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)
Z_asm = 6.29
alpha = 0.21
else:
Z_asm = 4.84
alpha = 0.35
log10_M_star_min = 9.5
log10_M_star_max = 11.5
M_star = np.arange(log10_M_star_min, log10_M_star_max, 0.2)
Z_gas = (Z_asm + alpha * M_star) * unyt.dimensionless # 12 + log(O/H)
M_star = 10**M_star * unyt.Solar_Mass
processed.associate_x(M_star,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Z_gas,
scatter=None,
comoving=True,
description="Gas phase metallicity")
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)
if not os.path.exists(output_directory):
os.mkdir(output_directory)
comment = (
"Assuming Chabrier IMF and Vmax selection, quoted redshift is lower bound of range. "
f"h-corrected for SWIFT using Cosmology: {cosmology.name}.")
citation = "Ilbert et al. (2013)"
bibcode = "2013A&A...556A..55I"
name = "GSMF from UltraVISTA"
multi_z = MultiRedshiftObservationalData()
multi_z.associate_comment(comment)
multi_z.associate_name(name)
multi_z.associate_citation(citation, bibcode)
multi_z.associate_cosmology(cosmology)
# z_bins is a 1-D ndarray containing the lower edges of the redshift bins
# gsmf_and_Mstar is a list of 2D ndarrays, one per redshift
# Each contains five columns as follows:
# log(Mstar) bins, Mstar errors, log(GSMF), GSMF +- errors
z_bins, gsmf_and_Mstar = load_file_and_split_by_z(input_filename)
for z, gsmf_and_Mstar_at_z in zip(z_bins, gsmf_and_Mstar):
multi_z.associate_dataset(process_for_redshift(z, gsmf_and_Mstar_at_z))
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)
processed_sf.associate_redshift(0.5 * sum(redshift_bin), *redshift_bin)
processed_sf.associate_plot_as(plot_as)
processed_nsf.associate_x(centers_nsf,
scatter=None,
comoving=False,
description="Galaxy Stellar Mass")
processed_nsf.associate_y(
medians_nsf,
scatter=deviations_nsf,
comoving=False,
description="Galaxy Half-Mass Radius",
)
processed_nsf.associate_redshift(0.5 * sum(redshift_bin), *redshift_bin)
processed_nsf.associate_plot_as(plot_as)
multi_z_sf.associate_dataset(processed_sf)
multi_z_nsf.associate_dataset(processed_nsf)
output_path_sf = f"{output_directory}/Mosleh2020_SF.hdf5"
output_path_nsf = f"{output_directory}/Mosleh2020_Q.hdf5"
if os.path.exists(output_path_sf):
os.remove(output_path_sf)
if os.path.exists(output_path_nsf):
os.remove(output_path_nsf)
multi_z_sf.write(filename=output_path_sf)
multi_z_nsf.write(filename=output_path_nsf)
f"Assuming Chabrier IMF, quoted redshift is average in range. "
"h-corrected for SWIFT using Cosmology: {cosmology.name}."
)
citation = "Tomczak et al (2013)"
bibcode = "2014ApJ...783...85T"
name = "GSMF from ZFOURGE/CANDELS"
multi_z = MultiRedshiftObservationalData()
multi_z.associate_comment(comment)
multi_z.associate_name(name)
multi_z.associate_citation(citation, bibcode)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(2)
# z_bins, mstar_bins are 1-D ndarrays containing the redshift ranges and the log(stellar mass) bins respectively
# gsmf is a 3-D ndarray with axes 0 and 1 corresponding to redshift ranges and stellar mass bins
# Axis 2 ranges from 0..2 and contains log(GSMF), and the +- errors respectively
z_bins, mstar_bins, gsmf = load_file_and_split_by_z(input_filename)
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}"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(output_path)
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)