Forward Modelling of a Many Lines Spectrum using MODIT ====================================================== Here, we try to compute a emission spectrum using MODIT. .. code:: ipython3 from exojax.spec import rtransfer as rt from exojax.spec import modit from exojax.spec import lpf import numpy as np import matplotlib.pyplot as plt plt.style.use('bmh') .. code:: ipython3 #ATMOSPHERE NP=100 T0=1295.0 #K Parr, dParr, k=rt.pressure_layer(NP=NP) Tarr = T0*(Parr)**0.1 We set a wavenumber grid using wavenumber_grid. Specify xsmode=“dit” though it is not mandatory. DIT uses FFT, so the (internal) wavenumber grid should be linear. But, you can also use a nonlinear grid. In this case, the interpolation (jnp.interp) is used. .. code:: ipython3 from exojax.utils.grids import wavenumber_grid nus,wav,R=wavenumber_grid(22900,23000,10000,unit="AA",xsmode="modit") .. parsed-literal:: nugrid is log: mode= modit Loading a molecular database of CO and CIA (H2-H2)… .. code:: ipython3 from exojax.spec import api, contdb mdbCO=api.MdbExomol('.database/CO/12C-16O/Li2015',nus) cdbH2H2=contdb.CdbCIA('.database/H2-H2_2011.cia',nus) .. parsed-literal:: Background atmosphere: H2 Reading transition file .broad is used. Broadening code level= a0 default broadening parameters are used for 71 J lower states in 152 states H2-H2 .. code:: ipython3 from exojax.spec import molinfo molmassCO=molinfo.molmass("CO") Computing the relative partition function, .. code:: ipython3 from jax import vmap qt=vmap(mdbCO.qr_interp)(Tarr) Pressure and Natural broadenings .. code:: ipython3 from jax import jit from exojax.spec.exomol import gamma_exomol from exojax.spec import gamma_natural gammaLMP = jit(vmap(gamma_exomol,(0,0,None,None)))\ (Parr,Tarr,mdbCO.n_Texp,mdbCO.alpha_ref) gammaLMN=gamma_natural(mdbCO.A) gammaLM=gammaLMP+gammaLMN[None,:] MODIT uses the normalized gammaL. .. code:: ipython3 dv_lines=mdbCO.nu_lines/R ngammaLM=gammaLM/dv_lines normalized Doppler broadening is common for the lines, so we compute the vector for the layers. .. code:: ipython3 from exojax.spec import normalized_doppler_sigma nsigmaDl=normalized_doppler_sigma(Tarr,molmassCO,R)[:,np.newaxis] And line strength .. code:: ipython3 from exojax.spec import SijT SijM=jit(vmap(SijT,(0,None,None,None,0)))\ (Tarr,mdbCO.logsij0,mdbCO.nu_lines,mdbCO.elower,qt) MODIT requires the grids of ngammaL, and wavenumber. For the emission spectrum, this grids should be prepared for each layer. dit.dgmatrix can compute these grids. .. code:: ipython3 dgm_ngammaL=modit.dgmatrix(ngammaLM) #you can change the resolution #dgm_gammaL=dit.dgmatrix(ngammaLM,res=0.1) We can check how the grids are set for each layers using plot.ditplot.plot_dgm .. code:: ipython3 #show the DIT grids from exojax.plot.ditplot import plot_dgmn plot_dgmn(Parr,dgm_ngammaL,ngammaLM,80,100) .. image:: Forward_modeling_using_MODIT_files/Forward_modeling_using_MODIT_22_0.png Initialize modit! .. code:: ipython3 from exojax.spec import initspec cnu,indexnu,R,pmarray=initspec.init_modit(mdbCO.nu_lines,nus) Compute the cross section array! .. code:: ipython3 xsmmodit=modit.xsmatrix(cnu,indexnu,R,pmarray,nsigmaDl,ngammaLM,SijM,nus,dgm_ngammaL) Some elements may be small negative values because of error for DIT. you can just use jnp.abs .. code:: ipython3 import jax.numpy as jnp print(len(xsmmodit[xsmmodit<0.0]),"/",len((xsmmodit).flatten())) .. parsed-literal:: 149449 / 1000000 .. code:: ipython3 xsmmodit=jnp.abs(xsmmodit) We also compute the cross section using the direct computation (LPF) for the comparison purpose. .. code:: ipython3 #direct LPF for comparison #we need sigmaDM for LPF from exojax.spec import doppler_sigma sigmaDM=jit(vmap(doppler_sigma,(None,0,None)))\ (mdbCO.nu_lines,Tarr,molmassCO) from exojax.spec.lpf import xsmatrix numatrix=initspec.init_lpf(mdbCO.nu_lines,nus) xsmdirect=xsmatrix(numatrix,sigmaDM,gammaLM,SijM) BTW, if you are not busy, check the computational time for both