Derived fields in yt for RAMSES-RTZ

Overview

Make derived fields in yt for simulations run with the RAMSES-RTZ code. Specifically, the MEGATRON Simulation.

The functionality is ever-expanding, here are a few examples of what is currently available:

Chemistry derived fields

• Electron number density
• Mean molecular weight
• Metallicity

Cooling derived fields

• H2 cooling (two different models)
• Full metal cooling

Star derived fields

• Correct star particle ages
• Filters for Pop. II and Pop. III stars
• Boolean field that calculates whether a Pop. III star is still alive

Radiative transfer derived fields

• Energy density of the radiation field for each frequency bin

Spectral derived fields

• Emission line module for collisional and recombination lines in the optical and UV (requires atomic data generated by the chianti package, see below)
• Nebular and stellar continuum spectra (requires data on glamdring, see below)
• INCOMING: Corrections for unresolved stromgren spheres.
• INCOMING: Ability to generate IFU spectra using bins of cells.

How to use

1. Install yt: Since the changes to YT can be very specific to the MEGATRON simulation, it is recommended to use a branch of yt that has been modified for this purpose. The branch yt_megatron on Anatole Storck's yt fork in GitHub contains the necessary changes.

   git clone https://github.com/AnatoleStorck/yt.git
   cd yt
   git switch yt_megatron
   pip install -e .

2. Clone and install this repository and copy TOML specific to MEGATRON for global yt configuration. If you don't want to make the configuration global, you can also copy the yt.toml file to the current working directory.

   git clone https://github.com/AnatoleStorck/yt_derived_fields.git
   cd yt_derived_fields
   pip install -e .

   cp yt_derived_fields/megatron_derived_fields/yt.toml ~/.config/yt/.

3. Use the derived field functions: Generate new fields to be used with various yt functions. Chemistry fields include the electron number density, mean molecular weight, molecular hydrogen number density, and others. Cooling includes two H2 cooling fields, full metal cooling, and others. Star derived fields include a proper way to derive the star particle ages.

   import yt
   import pandas as pd
   
   import yt_derived_fields.megatron_derived_fields.chemistry_derived_fields as chem_fields
   import yt_derived_fields.megatron_derived_fields.cooling_derived_fields as cool_fields
   import yt_derived_fields.megatron_derived_fields.stars_derived_fields as star_fields
   
   ds = yt.load(<path_to_your_favorite_megatron_output>)
   hc = pd.read_csv(<path_to_your_halo_catalog_in_pandas_format>)
   
   chem_fields.create_chemistry_derived_fields(ds, molecules=True, electron_number_density=True, mean_molecular_weight=False)
   cool_fields.create_cooling_derived_fields(ds, H2_cooling="moseley")
   star_fields.create_stars_derived_fields(ds)
   
   some_halo = hc[hc["id"] == 42]
   halo_sphere = ds.sphere(([some_halo.x,
                             some_halo.y,
                             some_halo.z], "Mpccm/h"),
                            (some_halo.r200b, "kpccm/h"))
   
   # An example field from each module
   halo_sphere["gas", "electron_number_density"]
   halo_sphere["gas", "cooling_H2"]
   halo_sphere["pop3", "isAlive"]

4. Generate atomic data for observational derived fields (On glamdring, this is currently found in /mnt/glacier/chianti/, so no need to create a new database): The emission line module requires atomic data generated by the chianti package. Once a chianti database is generated (https://www.chiantidatabase.org/chianti_download.html), you can use the spectral_utils/generate_atomic_data.py script to generate the atomic data for the emission line module (make sure to replace the path at the top of the file to the database). Then you can use the emission line module as follows.

   import yt
   import pandas as pd
   
   import yt_derived_fields.megatron_derived_fields.emission_derived_fields as emiss_fields
   
   ds = yt.load(<path_to_your_favorite_megatron_output>)
   hc = pd.read_csv(<path_to_your_halo_catalog_in_pandas_format>)
   
   coll_lines = ["O3-5007", "S2-6731"]
   rec_lines = ["Lya", "Hb", "He-1640"]
   
   emiss_fields.get_emission_lines(ds, coll_lines=coll_lines, rec_lines=rec_lines)
   
   some_halo = hc[hc["id"] == 42]
   halo_sphere = ds.sphere(([some_halo.x,
                             some_halo.y,
                             some_halo.z], "Mpccm/h"),
                            (some_halo.r200b, "kpccm/h"))
   
   # The O3-5007 luminosity in every cell of the sphere
   halo_sphere["gas", "O3-5007_luminosity"]

5. More observational derived fields for continuum spectra (nebular and stellar): The nebular and stellar continuum modules require data to compute the spectra. On glamdring, these can be found in /mnt/glacier/DATA/.

   import yt
   import pandas as pd
   
   import yt_derived_fields.megatron_derived_fields.nebular_continuum_fields as neb_fields
   import yt_derived_fields.megatron_derived_fields.stellar_continuum_fields as stellar_spec_fields
   
   ds = yt.load(<path_to_your_favorite_megatron_output>)
   hc = pd.read_csv(<path_to_your_halo_catalog_in_pandas_format>)
   
   neb_fields.get_nebular_continuum(ds)
   stellar_spec_fields.get_stellar_continuum(ds)
   
   some_halo = hc[hc["id"] == 42]
   halo_sphere = ds.sphere(([some_halo.x,
                             some_halo.y,
                             some_halo.z], "Mpccm/h"),
                            (some_halo.r200b, "kpccm/h"))
   
   # The two continuum processes as spectra in every cell of the sphere
   halo_sphere["gas", "nebc_resolved_recomb"]
   halo_sphere["gas", "nebc_resolved_two_photon"]
   
   # The pop2 stellar continuum (not per cell [YET], summed across the sphere)
   halo_sphere["gas", "pop2_spectra"]
   
   
   # One can then generate a full single spectra of the halo by summing the spectra in every cell for the nebular continuum and all emission lines, along with the stellar continuum.