name = "MOSDEF Survey: gas-phase metallicity of galaxies at 1.4 <= z <= 2.6"
plot_as = "line"
h = h_sim
name = f"Fit to the stellar mass - gas metallicity at z=[{redshift_header_info:s}]"
comment = (
"The data is taken from Chartab+21 "
"Median fit to galaxy stacks from MOSDEF survey. "
"Stellar masses obtained assuming a Chabrier IMF. "
"The metallicity is expressed as 12 + log10(O/H), in these units the solar metallicity is 8.69."
)
# Store metadata at the top level
multi_z = MultiRedshiftObservationalData()
multi_z.associate_citation(citation, bibcode)
multi_z.associate_name(name)
multi_z.associate_comment(comment)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(1)
output_filename = "Chartab2021.hdf5"
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
for z, dz_lower, dz_upper in zip(redshifts, redshifts_lower, redshifts_upper):
# Create a single observational-data instance at redshift z
processed = ObservationalData()
def passive_fractions_centrals():
# Meta-data
name = ("Fit to the passive fraction - stellar mass (centrals) "
f"at z=[{redshift_header_info:s}]")
comment = (
"The data is taken from https://www.peterbehroozi.com/data.html. "
"The quenched fractions are defined using the standard criterion where "
"specific star formation rate < 1e-11 yr^-1. "
"The stellar mass is the observed stellar mass as defined in Behroozi et al. "
"(2019) eq. 25. "
"Uses the Chabrier initial mass function. "
"The passive fractions are given by the 50th percentile of the posterior "
"distribution of the fitting model. "
"Cosmology: Omega_m=0.307, Omega_lambda=0.693, h=0.678, sigma_8=0.823, "
"n_s=0.96. "
"Shows the passive fraction of centrals versus galaxy stellar mass.")
# Store metadata at the top level
multi_z = MultiRedshiftObservationalData()
multi_z.associate_citation(citation, bibcode)
multi_z.associate_name(name)
multi_z.associate_comment(comment)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(1)
output_filename = "Behroozi2019_centrals.hdf5"
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
for z, dz_lower, dz_upper, a_str in zip(redshifts, redshifts_lower,
redshifts_upper,
scale_factors_str):
# Create a single observational-data instance at redshift z
processed = ObservationalData()
# Load raw Behroozi2019 data
data = np.loadtxt(f"../raw/Behroozi2019_qf_groupstats_a{a_str}.dat")
# Fetch the fields we need
log_M_star, QF, QF_plus, QF_minus = (
data[:, 0],
data[:, 1],
data[:, 2],
data[:, 3],
)
# We don't want to plot zeros
mask = np.where(QF > 0.0)
# Transform stellar mass
M_star = (10.0**log_M_star) * unyt.Solar_Mass
# Define scatter with respect to the best-fit value (16 and 84 percentiles)
QF_scatter = unyt.unyt_array((QF_minus[mask], QF_plus[mask]),
units="dimensionless")
# Compute \Delta z
redshift_lower, redshift_upper = [z - dz_lower, z + dz_upper]
processed.associate_x(
M_star[mask],
scatter=None,
comoving=False,
description="Galaxy Stellar Mass",
)
processed.associate_y(
QF[mask] * unyt.dimensionless,
scatter=QF_scatter,
comoving=False,
description="Passive Fraction (centrals)",
)
processed.associate_redshift(z, redshift_lower, redshift_upper)
processed.associate_plot_as(plot_as)
multi_z.associate_dataset(processed)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(filename=output_path)
def Phi_passive_galaxies():
# Meta-data
name = f"Fit to the quenched galaxy stellar mass function at z=[{redshift_header_info:s}]"
comment = (
"The data is taken from https://www.peterbehroozi.com/data.html. "
"The stellar mass is the observed stellar mass as defined in Behroozi et al. "
"(2019) eq. 25. "
"The quenched fractions are defined using the standard criterion where specific"
" star formation rate < 1e-11 yr^-1. "
"Uses the Chabrier initial mass function. "
"GSMF is incomplete below 10**7.0 Msun at z=0 and 10**8.5 Msum at z=8. "
"Cosmology: Omega_m=0.307, Omega_lambda=0.693, h=0.678, sigma_8=0.823, "
"n_s=0.96. "
"Shows the quenched galaxy stellar mass function (number densities in comoving"
" Mpc^-3 dex^-1 vs. stellar mass).")
# Store metadata at the top level
multi_z = MultiRedshiftObservationalData()
multi_z.associate_citation(citation, bibcode)
multi_z.associate_name(name)
multi_z.associate_comment(comment)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(1)
output_filename = "Behroozi2019_passive.hdf5"
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
for z, dz_lower, dz_upper, a_str in zip(redshifts, redshifts_lower,
redshifts_upper,
scale_factors_str):
# Create a single observational-data instance at redshift z
processed = ObservationalData()
# Load raw Behroozi2019 data
data = np.loadtxt(f"../raw/Behroozi2019_smf_a{a_str}.dat")
# Fetch the fields we need
log_M_star, Phi, Phi_plus, Phi_minus = (
data[:, 0],
data[:, 7],
data[:, 8],
data[:, 9],
)
# We don't want to plot zeros
mask = np.where(Phi > 0.0)
# Transform stellar mass
M_star = (10.0**log_M_star) * unyt.Solar_Mass
# Define scatter with respect to the best-fit value (16 and 84 percentiles)
Phi_scatter = unyt.unyt_array((Phi_minus[mask], Phi_plus[mask]),
units=unyt.Mpc**(-3))
# Compute \Delta z
redshift_lower, redshift_upper = [z - dz_lower, z + dz_upper]
processed.associate_x(
M_star[mask],
scatter=None,
comoving=False,
description="Galaxy Stellar Mass",
)
processed.associate_y(
Phi[mask] * (h_sim / ORIGINAL_H)**3 * unyt.Mpc**(-3),
scatter=Phi_scatter * (h_sim / ORIGINAL_H)**3,
comoving=True,
description="Phi (GSMF)",
)
processed.associate_redshift(z, redshift_lower, redshift_upper)
processed.associate_plot_as(plot_as)
multi_z.associate_dataset(processed)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(filename=output_path)
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
comment = ("Assuming Chabrier IMF, values calculated using 30 pkpc apertures. "
"Obtained from TNG data portal. "
f"h-corrected for SWIFT using Cosmology: {cosmology.name}.")
