Modelling and interpreting spectral energy distributions of galaxies with BEAGLE

By J. Chevallard, S. Charlot. 2016

249 citations recorded by the source corpus.

We present a new-generation tool to model and interpret spectral energy distributions (SEDs) of galaxies, which incorporates in a consistent way the production of radiation and its transfer through the interstellar and intergalactic media. This flexible tool, named BEAGLE (for BayEsian Analysis of GaLaxy sEds), allows one to build mock galaxy catalogues as well as to interpret any combination of photometric and spectroscopic galaxy observations in terms of physical parameters. The current version of the tool includes versatile modeling of the emission from stars and photoionized gas, attenuation by dust and accounting for different instrumental effects, such as spectroscopic flux calibration and line spread function. We show a first application of the BEAGLE tool to the interpretation of broadband SEDs of a published sample of ${\sim}10^4$ galaxies at redshifts $0.1 \lesssim z\lesssim8$. We find that the constraints derived on photometric redshifts using this multi-purpose tool are comparable to those obtained using public, dedicated photometric-redshift codes and quantify this result in a rigorous statistical way. We also show how the post-processing of BEAGLE output data with the Python extension PYP-BEAGLE allows the characterization of systematic deviations between models and observations, in particular through posterior predictive checks. The modular design of the BEAGLE tool allows easy extensions to incorporate, for example, the absorption by neutral galactic and circumgalactic gas, and the emission from an active galactic nucleus, dust and shock-ionized gas. Information about public releases of the BEAGLE tool will be maintained on this http URL

Subfields: Astronomy and Astrophysics, Ecology, Instrumentation

View Paper: https://www.semanticscholar.org/paper/d568d7272a28f7ca6680de4b968b0de085b58273

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Citations in the corpus

Lyα and C iii] emission in z = 7–9 Galaxies: accelerated reionization around luminous star-forming systems? (2016, methodology): “We parametrize the star formation histories of model galaxies in BEAGLE as exponentially delayed functions ψ(t) ∝ t exp(−t/τSFR), for star formation timescale in the range 7 ≤ log(τSFR/yr) ≤ 10.5 and formation redshift in the range zobs ≤ zform ≤ 50 (where zobs is the observed galaxy redshift).”
Stellar Population Inference with Prospector (2020, methodology): “This approach was pioneered by GalMC (Acquaviva et al. 2011), followed later by codes such as BEAGLE (Chevallard & Charlot 2016) and BAGPIPES (Carnall et al. 2018).”
An Older, More Quiescent Universe from Panchromatic SED Fitting of the 3D-HST Survey (2018, methodology): “Classic grid-based models grow exponentially in size with model dimensionality, but gridless “on-the-fly” models combined with Markov chain Monte Carlo (MCMC) algorithms can efficiently explore highdimensional (N7) spaces (Chevallard & Charlot 2016; Leja et al. 2017; Carnall et al. 2018).”
De re metallica: the cosmic chemical evolution of galaxies (2018, background): “…developed to infer the metallicity from the UV lines (Fosbury et al., 2003; Jaskot & Ravindranath, 2016; Gutkin et al., 2016; Feltre et al., 2016; Chevallard & Charlot, 2016; Pérez-Montero & Amorı́n, 2017; Byler et al., 2018; Nakajima et al., 2018), and some works also measure metallicities…”
The many flavours of photometric redshifts (2018, background): “One of the most recent public code is BEAGLE [29], which is among the few that adopt a fully Bayesian approach and thus a prior can be assigned to each parameter.”
Modelling the nebular emission from primeval to present-day star-forming galaxies (2016, methodology): “We refer the reader to the ‘quasi-universal’ prescription by Chevallard et al. (2013, see also section 2.5 of Chevallard & Charlot 2016) to express this quantity as a function of stellar age t′ and galaxy inclination θ, while accounting for 1 Available from http://www.astro.physik.uni-potsdam.de/…”
Inferring the star formation histories of massive quiescent galaxies with bagpipes: evidence for multiple quenching mechanisms (2017, methodology): “A new generation of modern spectral modelling and fitting tools has been developed in order to exploit this e.g. Beagle (Chevallard & Charlot 2016) and Prospector (Leja et al. 2017; Johnson MNRAS 000, 1–23 (2017) et al. in prep).”
Big Three Dragons: A z = 7.15 Lyman-break galaxy detected in [O iii] 88 μm, [C ii] 158 μm, and dust continuum with ALMA (2018, methodology): “Harikane et al. (2018) have interpreted the trend with photoionization models of CLOUDY (Ferland et al. 2013) implemented in spectral energy distribution (SED) models of BEAGLE (Chevallard & Charlot 2016).”
Spectroscopic confirmation of four metal-poor galaxies at z = 10.3–13.2 (2022, methodology): “We can gain further insight into the physical properties of these galaxies through spectral fitting with the BEAGLE [BayEsian Analysis of GaLaxy sEds 37 ] tool with setup described in Extended Data Table 2, adopting a constant star formation history (SFH) to probe the young stellar populations…”