Adding C/O vs O/H plots to understand behaviour of Carbon relative to…

… the poor comparison with observations in the gas phase

colibre/auto_plotter/metallicity.yml

@@ -754,19 +754,19 @@ gas_metallicity_gas_carbon_over_oxygen_lom_50_kpc:
   observational_data:
     - filename: GalaxyStellarMassGasMetallicity/Nicholls_2017a.hdf5
 
-gas_metallicity_gas_carbon_over_oxygen_lofloor_50_kpc:
+gas_metallicity_gas_carbon_over_oxygen_total_lom_50_kpc:
   type: "scatter"
   legend_loc: "upper left"
   selection_mask: "derived_quantities.has_cold_dense_gas_50_kpc"
-  comment: "All galaxies, LoM, Diffuse"
+  comment: "All galaxies, LoM, Total (Dust + Diffuse)"
   y:
-    quantity: "derived_quantities.gas_c_over_o_abundance_avglog_low_50_kpc"
+    quantity: "derived_quantities.gas_c_over_o_total_abundance_avglin_50_kpc"
     log: false
     units: "dimensionless"
     start: -2
     end: 1
   x:
-    quantity: "derived_quantities.gas_o_abundance_avglog_low_50_kpc"
+    quantity: "derived_quantities.gas_o_abundance_avglin_50_kpc"
     log: false
     units: "dimensionless"
     start: 7
@@ -783,48 +783,13 @@ gas_metallicity_gas_carbon_over_oxygen_lofloor_50_kpc:
       value: 10
       units: "dimensionless"
   metadata:
-    title: "Diffuse Gas metallicity - Diffuse Gas Carbon over Oxygen relation (mean-of-log, 50 kpc aperture)"
-    caption: Only shown for galaxies with cold, dense gas. Metallicity is represented by 12 + $\log_{10}$ O/H (where $\log_{10}$ O/H is averaged between gas particles with a [O/H]=-4 floor for diffuse O) of the cold, dense gas ($T < 10^{4.5}\;{\rm K}$,  $n_{\rm H} > 0.1 \; {\rm cm^{-3}}$). The floor values are set to 1e-4 x (C/O)sun and 1e-4 x (O/H)sun. All haloes are plotted, including subhaloes. This uses depleted gas metallicity, i.e. it does not include metals that are present in dust.
+    title: "Diffuse Gas metallicity - (Dust + Diffuse) Gas Carbon over Oxygen relation (log-of-mean, 50 kpc aperture, only cold dense gas)"
+    caption: Only shown for galaxies with cold, dense gas. Metallicity is represented by 12 + $\log_{10}$ O/H (where $\log_{10}$ O/H is averaged between gas particles for diffuse O) of the cold, dense gas ($T < 10^{4.5}\;{\rm K}$,  $n_{\rm H} > 0.1 \; {\rm cm^{-3}}$). No minimum metallicity is imposed. All haloes are plotted, including subhaloes. This uses total gas metallicity for C/O, i.e. it includes metals that are present in dust.
     section: Gas Metallicity
     show_on_webpage: true
   observational_data:
     - filename: GalaxyStellarMassGasMetallicity/Nicholls_2017a.hdf5
 
-gas_metallicity_gas_carbon_over_oxygen_hifloor_50_kpc:
-  type: "scatter"
-  legend_loc: "upper left"
-  selection_mask: "derived_quantities.has_cold_dense_gas_50_kpc"
-  comment: "All galaxies, LoM, Diffuse"
-  y:
-    quantity: "derived_quantities.gas_c_over_o_abundance_avglog_high_50_kpc"
-    log: false
-    units: "dimensionless"
-    start: -2
-    end: 1
-  x:
-    quantity: "derived_quantities.gas_o_abundance_avglog_high_50_kpc"
-    log: false
-    units: "dimensionless"
-    start: 7
-    end: 10
-  median:
-    plot: true
-    log: true
-    adaptive: true
-    number_of_bins: 30
-    start:
-      value: 7
-      units: "dimensionless"
-    end:
-      value: 10
-      units: "dimensionless"
-  metadata:
-    title: "Diffuse Gas metallicity - Diffuse Gas Carbon over Oxygen relation (mean-of-log, 50 kpc aperture)"
-    caption: Only shown for galaxies with cold, dense gas. Metallicity is represented by 12 + $\log_{10}$ O/H (where $\log_{10}$ O/H is averaged between gas particles with a [O/H]=-3 floor for diffuse O) of the cold, dense gas ($T < 10^{4.5}\;{\rm K}$,  $n_{\rm H} > 0.1 \; {\rm cm^{-3}}$). The floor values are set to 1e-3 x (C/O)sun and 1e-3 x (O/H)sun. All haloes are plotted, including subhaloes. This uses depleted gas metallicity, i.e. it does not include metals that are present in dust.
-    section: Gas Metallicity
-    show_on_webpage: true
-  observational_data:
-    - filename: GalaxyStellarMassGasMetallicity/Nicholls_2017a.hdf5
 
