High-resolution, 3D radiative transfer modelling

Nersesian, Angelos; Verstocken, Sam; Viaene, S.; Baes, M.; Xilouris, E. M.; Bianchi, S.; Casasola, V.; Clark, Christopher; Davies, Jonathan Ivor; Looze, I. De; Vis, P. De; Dobbels, Wouter; Fritz, J.; Galametz, M.; Galliano, F.; Jones, A. P.; Madden, S.; Mosenkov, Aleksandr V.; Trčka, Ana; Ysard, N.

doi:10.1051/0004-6361/201936176

Cited by 32 publications

(32 citation statements)

References 131 publications

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“…Furthermore, we confirm a tight correlation between the dust heating fraction and the sSFR which was previously reported for RT models of M 51 and M 31 (Viaene et al 2017). The full description of our method and the results of the modelling of M 81 will be presented in Verstocken et al (2020, submitted), while the detailed results of this study will be presented in Nersesian et al (2020). Finally, the modelling of a galaxy with an additional AGN component, NGC 1068 (M 77) will be presented in Viaene et al (in preparation).…”

Section: Discussionsupporting

confidence: 85%

High-resolution radiation transfer modelling of barred galaxies

et al. 2019

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Dust radiative transfer simulations provide us with the unique opportunity to study the heating mechanisms of dust by the stellar radiation field. From 2D observational images we derive the 3D distributions of stars and dust. Our aim is to analyze the contribution of the different stellar populations to the radiative dust heating processes in the nearby face-on barred galaxies NGC 1365, M 83 and M 95. We wish to decompose the FIR-submm SED and quantify the fraction directly related to star formation. To model the complex geometries mentioned above, we used SKIRT, a state-of-the-art, 3D Monte Carlo radiative transfer code designed to simulate the absorption, scattering and thermal re-emission of dust in a variety of environments. We find that the contribution of the evolved stars (8 Gyr) to the dust heating is non-negligible (∼35%) and can reach as high as 70%. We also find a tight correlation between the heating fraction by the unevolved stars (⩽ 100 Myr) and the specific star formation rate.

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Section: Discussionsupporting

confidence: 85%

High-resolution radiation transfer modelling of barred galaxies

et al. 2019

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“…8 presents the dust temperature (T dust ), dust mass (M dust ), and dust density (ρ dust ) maps. The diffuse dust temperature of each dust cell in our simulations was approximated through the strength of the ISRF (U), following the method used in Nersesian et al (2020). Assuming that dust is heated by an ISRF with a Milky Way-like spectrum (Mathis et al 1983) the dust temperatures of the diffuse dust can be approximated as…”

Section: Maps Of Physical Propertiesmentioning

confidence: 99%

“…Inverse radiative transfer simulations of face-on galaxies have now reached the point where they can be computationally efficient, and where they can provide all the necessary tools for a quantitative study of the mechanisms that power the infrared emission by dust, such as the different stellar populations (Viaene et al 2017;Williams et al 2019;Thirlwall et al 2020;Verstocken et al 2020;Nersesian et al 2020) or other heating sources, such as AGN (Viaene et al 2020). In a first attempt to quantify the dust heating fraction related to the star formation rate (SFR) in face-on spiral galaxies, De Looze et al…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the fifth in the series, we revisit the radiative transfer model of M 51a (De Looze et al 2014), and update the methodology of constructing such a model according to the pipeline established in Verstocken et al (2020). Within the Dust-Pedia framework (Davies et al 2017) the method was optimised and applied to the early-spiral galaxy M 81 (Paper II, Verstocken et al 2020) to a sample of face-on barred galaxies (Paper III, Nersesian et al 2020) and to NGC 1068 which harbours an AGN (Paper IV, Viaene et al 2020). Two additional studies employed our modelling strategy to build the models of the large Local Group galaxies M 31 (Viaene et al 2017) and M 33 (Williams et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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High-resolution, 3D radiative transfer modelling

