ExoJAX 0.5.5

Auto-differentiable line-by-line spectral modeling of exoplanets/brown dwarfs using JAX.

exojax

Auto-differentiable line-by-line spectral modeling of exoplanets/brown dwarfs using JAX. Read the docs 🐕.

Functions

Voigt Profile :heavy_check_mark:

from exojax.spec import voigt
nu=numpy.linspace(-10,10,100)
voigt(nu,1.0,2.0) #sigma_D=1.0, gamma_L=2.0

Cross Section using HITRAN/HITEMP/ExoMol :heavy_check_mark:

from exojax.spec import AutoXS
nus=numpy.linspace(1900.0,2300.0,40000,dtype=numpy.float64) #wavenumber (cm-1)
autoxs=AutoXS(nus,"ExoMol","CO") #using ExoMol CO (12C-16O). HITRAN and HITEMP are also supported.  
xsv=autoxs.xsection(1000.0,1.0) #cross section for 1000K, 1bar (cm2)

Do you just want to plot the line strength?

ls=autoxs.linest(1000.0,1.0) #line strength for 1000K, 1bar (cm)
plt.plot(autoxs.mdb.nu_lines,ls,".")

autoxs.mdb is the moldb.MdbExomol class for molecular database. Here is a entrance to a deeper level. exojax is more flexible in the way it calculates the molecular lines. 🐈 Go to the docs for the deeper level.

Emission Spectrum :heavy_check_mark:

from exojax.spec.rtransfer import nugrid
from exojax.spec import AutoRT
nus,wav,res=nugrid(1900.0,2300.0,40000,"cm-1")
Parr=numpy.logspace(-8,2,100) #100 layers from 10^-8 bar to 10^2 bar
Tarr = 500.*(Parr/Parr[-1])**0.02    
autort=AutoRT(nus,1.e5,2.33,Tarr,Parr) #g=1.e5 cm/s2, mmw=2.33
autort.addcia("H2-H2",0.74,0.74)       #CIA, mmr(H)=0.74
autort.addcia("H2-He",0.74,0.25)       #CIA, mmr(He)=0.25
autort.addmol("ExoMol","CO",0.01)      #CO line, mmr(CO)=0.01
F=autort.rtrun()

Are you an observer?

nusobs=numpy.linspace(1900.0,2300.0,10000,dtype=numpy.float64) #observation wavenumber bin (cm-1)
F=autort.spectrum(nusobs,100000.0,20.0,0.0) #R=100000, vsini=10km/s, RV=0km/s

If you want to customize the model, see here.

HMC-NUTS of Emission Spectra :heavy_check_mark:

To fit a spectrum model to real data, you need to know a little more about exojax. See here.

🥥 HMC-NUTS modeling of a brown dwarf, Luhman 16 A using exojax. See here for an example of the Bayes inference using the real spectrum.

HMC-NUTS of Transmission Spectra :x:

Not supported yet.

Cloud modeling :x:

Not supported yet.

Installation

pip install exojax

or

python setup.py install

Note on installation w/ GPU support

:books: You need to install CUDA, NumPyro, JAX w/ NVIDIA GPU support, and cuDNN.

• NumPyro

exojax supports NumPyro >0.5.0, which enables the forward differentiation of HMC-NUTS. Please check the required JAX version by NumPyro. In May 2021, it seems the recent version of NumPyro requires jaxlib>=0.1.62 (see setup.py of NumPyro for instance).

• JAX

Check you cuda version:

nvcc -V

Install such as

pip install --upgrade jax jaxlib==0.1.62+cuda112  -f https://storage.googleapis.com/jax-releases/jax_releases.html

In this case, jaxlib version is 0.1.62 and cuda version is 11.2. You can check which cuda version is avaiable at here

Visit here for the details.

• cuDNN

For instance, get .deb from NVIDIA and install such as

sudo dpkg -i libcudnn8_8.2.0.53-1+cuda11.3_amd64.deb

cuDNN is used for to compute the astronomical/instrumental response for the large number of wave number grid (exojax.spec.response). Otherwise, we do not use it.

References

• Kawahara, Kawashima, Masuda, and Crossfield (2021) under review: arXiv:2105.14782

License

🐈 Copyright 2020-2021 Hajime Kawahara. exojax is publicly available under the MIT license. Under development since Dec. 2020.