NSA SEDflow
Catalog¶
This probabilistic value-added catalog provides detailed physical properties
for 33,884 galaxies in the NASA-Sloan Atlas (http://www.nsatlas.org/).
The properties are inferred from optical photometry in the u, g, r, i, z
bands using SEDflow
, an accelerated Bayesian SED modeling method that
combines the Hahn et al. (2022a) PROVABGS
SED model with Amortized Neural Posterior
Estimation. For more details on this catalog and SEDflow
see Hahn & Melchior (2022).
For each galaxy, the catalog provides 10,000 samples drawn from the posteriors of
log_mstar :
log10 of stellar mass
log_sfr_1gyr :
log10 of average star formation rate over 1Gyr
log_Z_MW :
log10 of mass-weighted metallicity
beta1, beta2, beta3, beta4 :
coefficients of the non-negative matrix factorization (NMF)
star formation history basis functions
fburst :
fraction of stellar mass formed by star burst
tburst :
time of star burst event
log_gamma1, log_gamma2 :
log10 of coefficients of the NMF metallicity history basis
functions
tau_bc :
birth cloud optical depth
tau_ism :
diffuse dust optical depth
n_dust :
Calzetti (2001) dust index
For more details on the galaxy properties, see Hahn et al. (2022a).
A small fraction of NSA galaxies have photometry or uncertainties outside
of the SEDflow
training data.
For these galaxies, SEDflow
does not produce sensible posteriors.
We estimate their posteriors using the PROVABGS
SED model with MCMC
sampling in the same way as Hahn et al. (2022a).
We mark these galaxies using:
sedflow : boolean
True if posterior was estimated using SEDflow.
False if posterior was estimated using MCMC
The catalog also includes:
NSAID :
unique ID within the NSA catalog
mag_u, mag_g, mag_r, mag_i, mag_z :
u, g, r, i, z optical magnitudes derived from NSA catalog's
NMGY Galactic-extinction corrected AB photometric flux
sigma_u, sigma_g, sigma_r, sigma_i, sigma_z:
uncertainties of u, g, r, i, z optical photometry in
magnitude space
Download¶
Download the catalog nsa.sedflow.hdf5
at
The catalog is in hdf5 format and can be read in python using the h5py
package
import h5py
f = h5py.File('nsa.sedflow.hdf5', 'r')
# print data columns
print(f.keys())
# read stellar mass
logm = f['log_mstar'][...]
f.close()
Attribution¶
Please cite Hahn & Melchior (2022) if you use the SEDflow
NSA catalog in your research.