Nersesian

Viaene

Baes

et al. 2020

A&A

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Context. Investigating the dust heating mechanisms in galaxies provides a deeper understanding of how the internal energy balance drives their evolution. Over the last decade radiative transfer simulations based on the Monte Carlo method have emphasised the role of the various stellar populations heating the diffuse dust. Beyond the expected heating through ongoing star formation, older stellar populations (≥8 Gyr) and even active galactic nuclei can both contribute energy to the infrared emission of diffuse dust. Aims. In this particular study we examine how the radiation of an external heating source, such as the less massive galaxy NGC 5195 in the M 51 interacting system, could affect the heating of the diffuse dust of its parent galaxy NGC 5194, and vice versa. Our goal is to quantify the exchange of energy between the two galaxies by mapping the 3D distribution of their radiation field. Methods. We used SKIRT, a state-of-the-art 3D Monte Carlo radiative transfer code, to construct the 3D model of the radiation field of M 51, following the methodology defined in the DustPedia framework. In the interest of modelling, the assumed centre-to-centre distance separation between the two galaxies is ∼10 kpc. Results. Our model is able to reproduce the global spectral energy distribution of the system, and it matches the resolved optical and infrared images fairly well. In total, 40.7% of the intrinsic stellar radiation of the combined system is absorbed by dust. Furthermore, we quantify the contribution of the various dust heating sources in the system, and find that the young stellar population of NGC 5194 is the predominant dust-heating agent, with a global heating fraction of 71.2%. Another 23% is provided by the older stellar population of the same galaxy, while the remaining 5.8% has its origin in NGC 5195. Locally, we find that the regions of NGC 5194 closer to NGC 5195 are significantly affected by the radiation field of the latter, with the absorbed energy fraction rising up to 38%. The contribution of NGC 5195 remains under the percentage level in the outskirts of the disc of NGC 5194. This is the first time that the heating of the diffuse dust by a companion galaxy is quantified in a nearby interacting system.

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“…These studies find that about one third of starlight in spiral galaxies is reprocessed by dust (Viaene et al Interactive figures are available at https://www.aanda.org and at https://wdobbels.github.io/FIREnet/ 2016; Bianchi et al 2018). A UV-submm SED is also required to study the star-dust interaction with radiative transfer models (De Looze et al 2014;Viaene et al 2017;Nersesian et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting the global far-infrared SED of galaxies via machine learning techniques

Dobbels

Baes²,

Viaene

et al. 2020

A&A

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Context. Dust plays an important role in shaping a galaxy’s spectral energy distribution (SED). It absorbs ultraviolet (UV) to near-infrared radiation and re-emits this energy in the far-infrared (FIR). The FIR is essential to understand dust in galaxies. However, deep FIR observations require a space mission, none of which are still active today. Aims. We aim to infer the FIR emission across six Herschel bands, along with dust luminosity, mass, and effective temperature, based on the available UV to mid-infrared (MIR) observations. We also want to estimate the uncertainties of these predictions, compare our method to energy balance SED fitting, and determine possible limitations of the model. Methods. We propose a machine learning framework to predict the FIR fluxes from 14 UV–MIR broadband fluxes. We used a low redshift sample by combining DustPedia and H-ATLAS, and extracted Bayesian flux posteriors through SED fitting. We trained shallow neural networks to predict the far-infrared fluxes, uncertainties, and dust properties. We evaluated them on a test set using a root mean square error (RMSE) in log-space. Results. Our results (RMSE = 0.19 dex) significantly outperform UV–MIR energy balance SED fitting (RMSE = 0.38 dex), and are inherently unbiased. We can identify when the predictions are off, for example when the input has large uncertainties on WISE 22 μm, or when the input does not resemble the training set. Conclusions. The galaxies for which we have UV–FIR observations can be used as a blueprint for galaxies that lack FIR data. This results in a “virtual FIR telescope”, which can be applied to large optical-MIR galaxy samples. This helps bridge the gap until the next FIR mission.

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High-resolution, 3D radiative transfer modelling

Cited by 32 publications

References 131 publications

High-resolution radiation transfer modelling of barred galaxies

High-resolution radiation transfer modelling of barred galaxies

High-resolution, 3D radiative transfer modelling

Predicting the global far-infrared SED of galaxies via machine learning techniques

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