencies and individual frequencies. Specify the weighting of the terms in chi2_seismo by setting these controls. Naturally, all the fractions must be between zero and one. If the relevant fraction is not zero, the corresponding target values and uncertainties must be set.

chi2_seismo_delta_nu_fraction = 0d0  ! if > 0 then delta_nu and delta_nu_sigma must be set (see below)
chi2_seismo_nu_max_fraction = 0d0    ! if > 0 then nu_max and nu_max_sigma must be set (see below)
chi2_seismo_r_010_fraction = 0d0     ! if > 0, then include r_010 frequency ratios
chi2_seismo_r_02_fraction = 0d0      ! if > 0, then include r_02 frequency ratios

The fraction for the frequencies is whatever is left. i.e.

fraction for frequencies = 1 - (frac_r_010_ratios + frac_r_02_ratios + frac_delta_nu + frac_nu_max)

so if you only want to use individual frequencies in chi2_seismo, set all four fractions to zero.

normalize_chi2_seismo_frequencies

normalize_chi2_seismo_r_010

normalize_chi2_seismo_r_02

The terms for the frequencies and ratios are sums of terms for each frequency and ratio. If you normalize them, they are divided by that number of terms.

normalize_chi2_seismo_frequencies = .true.
normalize_chi2_seismo_r_010 = .true.
normalize_chi2_seismo_r_02 = .true.

trace_chi2_seismo_delta_nu_info

trace_chi2_seismo_nu_max_info

trace_chi2_seismo_ratios_info

trace_chi2_seismo_frequencies_info

trace_chi2_spectro_info

If .true., output information on that $\chi^2$ component to the terminal.

trace_chi2_seismo_delta_nu_info = .false.
trace_chi2_seismo_nu_max_info = .false.
trace_chi2_seismo_ratios_info = .false.
trace_chi2_seismo_frequencies_info = .false.

trace_chi2_spectro_info = .false. ! if true, output info to terminal

nu_max

nu_max_sigma

nu_max is the frequency of maximum oscillation power. You must set this value if chi2_seismo_nu_max_fraction is greater than zero.

 nu_max = -1
! nu_max is needed when
! chi2_seismo_nu_max_fraction > 0 or correction_factor > 0 (see below)
 nu_max_sigma = -1

delta_nu

delta_nu_sigma

delta_nu is the large frequency separation (roughly constant frequency difference between radial modes of increasing order) . You must set this value if chi2_seismo_delta_nu_fraction is greater than zero.

delta_nu = -1
delta_nu_sigma = -1

If delta_nu in the inlist is greater than zero, the code uses the inlist values for both delta_nu and delta_nu_sigma.

If delta_nu is less than or equal to zero in the inlist, the code estimates it by a linear fit to the observed radial frequencies and orders, l0_obs and l0_n_obs.

Along with calculating delta_nu, if delta_nu_sigma from the inlist is less than zero, then the code also sets it by using the radial data. Note that by setting delta_nu_sigma to a positive value and delta_nu to a negative value, you can have the code get delta_nu from the given l0_obs and l0_n_obs, while still using the delta_nu_sigma from the inlist.

Mode frequency data

nl

freq_target

freq_sigma

Data for oscillation modes. nl(el) is the number of modes of degree el, freq_target(el,1) to freq_target(el,nl(el)) are the frequencies (in increasing frequency order) and freq_sigma(el,1) to freq_sigma(el,nl(el)) are the uncertainties.

nl(0:3) = 0 ! number of observed modes
freq_target(0:3,:) = 0 ! frequencies. set e.g. freq_target(0,1) ... freq_target(0,nl(0)) for radial modes
freq_sigma(0:3,:) = 0 ! freq_sigma(el,i) is uncertainty for freq_target(el,i)

l0_n_obs

l0_n_obs(i) is the radial order of l0_obs(i) for i=1, nl0. Need to give these if the code is to calculate delta_nu and delta_nu_sigma. If you provide delta_nu, then you don’t need to set these. If l0_n_obs are provided, the Kjeldsen surface correction will use them.

l0_n_obs(:) = -1

Optimization method

eval_chi2_at_target_age_only

Set this to .true. if you only want $\chi^2$ for a specific age and no others. In addition, set max_age, max_years_for_timestep and num_adjusted_dt_steps_before_max_age.

