Local profiles behind the planet (Doppler Shadow)¶

In this notebook, we simulate a Sun-like star hosting a Jupiter-like planet, and compute the expected transit light curve and RV time series (standard QS FTS CCF). From the time-series of integrated CCFs we show how to obtain the local profiles.

In [1]:

import numpy as np
import matplotlib.pyplot as plt
from SOAP.visualizer import plot_shadow_map
from SOAP.utils import transit_durations
import SOAP
from IPython.display import HTML

import numpy as np import matplotlib.pyplot as plt from SOAP.visualizer import plot_shadow_map from SOAP.utils import transit_durations import SOAP from IPython.display import HTML

Warming up JIT-compiled functions...

In [2]:

# Star-like star, rigid rotation
radius=1                # Stellar radius [solar radii]
mass=1                  # Stellar mass [solar masses]
prot=24.47              # Stellar rotation period [days]
incl=45                 # Stellar inclination [degrees] -> 90 is equator-on, 0 is pole-on
logg= 4.4               # Stellar surface gravity [cgs]
feh= 0.00               # Stellar metallicity [dex]
Teff=5777               # Stellar effective temperature [K]
ldcn=[0.5, 0.16]       # Linear and quadratic limb-darkening coefficients (must agree with λ, see below)
law=1                   # Limb-darkening law (quadratic)
start_psi=0.0           # Starting phase of the star

# Mock jupiter-like planet close to the star
Pp=3                    # Orbital period [days]
e=0.0                   # Orbital eccentricity
w=90.0                  # Argument of periastron [degrees]
ip=90                   # Orbital inclination [degrees]
lbda=0                  # Sky-projected spin-orbit misalignement [degrees]
a=9                     # Semi-major axis [stellar radii]
Rp=0.15                 # Planetary radius [stellar radii]
Mp=317.8                # Planetary mass [Earth masses]

# Observational parameters
λ =[5882, 5902]         # Wavelength range [Angstrom]
Res=140000              # Instrumental resolution

# Star-like star, rigid rotation radius=1 # Stellar radius [solar radii] mass=1 # Stellar mass [solar masses] prot=24.47 # Stellar rotation period [days] incl=45 # Stellar inclination [degrees] -> 90 is equator-on, 0 is pole-on logg= 4.4 # Stellar surface gravity [cgs] feh= 0.00 # Stellar metallicity [dex] Teff=5777 # Stellar effective temperature [K] ldcn=[0.5, 0.16] # Linear and quadratic limb-darkening coefficients (must agree with λ, see below) law=1 # Limb-darkening law (quadratic) start_psi=0.0 # Starting phase of the star # Mock jupiter-like planet close to the star Pp=3 # Orbital period [days] e=0.0 # Orbital eccentricity w=90.0 # Argument of periastron [degrees] ip=90 # Orbital inclination [degrees] lbda=0 # Sky-projected spin-orbit misalignement [degrees] a=9 # Semi-major axis [stellar radii] Rp=0.15 # Planetary radius [stellar radii] Mp=317.8 # Planetary mass [Earth masses] # Observational parameters λ =[5882, 5902] # Wavelength range [Angstrom] Res=140000 # Instrumental resolution

Start the simulation¶

In [3]:

sim = SOAP.Simulation(wlll=(λ[1]+λ[0])/2, inst_reso=Res, grid=600, active_regions=[], ring=None)

sim = SOAP.Simulation(wlll=(λ[1]+λ[0])/2, inst_reso=Res, grid=600, active_regions=[], ring=None)

In [4]:

# Set the properties of the star and planet
sim.star.set(prot=prot,incl=incl,coeffs=ldcn,law=law, start_psi=start_psi,radius=radius,mass=mass,teff=Teff)
sim.planet.set(P=Pp,t0=start_psi,e=e,w=w,ip=ip,lbda=lbda,a=a,Rp=Rp,Mp=Mp)

# Set the properties of the star and planet sim.star.set(prot=prot,incl=incl,coeffs=ldcn,law=law, start_psi=start_psi,radius=radius,mass=mass,teff=Teff) sim.planet.set(P=Pp,t0=start_psi,e=e,w=w,ip=ip,lbda=lbda,a=a,Rp=Rp,Mp=Mp)

In [5]:

# Provide the range of stellar phases to simulate
psi = np.linspace(-0.1,0.1,50) / sim.star.prot

# Provide the range of stellar phases to simulate psi = np.linspace(-0.1,0.1,50) / sim.star.prot

In [6]:

# Calculate the transit signal
out=sim.calculate_signal(psi, renormalize_rv=True)

# Calculate the transit signal out=sim.calculate_signal(psi, renormalize_rv=True)

Compute the local profiles and plot them¶

SOAP provides the time series of integrated spectra/CCF weighted by the flux (sim.integrated_spectra_fw) as well as the master-out (sim.master_out_fw). Constructing it in the traditional sense as it is done in literature allows us to explore the impact of stellar activity in all the steps to retrieve the behind the planet spectra.

In [7]:

points_to_plot = sim.master_out_fw - sim.integrated_spectra_fw

points_to_plot = sim.master_out_fw - sim.integrated_spectra_fw

Plot the 2D map of the local profiles

In [8]:

plot_shadow_map(sim,psi);

plot_shadow_map(sim,psi);

Plot the transit scheme

In [9]:

sim.visualize(output=out, plot_type="shadow")

sim.visualize(output=out, plot_type="shadow")