Towards universal hybrid star formation rate estimators

Abstract

Context. To compute the star formation rate (SFR) of galaxies from the rest-frame ultraviolet (UV), it is essential to take the obscuration by dust into account. To do so, one of the most popular methods consists in combining the UV with the emission from the dust itself in the infrared (IR). Yet, different studies have derived different estimators, showing that no such hybrid estimator is truly universal. Aims. In this paper we aim at understanding and quantifying what physical processes fundamentally drive the variations between different hybrid estimators. In so doing, we aim at deriving new universal UV+IR hybrid estimators to correct the UV for dust attenuation at local and global scales, taking the intrinsic physical properties of galaxies into account. Methods. We use the CIGALE code to model the spatially resolved far-UV to far-IR spectral energy distributions of eight nearby star-forming galaxies drawn from the KINGFISH sample. This allows us to determine their local physical properties, and in particular their UV attenuation, average SFR, average specific SFR (sSFR), and their stellar mass. We then examine how hybrid estimators depend on said properties. Results. We find that hybrid UV+IR estimators strongly depend on the stellar mass surface density (in particular at 70 µm and 100 µm) and on the sSFR (in particular at 24 µm and the total infrared). Consequently, the IR scaling coefficients for UV obscuration can vary by almost an order of magnitude: from 1.55 to 13.45 at 24 µm for instance. This result contrasts with other groups who found relatively constant coefficients with small deviations. We exploit these variations to construct a new class of adaptative hybrid estimators based on observed UV to near-IR colours and near-IR luminosity densities per unit area. We find that they can reliably be extended to entire galaxies. Conclusions. The new estimators provide better estimates of attenuation-corrected UV emission than classical hybrid estimators published in the literature. Taking naturally variable impact of dust heated by old stellar populations into account, they constitute an important step towards universal estimators.

Cloud-scale ISM Structure and Star Formation in M51

“…That is, the approach that we use at θ = 30 ′′ agrees well with that which we are forced to use θ = 10 ′′ . The median ratio agrees by construction, but the small scatter gives us confidence in our use of 70 µm emission and our application of Equation 1 (though see Boquien et al 2016, for a more in depth consideration of IR emission as a function of scale).…”

Galex–sdss–wise Legacy Catalog (Gswlc): Star Formation Rates, Stellar Masses, and Dust Attenuations of 700,000 Low-Redshift Galaxies

“…For such galaxies the IR emission, if detected, will be dominated by dust heating by relatively dusty old or intermediate-age (e.g., AGB) stellar populations (Bressan et al 2001;Villaume et al 2015) and will therefore not be indicative of the current SF (Cortese et al 2008;Salim et al 2009). When the IR luminosity of such galaxies is converted into SFRs using simple, fixed-coefficient formulae (Equation (8)), which assume that dust is heated by young stars, the SFR will be overestimated (Buat & Xu 1996;Kennicutt 1998;Boquien et al 2016). For AGN galaxies with higher sSFRs, the mid-IR sSFRs tend to be up to 0.6 dex higher than the SED sSFRs, suggesting a non-negligible dust heating by AGNs, which produces excess emission in the mid-IR.…”

“…Many of these improvements were made possible by the use of the Code Investigating GALaxy Emission 10 (CIGALE; Noll et al 2009, M. Boquien et al 2016 to calculate the models and perform the SED fitting. GSWLC contains SDSS galaxies within the GALEX footprint (regardless of the UV detection), covering 90% of the SDSS area.…”

Quenching of the star formation activity in cluster galaxies

“…The code identifies the best-fit model through a χ 2 minimisation and makes a probability distribution function (PDF) analysis to identify the likelihood-weighted mean value and standard deviation for different physical parameters such as the stellar mass and the star formation rate. This code has been successfully used to reconstruct the star formation history of high-z and local galaxies Boquien et al 2016;Lo Faro et al, in prep. ), including those of the HRS (Boquien et al 2012(Boquien et al , 2013Ciesla et al 2014Ciesla et al , 2016.…”