 stellar_mass_star_metallicity_50:
   type: "scatter"

colibre/config.yml

@@ -361,6 +361,24 @@ scripts:
       xvar: Fe_H
       yvar: C_Fe
       dataset: APOGEE
+  - filename: scripts/stellar_abundances.py
+    caption: '[Fe/H] vs [C/O] using Asplund et al. (2009) values for [O/H]Sun = 8.69 and [C/H]Sun = 8.43. The median [C/O] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
+    output_file: stellar_abundances_FeH_CO_APOGEE.png
+    section: Stellar Metal Abundances
+    title: '[Fe/H] vs [C/O]'
+    additional_arguments:
+      xvar: Fe_H
+      yvar: C_O
+      dataset: APOGEE
+  - filename: scripts/stellar_abundances.py
+    caption: '[O/H] vs [C/O] using Asplund et al. (2009) values for [O/H]Sun = 8.69 and [C/H]Sun = 8.43. The median [C/O] vs median [O/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
+    output_file: stellar_abundances_OH_CO_APOGEE.png
+    section: Stellar Metal Abundances
+    title: '[O/H] vs [C/O]'
+    additional_arguments:
+      xvar: O_H
+      yvar: C_O
+      dataset: APOGEE
   - filename: scripts/stellar_abundances.py
     caption: '[Fe/H] vs [N/Fe] using Asplund et al. (2009) values for [Fe/H]Sun = 7.5 and [N/H]Sun = 7.83. The median [N/Fe] vs median [Fe/H] is indicated by the solid curve(s). The scatter points show abundances of individual stellar particles. The observational data for MW compiles the data from the APOGEE survey (Holtzman et al. 2018) and AstroNN added-value catalog (Leung, H.W. & Bovy, Jo 2019b). We create 6 stellar distributions by selecting stars from APOGEE based on galactocentric radial & azimuthal cuts, and combine them in order to derive a joint stellar abundance distribution that gives less weight to stars in the solar vicinity. The resulting contours use a log scale with 0.2 bin size.'
     output_file: stellar_abundances_FeH_NFe_APOGEE.png

colibre/registration.py

@@ -963,6 +963,40 @@ def register_carbon_to_oxygen(self, catalogue, aperture_sizes):
         )
         setattr(self, f"has_cold_dense_gas_{aperture_size}_kpc", mask)
 
+    for aperture_size in aperture_sizes:
+
+        # Fetch C over O times gas mass computed in apertures. The
+        # mass ratio between N and O has already been accounted for.
+        # Note that here we are calling the total quantities
+        log_C_over_O_times_gas_mass = catalogue.get_quantity(
+            f"lin_element_ratios_times_masses.lin_C_over_O_total_times_gas_mass_{aperture_size}_kpc"
+        )
+
+        # Fetch gas mass in apertures
+        gas_cold_dense_mass = catalogue.get_quantity(
+            f"cold_dense_gas_properties.cold_dense_gas_mass_{aperture_size}_kpc"
+        )
+
+        # Compute gas-mass weighted O over H
+        log_C_over_O = unyt.unyt_array(
+            np.zeros_like(gas_cold_dense_mass), "dimensionless"
+        )
+        # Avoid division by zero
+        mask = gas_cold_dense_mass > 0.0 * gas_cold_dense_mass.units
+        log_C_over_O[mask] = np.log10(
+            log_C_over_O_times_gas_mass[mask] / gas_cold_dense_mass[mask]
+        )
+
+        log_C_over_O.name = (
+            f"Total (Dust + Diffuse) Gas $\\log_{{10}}({{\\rm C/O}})$ ({aperture_size} kpc)"
+        )
+
+        # Register the field
+        setattr(
+            self, f"gas_c_over_o_total_abundance_avglin_{aperture_size}_kpc", log_C_over_O
+        )
+        setattr(self, f"has_cold_dense_gas_{aperture_size}_kpc", mask)
+
     # Loop over aperture average-of-log C/O-abundances
     for aperture_size in aperture_sizes:
 