eval_chi2_at_target_age_only = .false.

min_age_for_chi2

max_age_for_chi2

Use these if you only want to evaluate chi2 for a given range of ages.

min_age_for_chi2 = -1 ! (years) only use if > 0
max_age_for_chi2 = -1 ! (years) only use if > 0

search_type

This specifies the kind of search to perform, each of which has its own separate controls further on. The options are

use_first_values

This option means no search. Just do a single run using first values for the parameters.

scan_grid

Evaluates $\chi^2$ for each parameter combination within the min and max ranges specified below, with the spacing defined by delta.

For a first rough scan, consider setting chi2_seismo_delta_nu_fraction = 1, which skips the relatively costly calculations of frequencies and simply uses delta_nu along with the non-seismic information. You can then follow up with medium resolution scans in smaller regions around candidates from the rough scan with chi2_seismo_delta_nu_fraction = 0 to include frequencies.

simplex

Search for minimal $\chi^2$ model using Nelder-Mead simplex algorithm:

Nelder, J. A. and Mead, R.
"A Simplex Method for Function Minimization."
Comput. J. 7, 308-313, 1965.

There are versions of this in Numerical Recipes under the name “amoeba”, in Matlab under the name “fminsearch”, and in Mathematica as an option for “NMminimize”. Our version has lots of bells and whistles and is, of course, superior to the others. ;)

newuoa

Search for minimal $\chi^2$ model using Powell’s NEWUOA algorithm for unconstrained minimization without derivatives by quadratic polynomial approximation.

M.J.D. Powell, "Developments of NEWUOA for unconstrained minimization without derivatives",
Department of Applied Mathematics and Theoretical Physics, Cambridge, England, report NA05, 2007.

bobyqa

Search for minimal $\chi^2$ model using Powell’s “Bounded Optimization BY Quadratic Approximation” (BOBYQA) algorithm. Any location within the bounds is available for consideration.

M.J.D. Powell, "The BOBYQA algorithm for bound constrained optimization without derivatives",
Department of Applied Mathematics and Theoretical Physics, Cambridge, England, report NA06, 2009.

from_file

Calculates $\chi^2$ for the parameter values in a given file. For each line of the file (after the first, which has column names), set the parameter values to that of the file for those parameters with vary_param = .true..

The two methods from Powell use quadratic interpolation, either unconstrained (NEWUOA) or bounded (BOBYQA). The Nelder-Mead simplex method doesn’t interpolate; instead it simply compares values and moves toward lower $\chi^2$ and away from higher ones. In general, you can expect the Powell methods to converge faster than the simplex if the $\chi^2$ terrain is not too “bumpy” (bumps confuse the interpolation). Since the simplex scheme doesn’t do interpolation, bumps don’t cause it as much trouble, so it may be more robust. If you are just getting started, go with simplex at first. Try the interpolation methods when you have a very good candidate and want to look near it for even better results.

search_type = 'use_first_values'

scan_grid_output_filename

Output goes to the following file when search_type = 'scan_grid'.

scan_grid_output_filename = 'scan_grid_results.data'

restart_scan_grid_from_file

If .true., reads the scan_grid_output file and continues from where that stopped.

restart_scan_grid_from_file = .false.

simplex_itermax

Maximum number of iterations of the downhill simplex.

simplex_itermax = 1000 ! each iteration revises the simplex

simplex_fcn_calls_max

Maximum number of function calls for the downhill simplex. One iteration may use several function calls. Each “function call” is a stellar evolution track to get a $\chi^2$.

simplex_fcn_calls_max = 10000

simplex_x_atol

simplex_x_rtol

Terminate the simplex if the differences between iterations are less than either of these tolerances.

simplex_x_atol = 1d-10 ! tolerance for absolute differences
simplex_x_rtol = 1d-10 ! tolerance for relative differences

If you want the details, here’s the snippet of code. simplex(i,j) is value of i-th parameter for point j. l is the index of the best point. There are n parameters and n+1 points.

