Resampled Opacity Database for PICASO

Wavelength range of the opacities go from 0.3-15 microns. Opacities are resampled to R=15,000 from an original R=1e6 line by line calculation performed by Richard Freedman, Ehsan Gharib-Nezhad, and Roxana Lupu. This does not mean that these opacities are suitable for data at R=15k!!!! Instead, resampled opacities have to be convolved to data that are at a resolution of to 100x less than the original data. This tutorial shows the effects of under-sampling opacities. TLDR: In general, this opacity dataset are suitable for data that is no higher than R=3000, if using the 60k database and R=100 if using the 20k database. It is not suitable for high resolution cross correlation studies.  

Want higher sampling? Download version 2: https://zenodo.org/record/3759675#.YuN4E-zMLvU 

Difference between this and V1? Addition of SO2, and updated CH4 (HITEMP as described in Mukherjee et al. 2024 https://ui.adsabs.harvard.edu/abs/2024ApJ...963...73M/abstract ) 

Using PICASO to download data

PICASO get_data function can help you make sense of all of these files: https://natashabatalha.github.io/picaso/installation.html#autodownloads 

"But my data is less then R=3000, is it possible to get a lower sampling?"Yes!

import picaso.justdoit as jdi

#will get everything as is
opa = jdi.opannection(filename_db = "all_opacities_0.6_6_R60000.db")

#will compute spectra for only a subset of wavelength
opa = jdi.opannection(filename_db = "all_opacities_0.6_6_R60000.db", wave_ranage=[0.6,1])

#will compute spectra for a smaller resolution sampling
opa = jdi.opannection(filename_db = "all_opacities_0.6_6_R60000.db", resample=2)
#resample =2 decreases the sampling by a factor of 2 (e.g. R=60000 -> R=30000)

Using PICASO to Query the data

A full tutorial on querying the database is available in the PICASO read the docs. Below is a brief example:

import picaso.opacity_factory as opa

db_filename = 'all_opacities_0.6_6_R60000.db'
molecules, pt_pairs = opa.molecular_avail(db_filename)
print(molecules)

['AlH', 'C2H2', 'C2H4', 'C2H6', 'CH4', 'CO', 'CO2', 'CaH', 'CrH', 'Cs', 'Fe', 'FeH', 'H2', 'H2O', 'H2S', 'H3+', 'HCN', 'K', 'Li', 'LiCl', 'LiF', 'LiH', 'MgH', 'N2', 'N2O', 'NH3', 'Na', 'O2', 'O3', 'OCS', 'PH3', 'Rb', 'SO2', 'SiO', 'TiH', 'TiO', 'VO']

pt_pairs[0:10]

[(1, 1e-06, 75.0),
(2, 3e-06, 75.0),
(3, 1e-05, 75.0),
(4, 3e-05, 75.0),
(5, 0.0001, 75.0),
(6, 0.0003, 75.0),
(7, 0.001, 75.0),
(8, 0.003, 75.0),
(9, 0.01, 75.0),
(10, 0.03, 75.0)]

As you can see from the pt_pairs, our grid is computed on a specific pressure-temperature grid that has a total of 1460 points. 

NOTE: A full table of references and citations is currently being compiled for publication with the Sonora Grid (Marley+2020).

PICASO Citation Tools

PICASO can help you get out individual citations. If you do not want to look at code:

you can find bibid's here for each molecule

corresponding PICASO bib file here 

If you do want to look at code you can check out this tutorial. (code snippet below)

import picaso.references as pref

refs = pref.References()
opa_latex, bibdb = refs.get_opa(molecules=['H2O','CO2'])

print(opa_latex)

\begin{table*}
\centering
\begin{tabular}{c|c}
H2O & \citet{Polyansky2018H2O} \\
CO2 & \citet{HUANG2014reliable} \\

\end{tabular}
\caption{Line lists used to make PICASO Opacities}
\label{tab:opas}
\end{table*}

pref.create_bib(bibdb, 'molecule.bib')#creates bibtex file