THE PROPAGATION OF UNCERTAINTIES IN STELLAR POPULATION SYNTHESIS MODELING. III. MODEL CALIBRATION, COMPARISON, AND EVALUATION

By C. Conroy, James E. Gunn. 2009

551 citations recorded by the source corpus.

Stellar population synthesis (SPS) provides the link between the stellar and dust content of galaxies and their observed spectral energy distributions. In the present work, we perform a comprehensive calibration of our own flexible SPS (FSPS) model against a suite of data. These data include ultraviolet, optical, and near-IR photometry, surface brightness fluctuations, and integrated spectra of star clusters in the Magellanic Clouds (MCs), M87, M31, and the Milky Way (MW), and photometry and spectral indices of both quiescent and post-starburst galaxies at z ∼ 0. Several public SPS models are intercompared, including the models of Bruzual & Charlot (BC03), Maraston (M05), and FSPS. The relative strengths and weaknesses of these models are evaluated, with the following conclusions: (1) the FSPS and BC03 models compare favorably with MC data at all ages, whereas M05 colors are too red and the age dependence is incorrect; (2) all models yield similar optical and near-IR colors for old metal-poor systems, and yet they all provide poor fits to the integrated J − K and V − K colors of both MW and M31 star clusters; (3) FSPS is able to fit all of the ultraviolet data because both the post-asymptotic giant branch (post-AGB) and horizontal branch evolutionary phases are handled flexibly, while the BC03 and M05 models fail in the far-UV, and both far- and near-UV, respectively; (4) all models predict ugr colors too red, Dn4000 strengths too strong, and HδA strengths too weak compared to massive red sequence galaxies, under the assumption that such galaxies are composed solely of old metal-rich stars; and (5) FSPS and, to a lesser extent, BC03 can reproduce the optical and near-IR colors of post-starburst galaxies, while M05 cannot. Reasons for these discrepancies are explored. The failure at predicting the ugr colors, Dn4000, and HδA strengths can be explained by some combination of a minority population of metal-poor stars, young stars, blue straggler and/or blue horizontal branch (HB) stars, but not by appealing to inadequacies in either theoretical stellar atmospheres or canonical evolutionary phases (e.g., the main-sequence turnoff). The different model predictions in the near-IR for intermediate age systems are due to different treatments of the thermally pulsating asymptotic giant branch stellar evolutionary phase. We emphasize that due to a lack of calibrating star cluster data in regions of the metallicity–age plane relevant for galaxies, all of these models continue to suffer from serious uncertainties that are difficult to quantify.

Subfields: Astronomy and Astrophysics, Instrumentation, Statistical and Nonlinear Physics