term_val_x = 0
do j=1,n+1 ! check each point
   if (j == l) cycle ! l is the best point; so skip it
   do i=1,n ! check each coordinate of point j vs point l
      term1 = abs(simplex(i,j)-simplex(i,l)) / &
         (x_atol + x_rtol*max(abs(simplex(i,j)), abs(simplex(i,l))))
      if (term1 > term_val_x) term_val_x = term1
   end do
end do
if (term_val_x <= 1d0) exit ! converged

simplex_chi2_tol

Terminate the simplex if the best point has a $\chi^2$ less than this.

simplex_chi2_tol = 1d-10 ! tolerance for chi^2

simplex_centroid_weight_power

Each iteration starts by doing a reflection of the worst point through the centroid of the others. The centroid points are weighted by (1/chi^2)**power. power = 0 gives the standard unweighted centroid. power > 0 shifts the reflection towards the better points.

simplex_centroid_weight_power = 0d0

simplex_enforce_bounds

If .true., points outside the bounds will be rejected without evaluation. If .false., the bounds will only be used when creating the initial simplex and for adaptive random search.

simplex_enforce_bounds = .false.

simplex_adaptive_random_search

This flag controls what is done if the standard options of reflect or contract fail to produce a improvement in the simplex. If .true., test uniform random samples within the bounds until a better point is found. If .false., shrink the simplex toward the best point.

simplex_adaptive_random_search = .false.

simplex_output_filename

Filename for the simplex output.

simplex_output_filename = 'simplex_results.data'

restart_simplex_from_file

If .true., then reads the output file (simplex_output_filename) and continues from where that stopped using the best $n+1$ results as the initial simplex (where $n$ is the number of parameters). Note that this restores the best simplex but you may still see it rerun recent cases if they were not good enough to be included in the simplex. We don’t restore the information about those failed attempts, so we need to rerun them.

restart_simplex_from_file = .false.

simplex_seed

Integer seed for random number generation.

simplex_seed = 1074698122 ! seed for random number generation

simplex_alpha

Coefficients for the reflection step of the downhill simplex algorithm.

simplex_alpha = 1d0    ! reflect

simplex_beta

Coefficients for the expansion step of the downhill simplex algorithm.

simplex_beta = 2d0     ! expand

simplex_gamma

Coefficients for the contraction step of the downhill simplex algorithm.

simplex_gamma = 0.5d0  ! contract

simplex_delta

Coefficients for the shrink step of the downhill simplex algorithm.

simplex_delta = 0.5d0  ! shrink

newuoa_output_filename

Filename for the NEWUOA output.

newuoa_output_filename = 'newuoa_results.data'

newuoa_rhoend

This is the tolerance that determines relative accuracy of final values i.e., NEWUOA stops when results are changing by less than this. See mesa/num/public/num_newuoa for details.

newuoa_rhoend = 1d-6

bobyqa_output_filename

Filename for the BOBYQA output.

bobyqa_output_filename = 'bobyqa_results.data'

bobyqa_rhoend

This is the tolerance that determines relative accuracy of final values i.e., BOBYQA stops when results are changing by less than this. See mesa/num/public/num_bobyqa for details.

bobyqa_rhoend = 1d-6

filename_for_parameters

Filename containing parameter values when search_type = 'from_file'.

filename_for_parameters = 'undefined'

max_num_from_file

If greater than zero, stop from_file search after trying this many lines from the file.

max_num_from_file = -1 ! if > 0, then stop after doing this many lines from file.

file_column_for_param

You need to say which columns in the file hold the various parameters. For example, if your file starts like the following:

      chi2         mass        init_Y      init_FeH    alpha       init_f_ov   my_param1   my_param2   my_param3
654   0.81543178   1.35000000  0.27000000  0.21000000  1.76000000  0.01000000  0.00000000  0.00000000  0.00000000

then set the column numbers like this:

file_column_for_param(1) = 3
file_column_for_param(2) = 4
file_column_for_param(3) = 5
file_column_for_param(4) = 6
file_column_for_param(5) = 7
file_column_for_param(6) = 8
file_column_for_param(7) = 9
file_column_for_param(8) = 10

Note that if you are not varying one of the parameters, e.g. f_ov, then you don’t need to set the file_column for that parameter.

from_file_output_filename

Filename for the from_file output.

