Deriving physical parameters of unresolved star clusters

Abstract

Context. This paper is the second of a series that investigates the stochasticity and degeneracy problems that hinder the derivation of the age, mass, extinction, and metallicity of unresolved star clusters in external galaxies when broad-band photometry is used. Aims. While Paper I concentrated on deriving age, mass, and extinction of star clusters for one fixed metallicity, we here derive these parameters in case when metallicity is let free to vary. The results were obtained using several different filter systems (UBVRI, UBVRIJHK, GALEX+UBVRI), which allowed us to optimally reduce the different degeneracies between the cluster physical parameters. Methods. The age, mass, and extinction of a sample of artificial star clusters were derived by comparing their broad-band integrated magnitudes with the magnitudes of a large grid of cluster models with various metallicities. Results. A large collection of artificial clusters was studied to model the different degeneracies in the age, mass, extinction, and metallicity parameter space when stochasticity is taken into account in the cluster models. We show that, without prior knowledge on the metallicity, the optical bands (UBVRI) fail to allow a correct derivation of the age, mass, and extinction because of the strong degeneracies between models of different metallicities. Adding near-infrared information (UBVRI+JHK) slightly helps in improving the parameter derivation, except for the metallicity. Adding ultraviolet data (GALEX+UBVRI) helps significantly in deriving these parameters and allows constraining the metallicity when the photometric errors have a Gaussian distribution with standard deviations 0.05 mag for UBVRI and 0.15 mag for the GALEX bands.

SLUG – stochastically lighting up galaxies – III. A suite of tools for simulated photometry, spectroscopy, and Bayesian inference with stochastic stellar populations

“…Simulating stochastic stellar populations is useful, but to fully exploit this capability we must tackle the inverse problem: given an observed set of stellar photometry, what should we infer about the physical properties of the underlying stellar population in the regime where the mapping between physical and photometric properties is nondeterministic? A number of authors have presented numerical methods to tackle problems of this sort, mostly in the context of determining the properties of star clusters with stochastically-sampled IMFs (Popescu & Hanson 2009, 2010bFouesneau & Lançon 2010;Fouesneau et al 2012;Popescu et al 2012;Asa'd & Hanson 2012;Anders et al 2013;de Meulenaer et al 2013de Meulenaer et al , 2014de Meulenaer et al , 2015, and in some cases in the context of determining SFRs from photometry (da Silva et al 2014). Our method here is a generalization of that developed in da Silva et al (2014), and has a number of advantages as compared to earlier methods, both in terms of its computational practicality and its generality.…”

“…Significantly less work has been done in extending SPS methods to the regime where the IMF and SFH are not well-sampled. There are a number of codes available for simulating a simple stellar population (i.e., one where all the stars are the same age, so the SFH is described by a δ distribution) where the IMF is not well sampled (Maíz Apellániz 2009;Popescu & Hanson 2009, 2010bFouesneau & Lançon 2010;Fouesneau et al 2012Fouesneau et al , 2014Anders et al 2013;de Meulenaer et al 2013de Meulenaer et al , 2014de Meulenaer et al , 2015, and a great deal of analytic work has also been performed on this topic (Cerviño & Valls-Gabaud 2003;Cerviño & Luridiana 2004) -see Cerviño (2013) for a recent review. However, these codes only address the problem of stochastic sampling of the IMF; for non-simple stellar populations, stochastic sampling of the SFH proves to be a more important effect (Fumagalli et al 2011;da Silva et al 2014).…”

Deriving physical parameters of unresolved star clusters

“…New models (Fouesneau & Lançon 2010;Popescu & Hanson 2010;da Silva et al 2012;de Meulenaer et al 2013de Meulenaer et al , 2014de Meulenaer et al , 2015, hereafter Papers I-III) take into account this natural dispersion of integrated colors due to the star mass stochastic sampling and allow the cluster parameters to be derived in a more realistic way, avoiding the strong biases of the SSP method.…”

Deriving star cluster parameters with convolutional neural networks

“…Clusters older than log 10 (t/yr) = 8 with high extinction can be located in the same color-color area as clusters with low extinction. These effects have also been observed when using analytically integrated stellar luminosities (Bridžius et al 2008) and remain when stochastic effects of IMF sampling are included (de Meulenaer et al 2014). Fig.…”