Combined radial velocity and light curve fit#

To perform a combined radial velocity and photometry fit we just need to merge the two configuration files:

 1inputs:
 2  LCdata_TESS:
 3    file: ./HD189733_TESS_PyORBIT.dat
 4    kind: Phot
 5    models:
 6      - lc_model
 7  Bouchy2005_RV01_noRML:
 8    file: Bouchy2005_RV01_noRML_PyORBIT.dat
 9    kind: RV
10    models:
11      - radial_velocities
12common:
13  planets:
14    b:
15      orbit: circular
16      use_time_inferior_conjunction: True
17      boundaries:
18        P: [2.2185600, 2.2185800]
19        Tc: [2459770.4100, 2459770.4110]
20        K: [0.01, 300.0]
21      spaces:
22        P: Linear
23  star:
24    star_parameters:
25      priors:
26        mass: ['Gaussian', 0.806, 0.048]
27        radius: ['Gaussian', 0.756, 0.018]
28        density: ['Gaussian', 1.864, 0.175] #in Solar unit!!!!!!!
29    limb_darkening:
30      model: ld_quadratic
31      parametrization: Kipping
32models:
33  radial_velocities:
34    planets:
35      - b
36  lc_model:
37    model: batman_transit
38    limb_darkening: limb_darkening
39    planets:
40      - b
41parameters:
42  Tref: 2459750.00
43solver:
44  pyde:
45    ngen: 50000
46    npop_mult: 4
47  emcee:
48    npop_mult: 4
49    nsteps: 100000
50    nburn: 20000
51    nsave: 10000
52    thin: 100
53  nested_sampling:
54    nlive: 1000
55  recenter_bounds: True

In other words:

  • We include both datasets in the input sections

  • We specify the boundaries for the planetary parameters that are involved in either the radial velocity modelling or the light curve modelling

  • We specify both models in models.

The code will compute the likelihood using all the available data automatically, delivering a single set of orbital parameters able to reproduce the observed characteristics in all datasets.

Finally, you can add as many datasets and model as you wish:

 1inputs:
 2  Bouchy2005_RV01_noRML:
 3    file: Bouchy2005_RV01_noRML_PyORBIT.dat
 4    kind: RV
 5    models:
 6      - radial_velocities
 7  Winn2006_RV02_noRML:
 8    file: Winn2006_RV02_noRML_PyORBIT.dat
 9    kind: RV
10    models:
11      - radial_velocities
12  Boisse2009_RV03_noRML:
13    file: Boisse2009_RV03_noRML_PyORBIT.dat
14    kind: RV
15    models:
16      - radial_velocities
17  LCdata_TESS:
18    file: HD189733_TESS_PyORBIT.dat
19    kind: Phot
20    models:
21      - lc_model_TESS
22  LCdata_Bakos2006_LC06:
23    file: Bakos2006_LC06_PyORBIT.dat
24    kind: Phot
25    models:
26      - lc_model_Ic_Cousins
27  LCdata_Bakos2006_LC07:
28    file: Bakos2006_LC07_PyORBIT.dat
29    kind: Phot
30    models:
31      - lc_model_Ic_Cousins
32  LCdata_Bakos2006_LC08:
33    file: Bakos2006_LC08_PyORBIT.dat
34    kind: Phot
35    models:
36      - lc_model_Ic_Cousins
37  LCdata_Bakos2006_LC09:
38    file: Bakos2006_LC09_PyORBIT.dat
39    kind: Phot
40    models:
41      - lc_model_Ic_Cousins
42common:
43  planets:
44    b:
45      orbit: keplerian
46      parametrization: Eastman2013
47      use_time_inferior_conjunction: True
48      boundaries:
49        P: [2.2185600, 2.2185800]
50        Tc: [2459770.4100, 2459770.4110]
51        K: [0.01, 300.0]
52        e: [0.00, 0.95]
53      spaces:
54        P: Linear
55  star:
56    star_parameters:
57      priors:
58        mass: ['Gaussian', 0.806, 0.048]
59        radius: ['Gaussian', 0.756, 0.018]
60        density: ['Gaussian', 1.864, 0.175] #in Solar unit!!!!!!!
61    limb_darkening_TESS:
62      model: ld_quadratic
63      parametrization: Kipping
64    limb_darkening_Ic_Cousins:
65      type: ld_quadratic
66      #parametrization: Kipping
67      priors:
68        ld_c1: ['Gaussian', 0.45, 0.05]
69        ld_c2: ['Gaussian', 0.13, 0.05]
70models:
71  radial_velocities:
72    planets:
73      - b
74  lc_model_TESS:
75    model: batman_transit
76    limb_darkening: limb_darkening_TESS
77    planets:
78      - b
79  lc_model_Ic_Cousins:
80    kind: batman_transit
81    limb_darkening: limb_darkening_Ic_Cousins
82    planets:
83      - b
84parameters:
85  Tref: 2459750.00
86solver:
87  pyde:
88    ngen: 50000
89    npop_mult: 4
90  emcee:
91    npop_mult: 4
92    nsteps: 100000
93    nburn: 20000
94    nsave: 10000
95    thin: 100
96  nested_sampling:
97    nlive: 1000
98  recenter_bounds: True