Context.The thermal emission of dust grains is a powerful tool for probing cold, dense regions of molecular gas in the interstellar medium, and so constraining dust properties is key to obtaining accurate measurements of dust mass and temperature.Aims.By placing constraints on the dust emissivity spectral index,β, towards two star-forming infrared dark clouds – SDC18.888–0.476 and SDC24.489–0.689 – we aim to evaluate the role of mass concentration in the associated star-formation activity.Methods.We exploited the simultaneous 1.2 and 2.0 mm imaging capability of the NIKA camera on the IRAM 30 m telescope to construct maps ofβfor both clouds, and by incorporatingHerschelobservations, we created H2column density maps with 13′′ angular resolution.Results.While we find no significant systematic radial variations around the most massive clumps in either cloud on ≳0.1 pc scales, their meanβvalues are significantly different, withβ̅ = 2.07 ± 0.09 (random) ± 0.25 (systematic) for SDC18.888–0.476 andβ̅ = 1.71 ± 0.09 (random) ± 0.25 (systematic) for SDC24.489–0.689. These differences could be a consequence of the very different environments in which both clouds lie, and we suggest that the proximity of SDC18.888–0.476 to the W39 HIIregion may raiseβon scales of ~1 pc. We also find that the mass in SDC24.489–0.689 is more centrally concentrated and circularly symmetric than in SDC18.888–0.476, and is consistent with a scenario in which spherical globally-collapsing clouds concentrate a higher fraction of their mass into a single core than elongated clouds that will more easily fragment, distributing their mass into many cores.Conclusions.We demonstrate thatβvariations towards interstellar clouds can be robustly constrained with high signal-to-noise ratio (S/N) NIKA observations, providing more accurate estimates of their masses. The methods presented here will be applied to the Galactic Star Formation with NIKA2 (GASTON) guaranteed time large programme, extending our analysis to a statistically significant sample of star-forming clouds.

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