from_file_output_filename = 'from_file_results.data'

Parameters

You can set a large number of parameters (currently up to 100) that will be searched/optimized for a best-fitting solution. Specify the parameters by the name, then either implement your own parameters in the set_param subroutine in run_star_extras.f90, or rely on the default implementation, which provides the legacy options * initial_mass * initial_Y * initial_FeH * alpha * f_ov

param_name

Names for parameters that will searched/optimized. Empty names '' are ignored.

param_name(:) = ''

vary_param

If .true., that parameter will be varied by the search. If .false., that parameter will be fixed at the first value (see first_param).

vary_param(:) = .false.

first_param

Initial parameter values for parameters that vary; fixed values for parameters that don’t.

first_param(:) = 0

min_param

max_param

Lower and upper bounds for parameter values.

min_param(:) = 0
max_param(:) = 0

delta_param

Grid spacing for parameter values, used when search_type = 'scan_grid'.

delta_param(:) = 0

Y_depends_on_Z

Y0

dYdZ

Legacy controls from the old interface, which is available with the default run_star_extras.f90 by using the parameter names initial_Y and ``initial_FeH`.

If Y_depends_on_Z = .false., Y is a parameter like any other and you should set vary_Y, first_Y, min_Y and max_Y (and delta_Y for a grid). If .true., Y depends on Z according to

Y = Y0 + dYdZ*Z

where Y0 and dYdZ are set below.

Y_depends_on_Z = .false.
Y0 = 0.248d0
dYdZ = 1.4d0

Y_frac_he3

If using the legacy initial_Y parameter, this controls the proportion of the initial helium abundance that’s ³He rather than ⁴He.

Y_frac_he3 = 1d-4 ! = xhe3/(xhe3 + xhe4); Y = xhe3 + xhe4

f0_ov_div_f_ov

If using the legacy parameter f_ov, the overshoot f0 is changed along with overshoot f according to

f0_ov = f0_ov_div_f_ov * f_ov

so this must be set to a positive value if f_ov is not zero.

f0_ov_div_f_ov = -1

Parameter limits

Calculating mode frequencies is a relatively costly process, so we don’t want to do it for models that are not good candidates. i.e., we want to filter out the bad candidates using the following less expensive tests whenever possible.

Note that if none of the models in a run pass these tests, then you will not get a total $\chi^2$ result for that run. That might not matter but if you are eliminating too many candidates in this way, the search routines might not get enough valid results to work properly. So watch what you are doing! If your search or scan is getting lots of runs that fail to give $\chi^2$ results, you’ll need to adjust the limits.

min_age_limit

Don’t consider models that aren’t old enough.

min_age_limit = 1d6

Lnuc_div_L_limit

Don’t consider models with L_nuc/L less than this limit. This rules out pre-ZAMS models.

Lnuc_div_L_limit = 0.95

chi2_spectroscopic_limit

Don’t consider models with chi2_spectro above this limit.

chi2_spectroscopic_limit = 1000

chi2_delta_nu_limit

Don’t consider models with chi2_delta_nu above this limit.

chi2_delta_nu_limit = 1000

chi2_radial_limit

We only calculate radial modes if the previous checks pass.

Calculating non-radial modes is much more expensive than radial ones, so we skip the non-radial calculation if the radial results are poor.

Don’t consider models with chi2_radial above this limit.

chi2_radial_limit = 100

We only calculate full $\chi^2$ if the model passes all these limit checks.

Timestep adjustment

We don’t want to evaluate more models than we need to but we also want to make sure that we resolve the optimal $\chi^2$. These controls adjust the maximum timestep depending on how close we are to the target values.

