Fitting the spectral energy distributions of galaxies
Inversion Codes, Optical
The Spectral Synthesis non-parametric
fitting code by the SEAGal Group
Inversion with regularization
Ocvirk et al.
University of Lyon Spectral analysis
Software, Koleva et al.
VErsatile SPectral Analysis code by
Edinburgh group, Tojeiro et al.
(SDSS results available via SQL)
Penalized Pixel Fitting by
Capellari et Emsellem
Stellar Pops and emission lines
Sanchez
Fitting IteRativEly For Likelihood
analYsis
Fitting Analysis using Differential
evolution Optimization
Principled inference of stellar
population properties (spectra, photo)
Bayesian inference of
physical properties
Simulating optical Spectra with
stellar population models
by Fritz et al.2007
Galaxy-Observed-Simulated SED
Interactive Program by PANDORA
Group in INAF IASF-Milano
By Jesus Maiz-Apellaniz
A Markov Chain Monte Carlo Algorithm
for SED fitting by Acquaviva et al.
Code Investigating GALaxy
Emission by OAMP/LAM group
SEd Analysis using BAyesian
Statistics
Parameter estimation and model
comparison
Inferring metallicities and ionization
parameters
Bayesian Spectral Energy
Distribution Modeling of Galaxies
BayEsian Analysis of GaLaxy sEds
Fitting and Assessment of Synthetic
Templates
Bagpipes
Bayesian Analysis of Galaxies for
Inference
Photometric Redshifts
Public SED fitting codes
Photo-Z Accuracy Testing
TRAINING SET BASED
Photometric redshifts using Artificial
Neural Networks, Collister & Lahav
Random Forest Photo-z
Carliles et al.
Neural Network for photo-Z
Singal et al.
Neural network by Cavuoti et al.
machine learning methods, all
calculations are performed on server
TEMPLATE BASED
Uses Low-Resolution Templates of
Assef et al.
Bayesian Photometric Redshift
estimator by Benitez & Coe
”Easy and Accurate Z-phot from Yale
group”
Evolution of Radio Galaxies
Age and redshift calculation
Photometric redshift code by
Photo-Z code by Blanton et al.
PHotometric Analysis for Redshift
Estimations by Arnouts & Ilbert
The Zurich Extragalactic Bayesian
Redshift Analyzer using adaptive
templates (ETH)
Based on the stellar population synthesis
code PEGASE
Inversion Codes, Dust
Virtual observatory tools
Emission Lines
Bayesian approach to obtain metallicity,
nitrogen to oxygen ratio and ionisation
parameter
Fitting Ly alpha line profiles using
deep learning
