Default boundaries, spaces, and priors#
This page collects the default parameter properties used by PyORBIT when the
configuration file does not override them with boundaries, spaces, priors,
or fixed.
The model pages explain when a parameter is activated. This page is meant as a compact lookup table for the defaults attached to the common objects and to the few model-local parameters that define their own default properties.
Important
Boundaries and priors in the YAML file must be written in the physical (or natural) parameter space. This is also true when the parameter is sampled in a logarithmic space. In that case, boundaries must be strictly positive.
In the tables below:
Uniform []means a uniform prior over the listed boundaries, with no extra prior hyperparameters.Nonein theFixedcolumn means that the parameter has no predefined value when fixed by the user.dynamicmeans that the numerical boundary is computed from the dataset or from another model at initialisation time.Pattern rows such as
poly_c{0..9}represent all parameters in the indicated range.
Dataset systematics#
Each dataset will include one offset and one jitter parameter for each active flag in
the input file. The actual parameter names are offset_0, offset_1, … and
jitter_0, jitter_1, …
Parameter |
Boundaries |
Space |
Prior |
Fixed |
Notes |
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Same boundary logic as |
Offset boundaries#
The boundaries associated with each offset will be as large as possible to accommodate improbable or anomalous situations, such as eccentric planets or massive long-period companions with partial RV coverage.
Given x as the array with the time of the observations, y as the array with the data, and e as the corresponding error, the boundaries for the offset are determined in the following way:
x_range = np.max(x) - np.min(x)
y_range = np.max(y) - np.min(y)
y_trend = np.abs(y_range/x_range)
y_diff = np.abs(np.mean(x)-Tref) * y_trend + 1000.
offset_boundaries = [np.min(y) - 10.*y_diff, np.max(y) + 10.*y_diff]
where \(T_{\rm ref}\) is the reference time specified in the parame
Broad boundaries do not represent a problem when running an MCMC sampler, given an adequate starting point. When running pyDE+emcee, for example, you must allow a sufficiently large number of generations for the pyDE code to properly explore the parameter space; typically, 50000 generations is a good starting value.
Jitter boundaries#
The jitter boundaries are computed dynamically by taking the maximum error in the array as the reference and multiplying or dividing by 100 to set the upper or lower limits, respectively.
jitter_boundaries = [min(e)/100, 100 max(e)]
The lower boundary is intentionally set to a value other than zero to avoid problems for datasets where the jitter parameter is explored in logarithmic space. Since jitter is added in quadrature to the formal errors, a value much smaller than the error has the same effect as no jitter at all. Hence, in most cases it is not necessary to change the default lower limit to zero.
Changing the boundaries#
Offset and jitter boundaries are determined using the whole dataset, without distinction by the flag specified in the 4th and 5th columns (see Prepare a dataset file )
LEt’s take the case when a dataset is using two flags for jitter and two flags for offset, e.g., when putting together data collected with HARPS and HARPS-N but you want to use a single covariance matrix for the stellar activity. You can specify different boundaries for each distinct jitter and offset parameters:
1inputs:
2 RVdata:
3 file: datasets/K2-141_RV_PyORBIT.dat
4 kind: RV
5 models:
6 - radial_velocities
7 - tinygp_quasiperiodic
8 boundaries:
9 jitter_0: [ 0.00, 10.0]
10 jitter_1: [ 0.00, 20.0]
11 offset_0: [ -3500.0, -3300.0]
12 offset_1: [ -4000.0, -3000.0]
----- dataset: RVdata
jitter_0 id: 11 s:Linear b:[ 0.0000, 10.0000] p:Uniform []
jitter_1 id: 12 s:Linear b:[ 0.0000, 20.0000] p:Uniform []
offset_0 id: 13 s:Linear b:[ -3500.0000, -3300.0000] p:Uniform []
offset_1 id: 14 s:Linear b:[ -4000.0000, -3000.0000] p:Uniform []
If you don’t specify the boundaries for one of the parameters, the automa
8 boundaries:
9 #jitter_0: [ 0.00, 10.0]
10 jitter_1: [ 0.00, 20.0]
11 #offset_0: [ -3500.0, -3300.0]
