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