Dust SEDs in the era of Herschel and Planck: a Hierarchical Bayesian fitting technique

Details Dust SEDs in the era of Herschel and Planck: a Hierarchical Bayesian fitting technique Dust SEDs in the era of Herschel and Planck: a Hierarchical Bayesian fitting technique

2012-02-29

arxiv.org/abs/1203.0025v2

Astronomy and Astrophysics

Astrophysics

Instrumentation and Methods for Astrophysics

20 pages, 9 figures, ApJ format, revised version matches ApJ-accepted version

Scientific paper

We present a hierarchical Bayesian method for fitting infrared spectral energy distributions (SEDs) of dust emission to observed fluxes. Under the standard assumption of optically thin single temperature (T) sources the dust SED as represented by a power--law modified black body is subject to a strong degeneracy between T and the spectral index beta. The traditional non-hierarchical approaches, typically based on chi-square minimization, are severely limited by this degeneracy, as it produces an artificial anti-correlation between T and beta even with modest levels of observational noise. The hierarchical Bayesian method rigorously and self-consistently treats measurement uncertainties, including calibration and noise, resulting in more precise SED fits. As a result, the Bayesian fits do not produce any spurious anti-correlations between the SED parameters due to measurement uncertainty. We demonstrate that the Bayesian method is substantially more accurate than the chi-square fit in recovering the SED parameters, as well as the correlations between them. As an illustration, we apply our method to Herschel and sub millimeter ground-based observations of the star-forming Bok globule CB244. This source is a small, nearby molecular cloud containing a single low-mass protostar and a starless core. We find that T and beta are weakly positively correlated -- in contradiction with the chi-square fits, which indicate a T-beta anti-correlation from the same data-set. Additionally, in comparison to the chi-square fits the Bayesian SED parameter estimates exhibit a reduced range in values.

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