JINGLE V: Dust properties of nearby galaxies derived from hierarchical Bayesian SED fitting Academic Article uri icon

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abstract

  • Abstract We study the dust properties of 192 nearby galaxies from the JINGLE survey using photometric data in the 22-850$\mu$m range. We derive the total dust mass, temperature T and emissivity index β of the galaxies through the fitting of their spectral energy distribution (SED) using a single modified black-body model (SMBB). We apply a hierarchical Bayesian approach that reduces the known degeneracy between T and β. Applying the hierarchical approach, the strength of the T-β anti-correlation is reduced from a Pearson correlation coefficient R = −0.79 to R = −0.52. For the JINGLE galaxies we measure dust temperatures in the range 17 − 30 K and dust emissivity indices β in the range 0.6 − 2.2. We compare the SMBB model with the broken emissivity modified black-body (BMBB) and the two modified black-bodies (TMBB) models. The results derived with the SMBB and TMBB are in good agreement, thus applying the SMBB, which comes with fewer free parameters, does not penalize the measurement of the cold dust properties in the JINGLE sample. We investigate the relation between T and β and other global galaxy properties in the JINGLE and Herschel Reference Survey (HRS) sample. We find that β correlates with the stellar mass surface density (R = 0.62) and anti-correlates with the HI mass fraction (MHI/M*, R = −0.65), whereas the dust temperature correlates strongly with the SFR normalized by the dust mass (R = 0.73). These relations can be used to estimate T and β in galaxies with insufficient photometric data available to measure them directly through SED fitting.

authors

  • Lamperti, Isabella
  • Saintonge, Amélie
  • De Looze, Ilse
  • Accurso, Gioacchino
  • Clark, Christopher JR
  • Smith, Matthew WL
  • Wilson, Christine D
  • Brinks, Elias
  • Brown, Toby
  • Bureau, Martin
  • Clements, David L
  • Eales, Stephen
  • Glass, David HW
  • Hwang, Ho Seong
  • Lee, Jong Chul
  • Lin, Lihwai
  • Michalowski, Michal J
  • Sargent, Mark
  • Williams, Thomas G
  • Xiao, Ting
  • Yang, Chentao

publication date

  • November 1, 2019