If you set the timestep limits too large, you run the risk of missing good $\chi^2$ cases. But if they are very small, you will spend a lot of runtime calculating lots of frequencies for lots of models. There is no standard set of best values for this. The choice will depend on the stage of evolution and how fast things are changing in the general region of the models with good $\chi^2$ values. These are just default values: there is no alternative to trying things and tuning the controls for your problem.

max_yrs_dt_when_cold

max_yrs_dt_when_cold = 1d8 ! when fail Lnuc/L, chi2_spectro, or ch2_delta_nu

max_yrs_dt_when_warm

max_yrs_dt_when_warm = 1d7 ! when pass previous but fail chi2_radial; < max_yrs_dt_when_cold

max_yrs_dt_when_hot

max_yrs_dt_when_hot = 1d6 ! when pass chi2_radial; < max_yrs_dt_when_warm

chi2_limit_for_small_timesteps

chi2_limit_for_small_timesteps = -1

max_yrs_dt_chi2_small_limit

max_yrs_dt_chi2_small_limit = 3d5 ! < max_yrs_dt_when_hot

chi2_limit_for_smaller_timesteps

chi2_limit_for_smaller_timesteps = -1 ! < chi2_limit_for_small_timesteps

max_yrs_dt_chi2_smaller_limit

max_yrs_dt_chi2_smaller_limit = 1d5 ! < max_yrs_dt_chi2_small_limit

chi2_limit_for_smallest_timesteps

chi2_limit_for_smallest_timesteps = -1 ! < chi2_limit_for_smaller_timesteps

max_yrs_dt_chi2_smallest_limit

max_yrs_dt_chi2_smallest_limit = 5d4 ! < max_yrs_dt_chi2_smaller_limit

sigmas_coeff_for_delta_nu_limit

sigmas_coeff_for_constraint_limit

We need a way to decide when to stop an evolution run. The following limits are used for this. We don’t want to stop too soon, so these limits are only tested for models that are okay for the Lnuc_div_L_limit.

The limit on each variable X is

X_limit = X_target + X_sigma*sigmas_coeff_for_X_limit

We only use limits where sigma_coeff_X is not zero. If sigma_coeff is positive, we stop when that value is greater than the limit. If sigma_coeff is negative, we stop when that value is less than the limit. Hence, use positive sigma_coeff for values that are increasing (e.g. log_L) and negative sigma_coeff for values that are decreasing (e.g. log_g, Teff, delta_nu).

sigmas_coeff_for_delta_nu_limit = 0
sigmas_coeff_for_constraint_limit(:) = 0

$\chi^2$ limits

You can stop the run if $\chi^2$ is rising.

limit_num_chi2_too_big

chi2_relative_increase_limit

If any of the following conditions are met in limit_num_chi2_too_big consecutive models after a minimum $\chi^2$ has been found, stop the run for that sample.

$\chi^2$ is greater than chi2_relative_increase_limit times the best $\chi^2$ for the run.
chi2_spectro, chi2_delta_nu or chi2_radial is greater than its corresponding _limit.
MESA fails to evaluate $\chi^2$ (because e.g. it can’t match all the modes of a particular degree).

limit_num_chi2_too_big = 20
chi2_relative_increase_limit = 2.0

chi2_search_limit1

chi2_search_limit2

If the best $\chi^2$ for the run is less than chi2_search_limit1, stop the run if $\chi^2$ is greater than chi2_search_limit2.

chi2_search_limit1 = 3.0
chi2_search_limit2 = 4.0

If you are doing a search or scanning a grid, you can use previous results as a guide for when to stop a run

min_num_samples_for_avg

max_num_samples_for_avg

avg_age_sigma_limit

avg_model_number_sigma_limit

We can stop the run using limits based on the average age of previous samples. Specifically, use at least min_num_samples_for_avg and at most max_num_samples_for_avg to compute the average age and model number, then stop the run if the age is greater than the average age or model number plus avg_age_sigma_limit or avg_model_number_sigma_limit times the standard deviation of the same set of samples.

min_num_samples_for_avg = 2 ! want at least this many samples to form averages
max_num_samples_for_avg = 10 ! use this many of the best chi^2 samples for averages
avg_age_sigma_limit = 10 ! stop if age > avg age + this limit times sigma of avg age
avg_model_number_sigma_limit = 10 ! ditto for model number

Surface corrections

correction_scheme

The options for the correction scheme are:

'kjeldsen', the correction of Kjeldsen et al. (2008);
'power_law', a free power law, used as a sanity check in Ball & Gizon (2017);
'cubic', the cubic/one-term correction by Ball & Gizon (2014, eqn 3);
'combined', the combined/two-term correction by Ball & Gizon (2014, eq 4);
'sonoi', the modified Lorentzian by Sonoi et al. (2015, eq 9); or
'', no corrections.

