Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry

OVERVIEW

The substellar atmospheric models described in Mukherjee et al. (2022) are presented here. The grid of these 1D radiative-convective atmospheric models was computed using the newly released open-source climate code PICASO 3.0 (Mukherjee et al. (2022), Batalha et al. (2019)). The grid consists of four parameters – the effective temperature (T_eff), gravity (log(g)), K_zz in the radiative zones, and mixing length in the convective zones. Models with T_eff  between 400-1000 K with an increment of 25 K are included. log(g) has been varied from 4.5 to 5.5 with an increment of 0.25 dex. The K_zz in the radiative zone has been varied between 1x, 0.01x, and 100x the parametrization presented in Moses et al. (2021), whereas the convective mixing length has been between the atmospheric pressure scale height and 0.1x the scale height.

There are three types of files released here – atmospheric composition files (TP_chemistry), thermal emission spectra files (spectra), and atmospheric Kzz profile files (TP_kz). 

ATMOSPHERIC COMPOSITION

The atmospheric composition files are located in the folder TP_chemistry.  These files have the temperature structure of the atmosphere as a function of pressure accompanied by the volume mixing ratio of 37 gases as a function of pressure. 

The TP_chemistry files are named following the format “profile_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat", where [factor1] denotes the multiplier used for the radiative zone Kzz and can vary between ‘x0pt01’, ‘x1’, and ‘x100’. [factor2] denotes the multiplier for the mixing length and can vary between ‘1’ and ‘0pt1’. [Teff] and [gravity] denote the Teff and gravity of the models used. A simple code snippet to read and plot these files is presented below.

SPECTRA

The spectra files are located in the folder "spectra_highres_1", "spectra_highres_2", "spectra_highres_3", and "spectra_highres_4". These files have the thermal emission spectra between 0.3-30 microns calculated using the computed models. The native spectral resolution of these calculations is at an R = 500,000, but please be aware that these spectra should always be binned down to a resolution of R = 50,000 or less before usage. This means that these spectra should only be used to interpret datasets with a spectral resolution of 50,000 or less. Please contact the authors if higher resolution spectra are needed. The spectra have been uploaded in three different folders to make the file sizes manageable for transfer.

The spectra files are also similarly named using the format “spectra_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.tar.gz". These files can be directly read into a Python pandas dataframe using 

pd.read_csv(filename, compression='gzip')

 The first column of the file is wavenumbers in cm^-1, which can be converted to wavelength in microns by wavelength [microns] =10000/wavenumbers[cm^-1].   The second column of the file is flux in erg/s/cm²/cm. Note that these fluxes need to be multiplied with R²/D²  before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here. A tutorial to convert these fluxes to other units is present in this link. A binned-down version (R=15,000) of these high-resolution spectra can also be found in the "spectra_lowres" folder. These can be used for datasets that have a maximum spectral resolution of 15,000.

K_zz PROFILE

The K_zz  as a function of pressure for each model is presented in these files. The K_zz is reported in cm²/s. These files are also similarly named using the format “kz_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat". The columns of the files are pressure in bars, the temperature in K, and Kzz in cm²/s.

EXAMPLE PYTHON CODE TO READ AND PLOT COMPOSITION FILES

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


grav = np.array([316,562,1000,1780,3160])
Teff=np.array([400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000])
factor1 = np.array(['x0pt01','x1','x100'])
factor2 = np.array(['1','0pt1'])
file ="profile_sc_qt_rz_"+factor1[0]+"_cz_"+factor2[0]+"_"+str(Teff[14])+"_grav_"+str(grav[14])+"_mh_+0.0_sm_NA.dat"

df = pd.read_csv(file, sep="\t")

# Plot T(P) profile
plt.ylim(100,1e-4)
plt.semilogy(df['temperature'],df['pressure'])
plt.show()

# Plot H2O mixing ratio profile
plt.ylim(100,1e-4)
plt.loglog(df['H2O'],df['pressure'])
plt.show()

CREDITS

If you use these tables, please cite Mukherjee et al. (2022, Astrophysical Journal, in press.)