12 offset_1: [ -4000.0, -3000.0]
----- dataset: RVdata
jitter_0 id: 11 s:Linear b:[ 0.0120, 1088.3783] p:Uniform []
jitter_1 id: 12 s:Linear b:[ 0.0000, 20.0000] p:Uniform []
offset_0 id: 13 s:Linear b:[ -13430.6237, 6632.5000] p:Uniform []
offset_1 id: 14 s:Linear b:[ -4000.0000, -3000.0000] p:Uniform []
To apply the same boundaries:
8 boundaries:
9 jitter: [ 0.00, 10.0]
10 offset: [ -3500.0, -3300.0]
----- dataset: RVdata
jitter_0 id: 11 s:Linear b:[ 0.0000, 10.0000] p:Uniform []
jitter_1 id: 12 s:Linear b:[ 0.0000, 10.0000] p:Uniform []
offset_0 id: 13 s:Linear b:[ -3500.0000, -3300.0000] p:Uniform []
offset_1 id: 14 s:Linear b:[ -3500.0000, -3300.0000] p:Uniform []
The same considerations above applies also to spaces and priors, for example:
8 boundaries:
9 jitter: [ 0.00, 10.0]
10 offset: [ -3500.0, -3300.0]
11 priors:
12 jitter: ['HalfGaussian', 0.00, 10.0]
13 offset: ['Gaussian', -3450.0, 10.0]
----- dataset: RVdata
jitter_0 id: 11 s:Linear b:[ 0.0000, 10.0000] p:HalfGaussian [ 0. 10.]
jitter_1 id: 12 s:Linear b:[ 0.0000, 10.0000] p:HalfGaussian [ 0. 10.]
offset_0 id: 13 s:Linear b:[ -3500.0000, -3300.0000] p:Gaussian [-3450. 10.]
offset_1 id: 14 s:Linear b:[ -3500.0000, -3300.0000] p:Gaussian [-3450. 10.]
Planets#
Common object: common: planets.
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Prior |
Fixed |
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Star Parameters#
Common object: common: star: star_parameters.
Parameter |
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Prior |
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Stellar Activity#
Common object: common: activity.
Parameter |
Boundaries |
Space |
Prior |
Fixed |
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Limb Darkening#
Common objects: ld_linear, ld_quadratic, ld_square-root,
ld_logarithmic, ld_exponential, ld_power2, and ld_nonlinear.
Common object |
Parameter |
Boundaries |
Space |
Prior |
Fixed |
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two-coefficient laws |
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two-coefficient laws |
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two-coefficient laws |
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two-coefficient laws |
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The two-coefficient laws are ld_quadratic, ld_square-root,
ld_logarithmic, ld_exponential, and ld_power2.
Normalization, Dilution, Offset, and Jitter#
Common object |
Parameter |
Boundaries |
Space |
Prior |
Fixed |
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common_offset and common_jitter inherit their numerical boundaries from the
datasets that use them. If more than one dataset shares the same common
parameter, the boundary is expanded to include all relevant datasets.
Polynomial and Detrending Models#
Common objects: polynomial_trend, detrending, and lightcurve_detrending.
Common object |
Parameter |
Boundaries |
Space |
Prior |
Fixed |
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Correlation Models#
Common objects: correlation and complex_correlation.
Model or common object |
Parameter |
Boundaries |
Space |
Prior |
Fixed |
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The local_correlated_jitter coefficients are model-local rather than common
object parameters. Their highest order is set by the model keyword order.
Harmonics and Sinusoids#
Common objects: harmonics and sinusoid.
Common object |
Parameter |
Boundaries |
Space |
Prior |
Fixed |
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CHEOPS and CCF Parameters#
Common objects: cheops_modelling and ccf_parameters.
Common object |
Parameter |
Boundaries |
Space |
Prior |
Fixed |
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Model Families#
Most entries in the models section do not define new boundaries directly.
They select which common-object parameters are sampled and whether those
parameters are shared among datasets or local to a dataset.
Model family |
Parameter defaults used |
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Multidimensional GP models |
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The model-local parameters listed in Correlation Models. |
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