colibre/scripts/stellar_abundances.py

@@ -88,6 +88,7 @@ def read_data(data, xvar, yvar):
     N_Fe_Sun = N_H_Sun_Asplund - Fe_H_Sun_Asplund - np.log10(mFe_in_cgs / mN_in_cgs)
     O_Fe_Sun = O_H_Sun_Asplund - Fe_H_Sun_Asplund - np.log10(mFe_in_cgs / mO_in_cgs)
     N_O_Sun = N_H_Sun_Asplund - O_H_Sun_Asplund - np.log10(mO_in_cgs / mN_in_cgs)
+    C_O_Sun = C_H_Sun_Asplund - O_H_Sun_Asplund - np.log10(mO_in_cgs / mC_in_cgs)
     Mg_Fe_Sun = Mg_H_Sun_Asplund - Fe_H_Sun_Asplund - np.log10(mFe_in_cgs / mMg_in_cgs)
 
     Si_Fe_Sun = Si_H_Sun_Asplund - Fe_H_Sun_Asplund - np.log10(mFe_in_cgs / mSi_in_cgs)
@@ -108,11 +109,13 @@ def read_data(data, xvar, yvar):
     if yvar == "N_O":
         nitrogen = data.stars.element_mass_fractions.nitrogen
         oxygen = data.stars.element_mass_fractions.oxygen
+    if yvar == "C_O":
+        carbon = data.stars.element_mass_fractions.carbon
+        oxygen = data.stars.element_mass_fractions.oxygen
     if yvar == "Mg_Fe":
         magnesium = data.stars.element_mass_fractions.magnesium
     if yvar == "Fe_SNIa_fraction":
         iron_snia = data.stars.iron_mass_fractions_from_snia
-
     if yvar == "Si_Fe":
         silicon = data.stars.element_mass_fractions.silicon
     if yvar == "Ne_Fe":
@@ -155,6 +158,12 @@ def read_data(data, xvar, yvar):
         N_O[nitrogen == 0] = -2  # set lower limit
         N_O[N_O < -2] = -2  # set lower limit
         yval = N_O
+    elif yvar == "C_O":
+        C_O = np.log10(carbon / oxygen) - C_O_Sun
+        C_O[oxygen == 0] = -2  # set lower limit
+        C_O[carbon == 0] = -2  # set lower limit
+        C_O[C_O < -2] = -2  # set lower limit
+        yval = C_O
     elif yvar == "O_Fe":
         O_Fe = np.log10(oxygen / iron) - O_Fe_Sun
         O_Fe[iron == 0] = -2  # set lower limit
@@ -291,6 +300,10 @@ def read_data(data, xvar, yvar):
             observational_data = (
                 f"{path_to_obs_data}/data/StellarAbundances/APOGEE_data_NO.hdf5"
             )
+        elif yvar == "C_O":
+            observational_data = (
+                f"{path_to_obs_data}/data/StellarAbundances/APOGEE_data_CO.hdf5"
+            )
         elif yvar == "O_Fe":
             observational_data = (
                 f"{path_to_obs_data}/data/StellarAbundances/APOGEE_data_O.hdf5"
@@ -314,6 +327,10 @@ def read_data(data, xvar, yvar):
             observational_data = (
                 f"{path_to_obs_data}/data/StellarAbundances/APOGEE_data_NOOH.hdf5"
             )
+        elif yvar == "C_O":
+            observational_data = (
+                f"{path_to_obs_data}/data/StellarAbundances/APOGEE_data_COOH.hdf5"
+            )
         elif yvar == "Mg_Fe":
             observational_data = (
                 f"{path_to_obs_data}/data/StellarAbundances/APOGEE_data_OHMGFE.hdf5"
@@ -451,6 +468,7 @@ def read_data(data, xvar, yvar):
     "C_Fe": "[C/Fe]",
     "N_Fe": "[N/Fe]",
     "N_O": "[N/O]",
+    "C_O": "[C/O]",
     "O_Fe": "[O/Fe]",
     "Mg_Fe": "[Mg/Fe]",
     "Si_Fe": "[Si/Fe]",
@@ -468,6 +486,7 @@ def read_data(data, xvar, yvar):
     "C_Fe": (-1.5, 1.5),
     "N_Fe": (-1.5, 1.5),
     "N_O": (-1.5, 1.5),
+    "C_O": (-1.5, 1.5),
     "O_Fe": (-1.5, 1.5),
     "Mg_Fe": (-1.5, 2.0),
     "Si_Fe": (-1.5, 2.0),