LPF and MODIT. .. code:: ipython3 %timeit modit.xsmatrix(cnu,indexnu,R,pmarray,nsigmaDl,ngammaLM,SijM,nus,dgm_ngammaL) .. code:: ipython3 %timeit xsmatrix(numatrix,sigmaDM,gammaLM,SijM) .. parsed-literal:: 71.9 ms ± 1.17 ms per loop (mean ± std. dev. of 7 runs, 100 loops each) Let’s see the cross section matrix! .. code:: ipython3 import numpy as np import matplotlib.pyplot as plt fig=plt.figure(figsize=(20,4)) ax=fig.add_subplot(211) c=plt.imshow(np.log10(xsmmodit),cmap="bone_r",vmin=-23,vmax=-19) plt.colorbar(c,shrink=0.8) plt.text(50,30,"MODIT") ax.set_aspect(0.1/ax.get_data_ratio()) ax=fig.add_subplot(212) c=plt.imshow(np.log10(xsmdirect),cmap="bone_r",vmin=-23,vmax=-19) plt.colorbar(c,shrink=0.8) plt.text(50,30,"DIRECT") ax.set_aspect(0.1/ax.get_data_ratio()) plt.show() .. parsed-literal:: /tmp/ipykernel_39483/2412046399.py:5: RuntimeWarning: divide by zero encountered in log10 c=plt.imshow(np.log10(xsmmodit),cmap="bone_r",vmin=-23,vmax=-19) .. image:: Forward_modeling_using_MODIT_files/Forward_modeling_using_MODIT_36_1.png computing delta tau for CO .. code:: ipython3 from exojax.spec.rtransfer import dtauM Rp=0.88 Mp=33.2 g=2478.57730044555*Mp/Rp**2 #g=1.e5 #gravity cm/s2 MMR=0.0059 #mass mixing ratio dtaum=dtauM(dParr,xsmmodit,MMR*np.ones_like(Tarr),molmassCO,g) .. code:: ipython3 dtaumdirect=dtauM(dParr,xsmdirect,MMR*np.ones_like(Tarr),molmassCO,g) computing delta tau for CIA .. code:: ipython3 from exojax.spec.rtransfer import dtauCIA mmw=2.33 #mean molecular weight mmrH2=0.74 molmassH2=molinfo.molmass("H2") vmrH2=(mmrH2*mmw/molmassH2) #VMR dtaucH2H2=dtauCIA(nus,Tarr,Parr,dParr,vmrH2,vmrH2,\ mmw,g,cdbH2H2.nucia,cdbH2H2.tcia,cdbH2H2.logac) The total delta tau is a summation of them .. code:: ipython3 dtau=dtaum+dtaucH2H2 dtaudirect=dtaumdirect+dtaucH2H2 you can plot a contribution function using exojax.plot.atmplot .. code:: ipython3 from exojax.plot.atmplot import plotcf plotcf(nus,dtau,Tarr,Parr,dParr) plt.show() .. image:: Forward_modeling_using_MODIT_files/Forward_modeling_using_MODIT_45_0.png radiative transfering… .. code:: ipython3 from exojax.spec import planck from exojax.spec.rtransfer import rtrun sourcef = planck.piBarr(Tarr,nus) F0=rtrun(dtau,sourcef) F0direct=rtrun(dtaudirect,sourcef) The difference is very small except around the edge (even for this it’s only 1%). .. code:: ipython3 fig=plt.figure() ax=fig.add_subplot(211) plt.plot(wav[::-1],F0,label="MODIT") plt.plot(wav[::-1],F0direct,ls="dashed",label="direct") plt.legend() ax=fig.add_subplot(212) plt.plot(wav[::-1],(F0-F0direct)/np.median(F0direct)*100,label="MODIT") plt.legend() #plt.ylim(-0.1,0.1) plt.ylabel("residual (%)") plt.xlabel("wavelength ($\AA$)") plt.show() .. image:: Forward_modeling_using_MODIT_files/Forward_modeling_using_MODIT_49_0.png .. code:: ipython3 ax=fig.add_subplot(212) plt.plot(wav[::-1],(F0-F0direct)/np.median(F0direct)*100,label="MODIT") plt.legend() plt.ylim(-0.1,0.1) plt.xlim(22938,22945) plt.ylabel("residual (%)") plt.xlabel("wavelength ($\AA$)") plt.show() .. image:: Forward_modeling_using_MODIT_files/Forward_modeling_using_MODIT_50_0.png MODIT uses ESLOG as the wavenumebr grid. So, we can directly apply the response. applying an instrumental response and planet/stellar rotation to the raw spectrum .. code:: ipython3 from exojax.spec import response from exojax.utils.constants import c import jax.numpy as jnp wavd=jnp.linspace(22920,23000,500) #observational wavelength grid nusd = 1.e8/wavd[::-1] RV=10.0 #RV km/s vsini=20.0 #Vsini km/s u1=0.0 #limb darkening u1 u2=0.0 #limb darkening u2 Rinst=100000. beta=c/(2.0*np.sqrt(2.0*np.log(2.0))*Rinst) #IP sigma need check Frot=response.rigidrot(nus,F0,vsini,u1,u2) F=response.ipgauss_sampling(nusd,nus,Frot,beta,RV) .. code:: ipython3 plt.plot(wav[::-1],F0) plt.plot(wavd[::-1],F) plt.xlim(22920,23000) .. parsed-literal:: (22920.0, 23000.0) .. image:: Forward_modeling_using_MODIT_files/Forward_modeling_using_MODIT_54_1.png