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Citations in the corpus

EMERGE – an empirical model for the formation of galaxies since z ∼ 10 (2017, methodology): “Using the FSPS package (Conroy & Gunn 2010; Conroy, White & Gunn 2010) to calculate the rate of stellar mass loss for a Chabrier (2003) IMF, the fraction of lost stars is well-fit by floss(t) = 0.05 ln ( 1 + t 1.4 Myr ) .”
GALEX–SDSS–WISE LEGACY CATALOG (GSWLC): STAR FORMATION RATES, STELLAR MASSES, AND DUST ATTENUATIONS OF 700,000 LOW-REDSHIFT GALAXIES (2016, methodology): “…information from photometry (and/or spectra), it will be limited by the uncertainties in stellar and dust attenuation models (Conroy et al. 2009; Conroy & Gunn 2010; Conroy et al. 2010b; Mitchell et al. 2013), and the choice of priors (e.g., appropriate SF histories and dust attenuation laws).”
CLASH: THREE STRONGLY LENSED IMAGES OF A CANDIDATE z ≈ 11 GALAXY (2012, methodology): “To explore an even broader range of galaxy SED models than used in §4, we utilized the flexible stellar population synthesis (FSPS) models from Conroy et al. (2009) and Conroy & Gunn (2010).”
Modelling and interpreting spectral energy distributions of galaxies with BEAGLE (2016, background): “This second limitation has been addressed in several studies already (e.g. Charlot, Worthey & Bressan 1996; Cerviño, Luridiana & Castander 2000; Conroy, Gunn & White 2009; Percival & Salaris 2009; Conroy & Gunn 2010; Conroy, White & Gunn 2010a).”
COLOR–MASS-TO-LIGHT-RATIO RELATIONS FOR DISK GALAXIES (2014, background): “In the mean time, considerable caution is warranted in assigning stellar masses based on SED fits (Conroy & Gunn 2010).”
UV-extended E-MILES stellar population models: young components in massive early-type galaxies (2016, background): “There are models based on theoretical stellar libraries that provide us with low resolution SEDs redward the optical range (e.g., Bruzual & Charlot 2003; Maraston 2005; Conroy & Gunn 2010).”
THE ACS NEARBY GALAXY SURVEY TREASURY. IX. CONSTRAINING ASYMPTOTIC GIANT BRANCH EVOLUTION WITH OLD METAL-POOR GALAXIES (2010, background): “…properties, like their colors and surface brightness fluctuations (see e.g. Maraston 2005; Pessev et al. 2008; Raimondo 2009; Conroy et al. 2009; Conroy & Gunn 2010), which by their very nature cannot disentangle different evolutionary properties like stellar luminosities, lifetimes, and…”
Fitting the integrated spectral energy distributions of galaxies (2010, background): “The reader is also referred to the series of papers Conroy et al. (2009); Conroy and Gunn (2010) for a recent systematic study of some of the uncertainties affecting SSP models.”
STELLAR MASSES FROM THE CANDELS SURVEY: THE GOODS-SOUTH AND UDS FIELDS (2014, methodology): “…masses are commonly estimated by fitting the observed multiwavelength photometry with stellar population synthesis templates (e.g., Fioc & Rocca-Volmerange 1997; Bruzual & Charlot 2003, BC03 hereafter; Maraston 2005; Bruzual 2007, CB07 hereafter; Conroy & Gunn 2010; see Conroy 2013 for a review).”
A CATALOG OF BULGE, DISK, AND TOTAL STELLAR MASS ESTIMATES FOR THE SLOAN DIGITAL SKY SURVEY (2013, methodology): “As mentioned previously, FSPS already incorporates an empirical calibration of Δ log L and Δ log T relative to the fiducial tp-AGB evolutionary tracks of Marigo & Girardi (2007), described by Equations (2) and (3) of Conroy & Gunn (2010).”
THE AVERAGE STAR FORMATION HISTORIES OF GALAXIES IN DARK MATTER HALOS FROM z = 0–8 (2012, methodology): “Star Formation Histories Under our assumption of a Chabrier (2003) IMF and Bruzual & Charlot (2003) stellar evolution tracks, we use th FSPS package (Conroy et al. 2010; Conroy & Gunn 2010) to calculate the rate of stellar mass loss.”
A HIGHLY CONSISTENT FRAMEWORK FOR THE EVOLUTION OF THE STAR-FORMING “MAIN SEQUENCE” FROM z ∼ 0–6 (2014, background): “…population synthesis (SPS) modeling (Fioc & Rocca-Volmerange 1997; Bruzual & Charlot 2003; Maraston 2005; Percival et al. 2009; Conroy et al. 2009; Conroy & Gunn 2010) and improved global diagnostics of galactic star formation (Murphy et al. 2011; Hao et al. 2011; Kennicutt & Evans 2012 (KE12),…”
Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe (2016, background): “For example, the contribution of thermally pulsing asymptotic giant branch stars (TP-AGB) may or may not dominate the near-IR emission of galaxies of intermediate age (Maraston 2005; Marigo et al. 2008; Conroy & Gunn 2010).”
The star formation activity of IllustrisTNG galaxies: main sequence, UVJ diagram, quenched fractions, and systematics (2018, methodology): “…of simulated galaxies is determined following Nelson et al. (2018a) i.e. adopting the FSPS stellar population synthesis code (Conroy et al. 2009; Conroy & Gunn 2010; ForemanMackey et al. 2014) with the Padova isochrones, MILES stellar library, and assuming a Chabrier initial mass function…”
How sensitive are predicted galaxy luminosities to the choice of stellar population synthesis model (2013, methodology): “Over the last decade SPS models have incorporated improved descriptions of stars in a TP-AGB phase (Bruzual & Charlot 2003; Maraston 2005; Conroy & Gunn 2010), and also of horizontal branch stars which are particularly relevant for the ultra-violet (UV) emission from old stellar populations (Conroy…”
MODELING THE METALLICITY DISTRIBUTION OF GLOBULAR CLUSTERS (2010, background): “It should be noted that a known discrepancy exists between the B − V colors predicted by all major population synthesis codes and those of observed globular clusters (Conroy & Gunn 2010).”
CIGALE: a python Code Investigating GALaxy Emission (2018, methodology): “Such a method has been adopted by modern SED modelling codes such as CIGALE (Burgarella et al. 2005; Noll et al. 2009), MAGPHYS (da Cunha et al. 2008), and FSPS (Conroy et al. 2009; Conroy & Gunn 2010) for instance.”
The Data Release of the Sloan Digital Sky Survey-II Supernova Survey (2014, methodology): “The former method utilizes SED models from the code Flexible Stellar Population Synthesis (FSPS; Conroy et al. 2009; Conroy & Gunn 2010) while the latter utilizes SED models from the code PÉGASE.2 (Fioc & Rocca-Volmerange 1997; Le Borgne et al. 2004).”
Stellar population models at high spectral resolution (2011, methodology): “Curiously, Conroy & Gunn (2010) came to the opposite conclusion.”
THE DUST ATTENUATION LAW IN DISTANT GALAXIES: EVIDENCE FOR VARIATION WITH SPECTRAL TYPE (2013, methodology): “In this Letter we use well-sampled composite SEDs constructed from the NMBS photometry (Kriek et al. 2011), in com- bination with the flexible stellar population synthesis (FSFS; Conroy et al. 2009, 2010a; Conroy & Gunn 2010) models, to constrain the dust attenuation curve in 0.5 < z < 2.0 galaxies.”