correction_scheme = 'kjeldsen'

If you’d like to experiment with your own correction scheme, you can use the other_astero_freq_corr hook in $MESA_DIR/star.

surf_coef1_name

surf_coef2_name

Each surface correction has one or two parameters that are passed around in the code as surf_coef1 and surf_coef2 and reported in the output under the control parameters surf_coef1_name and surf_coef2_name. In the various schemes, the surface corrections added to a mode with frequency $\nu$, normalized inertia $\mathcal{I}$ and relative inertia $Q$ are

kjeldsen         surf_coef1*(ν/νmax)**correction_b / Q (surf_coef2 = correction_r, eq. 6 of K08)
power_law        surf_coef1*(ν/νmax)**surf_coef2 / Q
cubic            surf_coef1*(ν/νac)**3 / I             (surf_coef2 is meaningless)
combined         (surf_coef1*(ν/νac)**3 + surf_coef2/(ν/νac)) / I
sonoi            surf_coef1*νmax*[1 - 1/(1 + (ν/νmax)**surf_coef2)] / Q

where νac is the acoustic cutoff frequency, computed by scaling the solar value of 5mHz in the same way as νmax (i.e. νac = 5mHz * (g/gsun)/sqrt(Teff/Teff_sun)).

surf_coef1_name = 'a_div_r'
surf_coef2_name = 'correction_r'

The default values a_div_r and correction_r correspond to the behaviour in r11701 and before.

correction_factor

Scale the correction by this fraction. Set to zero to skip doing corrections.

correction_factor = 0

correction_b

Solar-calibrated power law index used in the kjeldsen surface correction. The kjeldsen surface correction uses the observed radial orders of the radial modes (l0_n_obs) if they are provided.

correction_b = 4.90d0

note: to set nu_max_sun or delta_nu_sun, see star/defaults/controls.defaults

output controls

astero_results_directory

Directory in which to save astero outputs. If it doesn’t exist, it will be created.

astero_results_directory = 'outputs'

astero_results_dbl_format

astero_results_int_format

astero_results_txt_format

Formats for writing floats (reals), integers and characters (strings) to the results file.

astero_results_dbl_format = ‘(1pes26.16)’ astero_results_int_format = ‘(i26)’ astero_results_txt_format = ‘(a26)’

write_best_model_data_for_each_sample

write_best_model_data_for_each_sample = .true.

num_digits

Number of digits in sample number (with leading zeros).

num_digits = 4

sample_results_prefix

sample_results_postfix

Prefix and postfix for sample results filenames.

sample_results_prefix = 'sample_'
sample_results_postfix = '.data'

model_num_digits

Number of digits in model number (with leading zeros).

model_num_digits = 4

write_fgong_for_each_model

write_fgong_for_each_model = .false.

fgong_prefix

fgong_postfix

Prefix and postfix for FGONG filenames. You can include a directory in the prefix if desired but you must create the directory yourself.

fgong_prefix = 'fgong_'
fgong_postfix = '.data'

write_fgong_for_best_model

write_fgong_for_best_model = .false.

best_model_fgong_filename

best_model_fgong_filename = ''

write_gyre_for_each_model

write_gyre_for_each_model = .false.

gyre_prefix

gyre_postfix

Prefix and postfix for GYRE filenames. You can include a directory in the prefix if desired but you must create the directory yourself.

gyre_prefix = 'gyre_'
gyre_postfix = '.data'

max_num_gyre_points

max_num_gyre_points = -1 ! only used if > 1

write_gyre_for_best_model

write_gyre_for_best_model = .false.

best_model_gyre_filename

best_model_gyre_filename = ''

write_profile_for_best_model

write_profile_for_best_model = .false.

best_model_profile_filename

best_model_profile_filename = ''

save_model_for_best_model

save_model_for_best_model = .false.

best_model_save_model_filename

best_model_save_model_filename = ''

save_info_for_last_model

last_model_save_info_filename

If save_info_for_last_model = .true., treat the final model as the “best” and save info about final model to last_model_save_info_filename.

save_info_for_last_model = .false.
last_model_save_info_filename = '' ! and save info about final model to this file.

