your work is very interesting for me.
I'm trying to figure out your code.
Where did you get the file ''betaPic_mag_res.dat"?
What is variable "ulimb" in function "diskintensity" and why it is =0.79? It value taken from papire Brogi 2012?
What are the parameters specified in the variable "par" they have labels : "t{mid}", "b", "c_e", "lambda","P", in part "Exocomet model"?
I don't understand where the variable 'theta" is defined for "lnlike()" functions. Can you tell where it comes from?
Thank you so much. I will be glad if you give an answer.
ulimb is the linear limb darkening coefficient. Limb darkening arises because stars aren’t uniformly bright disk but get fainter at the edges. This effect has to be taken into account when you fit transits.
We didn’t actually use ulimb=0.79 in the final paper. It’s just in the Jupyter Notebook to perform some initial estimates. But if you look at the _exocometfit.ipynb Notebook, you’ll see that _diskintensity is used in modelexi which fixes the ulimb value to 0.275 later. There are different approaches to get limb darkening parameters for your star. The one we used in the paper is based on Claret 2000. But you can also use for example EXOCTK (https://exoctk.stsci.edu/limb_darkening). Just enter the parameters of your star and you'll have your limb darkening parameter(s).
t_{mid}", "b", "c_e", "lambda","P":
t_mid is the transit time, it’s basically the time between the first dimming and when the star is back to full luminosity
Hello Dear Sebastian,
your work is very interesting for me. I'm trying to figure out your code. Where did you get the file ''betaPic_mag_res.dat"? What is variable "ulimb" in function "diskintensity" and why it is =0.79? It value taken from papire Brogi 2012? What are the parameters specified in the variable "par" they have labels : "t{mid}", "b", "c_e", "lambda","P", in part "Exocomet model"? I don't understand where the variable 'theta" is defined for "lnlike()" functions. Can you tell where it comes from?
Thank you so much. I will be glad if you give an answer.
Maksym Vasykenko