CIGALEMC: GALAXY PARAMETER ESTIMATION USING A MARKOV CHAIN MONTE CARLO APPROACH WITH CIGALE
- Astrophysics Branch, NASA/Ames Research Center, MS 245-6, Moffett Field, CA 94035 (United States)
- Observatoire Astronomique de Marseille-Provence, 38 rue Frederic Joliot-Curie, 13388 Marseille Cedex 13 (France)
- Institut fuer Astro- und Teilchenphysik, Universitaet Innsbruck, Technikerstr.25/8, 6020 Innsbruck (Austria)
We introduce a fast Markov Chain Monte Carlo (MCMC) exploration of the astrophysical parameter space using a modified version of the publicly available code Code Investigating GALaxy Emission (CIGALE). The original CIGALE builds a grid of theoretical spectral energy distribution (SED) models and fits to photometric fluxes from ultraviolet to infrared to put constraints on parameters related to both formation and evolution of galaxies. Such a grid-based method can lead to a long and challenging parameter extraction since the computation time increases exponentially with the number of parameters considered and results can be dependent on the density of sampling points, which must be chosen in advance for each parameter. MCMC methods, on the other hand, scale approximately linearly with the number of parameters, allowing a faster and more accurate exploration of the parameter space by using a smaller number of efficiently chosen samples. We test our MCMC version of the code CIGALE (called CIGALEMC) with simulated data. After checking the ability of the code to retrieve the input parameters used to build the mock sample, we fit theoretical SEDs to real data from the well-known and -studied Spitzer Infrared Nearby Galaxy Survey sample. We discuss constraints on the parameters and show the advantages of our MCMC sampling method in terms of accuracy of the results and optimization of CPU time.
- OSTI ID:
- 21587427
- Journal Information:
- Astrophysical Journal, Vol. 740, Issue 1; Other Information: DOI: 10.1088/0004-637X/740/1/22; Country of input: International Atomic Energy Agency (IAEA); ISSN 0004-637X
- Country of Publication:
- United States
- Language:
- English
Similar Records
DIGS: deep inference of galaxy spectra with neural posterior estimation
A critical assessment of stellar mass measurement methods