Miscellaneous

save_next_best_at_higher_frequency

save_next_best_at_lower_frequency

Save info about next best matches.

save_next_best_at_higher_frequency = .false.
save_next_best_at_lower_frequency = .false.

trace_limits

If .true., write info to terminal about status relative to various limits, e.g. if sigmas_coeff_for_Teff_limit is set, Teff_limit = Teff_target + Teff_sigma*sigmas_coeff_for_Teff_limit and the run will stop when Teff < Teff_limit. Trace will write out values of Teff and Teff_limit. The same goes for other limits, e.g. logg, logL, delta_nu, etc.

trace_limits = .false.

save_controls

save_controls_filename

If save_controls = .true., the controls in &astero_search_controls are dumped to the file save_controls_filename. If save_controls_filename is empty, a default filename is used.

save_controls = .false. ! dumps &astero_search_controls controls to file
save_controls_filename = '' ! if empty, uses a default name

save_mode_model_number

save_mode_filename

el_to_save

order_to_save

Save an eigenfunction.

save_mode_model_number = 0
save_mode_filename = ''
el_to_save = 0
order_to_save = 0

add_atmosphere

If .true., then star adds the atmosphere structure before passing the model to ADIPLS or GYRE. The atmosphere model is determined by the atmosphere boundary conditions in the &controls namelist for star.

add_atmosphere = .false.

keep_surface_point

If .true., keep the k=1 point of model.

keep_surface_point = .false.

add_center_point

If .true., add a point at r=0.

add_center_point = .true.

Oscillation calculation controls

oscillation_code

Either 'adipls' or 'gyre' (lower case).

oscillation_code = 'adipls'

trace_time_in_oscillation_code

trace_time_in_oscillation_code = .false.

GYRE controls

The GYRE controls are read from the gyre_input_file. Rich offers the following comments on setting them:

> I suggest setting freq_min to 0.9*MINVAL(l0_obs), > and freq_max to 1.1*MAXVAL(l0_obs) > (similarly for the other l values).

> freq_units should be ‘UHZ’, > and set grid_type to ‘LINEAR’.

> For n_freq, I suggest either setting it to 10*(freq_max - freq_min)/dfreq, > where dfreq is the estimated frequency spacing; or, set it to 10*nl0. > The factor 10 is arbitrary, but seems to be a good safety factor.

gyre_input_file

gyre_input_file = 'gyre.in'

gyre_non_ad

gyre_non_ad = .false.

ADIPLS controls

Some ADIPLS controls are set here. The rest are set in adipls.c.pruned.in.

ADIPLS looks for frequencies in a given range and with a given “density” of coverage. For example, for l=0, the ADIPLS frequency search range is nu_lower_factor*l0_obs(1) to nu_upper_factor*l0_obs(nl0) and it uses iscan = iscan_factor_l0*nl0 to determine how fine the scan is over the range.

do_redistribute_mesh

The number of zones for ADIPLS’ remeshing is set in redistrb.c.pruned.in. If you set this .false., then the mesh from star is used directly. If you set this .true., then astero calls ADIPLS’s redistb before doing the frequency analysis.

do_redistribute_mesh = .true.

iscan_factor

ADIPLS will scan for frequencies at iscan_factor(l) times the number of observed l modes.

iscan_factor(0) = 15
iscan_factor(1) = 15
iscan_factor(2) = 15
iscan_factor(3) = 15

nu_lower_factor

nu_upper_factor

The frequency scan range is set from the observed frequencies times these factors.

nu_lower_factor = 0.8
nu_upper_factor = 1.2

adipls_irotkr

adipls_nprtkr

adipls_igm1kr

adipls_npgmkr

Miscellaneous ADIPLS parameters for experts.

adipls_irotkr = 0
adipls_nprtkr = 0
adipls_igm1kr = 0
adipls_npgmkr = 0

Include other inlists

read_extra_astero_search_inlist(1..5)

extra_astero_search_inlist_name(1..5)

If read_extra_astero_search_inlist(i) is .true., then read the namelist in file extra_astero_search_inlist_name(i).

read_extra_astero_search_inlist(:) = .false.
extra_astero_search_inlist_name(:) = 'undefined'