Stellar populations of galaxies in the ALHAMBRA survey up toz ~ 1

Abstract

Aims. We present MUFFIT, a new generic code optimized to retrieve the main stellar population parameters of galaxies in photometric multi-filter surveys, and check its reliability and feasibility with real galaxy data from the ALHAMBRA survey. Methods. Making use of an error-weighted χ 2 -test, we compare the multi-filter fluxes of galaxies with the synthetic photometry of mixtures of two single stellar populations at different redshifts and extinctions, to provide the most likely range of stellar population parameters (mainly ages and metallicities), extinctions, redshifts, and stellar masses. To improve the diagnostic reliability, MUFFIT identifies and removes from the analysis those bands that are significantly affected by emission lines. The final parameters and their uncertainties are derived by a Monte Carlo method, using the individual photometric uncertainties in each band. Finally, we discuss the accuracies, degeneracies, and reliability of MUFFIT using both simulated and real galaxies from ALHAMBRA, comparing with results from the literature. Results. MUFFIT is a precise and reliable code to derive stellar population parameters of galaxies in ALHAMBRA. Using the results from photometric-redshift codes as input, MUFFIT improves the photometric-redshift accuracy by ∼10-20%. MUFFIT also detects nebular emissions in galaxies, providing physical information about their strengths. The stellar masses derived from MUFFIT show excellent agreement with the COSMOS and SDSS values. In addition, the retrieved age-metallicity locus for a sample of z ≤ 0.22 early-type galaxies in ALHAMBRA at different stellar mass bins are in very good agreement with the ones from SDSS spectroscopic diagnostics. Moreover, a one-to-one comparison between the redshifts, ages, metallicities, and stellar masses derived spectroscopically for SDSS and by MUFFIT for ALHAMBRA reveals good qualitative agreements in all the parameters, hence reinforcing the strengths of multi-filter galaxy data and optimized analysis techniques, like MUFFIT, to conduct reliable stellar population studies.

The ALHAMBRA survey:B-band luminosity function of quiescent and star-forming galaxies at 0.2 ≤ z < 1 by PDF analysis

“…This problem, known as the colour-redshift degeneracy, makes PDFs the only robust way to track the uncertainties in the observed photometry to the physical properties of interest. In this context, the ALHAMBRA photometric redshift PDFs have been successfully used to study high-redshift (z > 2) galaxies (Viironen et al 2015), to detect galaxy groups and clusters (Ascaso et al 2015), to estimate the merger fraction (López-Sanjuan et al 2015b), or to improve the estimation of stellar population parameters (Díaz-García et al 2015).…”

“…Because of this, dust reddened star-forming galaxies could be described by the E/S0 templates of BPZ2, and the red population would comprise therefore quiescent and dusty star-forming galaxies. We resolved this limitation thanks to the MUlti-Filter FITing code MUFFIT (Díaz-García et al 2015). The MUFFIT code is specifically performed and optimised to deal with multiphotometric data, such as the ALHAMBRA dataset, through the SED-fitting (based in a χ 2 -test weighted by errors) to mixtures of two single stellar populations (a dominant old component plus a posterior star formation episode, which can be related with a burst or a younger/extended tail in the star formation history).…”

The ALHAMBRA survey: tight dependence of the optical mass-to-light ratio on galaxy colour up to z = 1.5

“…These parameters were estimated assuming Bruzual & Charlot (2003, BC03) stellar population models, Fitzpatrick (1999) extinction law, and Chabrier (2003) initial mass function (IMF). We refer the reader to Díaz-García et al (2015), Díaz-García et al (2018) and DG17 for further details about MUFFIT and derived quantities.…”

A Comprehensive Bayesian Discrimination of the Simple Stellar Population Model, Star Formation History, and Dust Attenuation Law in the Spectral Energy Distribution Modeling of Galaxies

“…Recently, some authors have suggested some more complicated parameterizations of SFH (Gladders et al 2013;Abramson et al 2016;Diemer et al 2017;Ciesla et al 2017;Carnall et al 2018), and physically motivated prescriptions of SFHs drawn from either the hydrodynamic or the semi-analytic models of galaxy formation (Finlator et al 2007;Pacifici et al 2012;Iyer & Gawiser 2017). Besides, it is also possible to directly employ the non-parametric form of SFH, an approach that has been employed by many works (Heavens et al 2000;Cid Fernandes et al 2005;Ocvirk et al 2006;Tojeiro et al 2007;Koleva et al 2009;Díaz-García et al 2015;Magris C. et al 2015;Leja et al 2017;Zhang et al 2017). However, the aim of this paper is to evaluate the effectiveness of the Bayesian model comparison method and build a reference for future study, it is better to start with much simpler forms of SFH.…”