citation = "Pillepich et al. (2018)"
bibcode = "2018MNRAS.475..648P"
name = "GSMF from Illustris-TNG{box}"
for box_sz in box_sizes:
multi_z = MultiRedshiftObservationalData()
multi_z.associate_comment(comment)
multi_z.associate_name(name.format(box=box_sz))
multi_z.associate_citation(citation, bibcode)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(1)
z_bins, Mstar_bins, gsmf = load_file(input_filename.format(box=box_sz))
for z, gsmf_at_z in zip(z_bins, gsmf):
multi_z.associate_dataset(
process_for_redshift(z, Mstar_bins, gsmf_at_z))
output_path = f"{output_directory}/{output_filename.format(box=box_sz)}"
if os.path.exists(output_path):
os.remove(output_path)
plot_as = "points"
h_obs = 0.7
h = cosmology.h
stellar_mass_bin_range = unyt.unyt_array([10**9.6, 10**11.6], "Solar_Mass")
number_of_bins = 10
z = raw.T[1]
M = raw.T[2] * unyt.Solar_Mass
R = raw.T[3] * unyt.kpc
e_R = raw.T[4] * unyt.kpc
sf = raw.T[5].astype(bool)
multi_z_sf = MultiRedshiftObservationalData()
multi_z_sf.associate_comment(f"{comment} Includes SFing galaxies only.")
multi_z_sf.associate_name(f"{name} (SF)")
multi_z_sf.associate_citation(f"{citation} (SF)", bibcode)
multi_z_sf.associate_cosmology(cosmology)
multi_z_sf.associate_maximum_number_of_returns(1)
multi_z_nsf = MultiRedshiftObservationalData()
multi_z_nsf.associate_comment(f"{comment} Includes quiescent galaxies only.")
multi_z_nsf.associate_name(f"{name} (Q)")
multi_z_nsf.associate_citation(f"{citation} (Q)", bibcode)
multi_z_nsf.associate_cosmology(cosmology)
multi_z_nsf.associate_maximum_number_of_returns(1)
redshift_bins = [[0.3, 0.7], [0.7, 1.0], [1.0, 1.3], [1.3, 2.0]]
for redshift_bin in redshift_bins:
processed_sf = ObservationalData()
def StellarMassHaloMassRatios_vs_StellarMass():
name = f"Fit to the stellar mass / halo mass - stellar mass relation at z=[{redshift_header_info:s}]"
comment = (
"The data is taken from https://www.peterbehroozi.com/data.html. "
"Median fit to the raw data for centrals (i.e. excluding satellites). "
"The stellar mass is the true stellar mass (i.e. w/o observational "
"corrections). "
"The halo mass is the peak halo mass that follows the Bryan & Norman (1998) "
"spherical overdensity definition. "
"The fitting function does not include the intrahalo light contribution to the "
"stellar mass. "
"Cosmology: Omega_m=0.307, Omega_lambda=0.693, h=0.678, sigma_8=0.823, "
"n_s=0.96. "
"Shows the ratio between stellar mass and halo mass as a function of stellar "
"mass. ")
# Store metadata at the top level
multi_z = MultiRedshiftObservationalData()
multi_z.associate_citation(citation, bibcode)
multi_z.associate_name(name)
multi_z.associate_comment(comment)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(1)
output_filename = "Behroozi2019RatioStellar.hdf5"
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
for z, dz_lower, dz_upper in zip(redshifts, redshifts_lower,
redshifts_upper):
# Create a single observational-data instance at redshift z
processed = ObservationalData()
# Stellar masses (for the given halo masses, at redshift z)
# Stellar masses (for the given halo masses, at redshift z)
M_star, M_84, M_16 = behroozi_2019_raw_with_uncertainties(
z, M_BN98,
"../raw/Behroozi_2019_fitting_params_smhm_true_med_cen.txt")
# Compute \Delta z
redshift_lower, redshift_upper = [z - dz_lower, z + dz_upper]
# Define scatter
y_scatter = unyt.unyt_array(
((M_star - M_16) / M_BN98, (M_84 - M_star) / M_BN98))
processed.associate_x(
M_star * unyt.Solar_Mass,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass",
)
processed.associate_y(
(M_star / M_BN98) * unyt.dimensionless,
scatter=y_scatter * unyt.dimensionless,
comoving=True,
description=
"Galaxy Stellar Mass / Halo Mass ($M_* / M_{\\rm BN98}$)",
)
processed.associate_redshift(z, redshift_lower, redshift_upper)
processed.associate_plot_as(plot_as)
multi_z.associate_dataset(processed)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(filename=output_path)