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tokens: has delayed or is preventing prodigious star formation in the Brick? Dynamical arguments, such as its highly turbulent nature (Kauffmann, Pillai Zhang 2013 ; Johnston et al. 2014 ; Rathborne et al. 2015 ) or its apparent location within a coherent ring-like stream of molecular clouds in orbit about Sgr A* (Molinari et al. 2011 ; Longmore et al. 2013 ; Kruijssen et al. 2015 ) have been cited as possible agents of star formation suppression. In order to set more stringent constraints on the level of star formation activity, it would seem worthwhile to examine other possible indicators, such as the presence of localized peaks in dust temperature that might signal the presence of protostars at early stages of evolution. With this in mind, we present here the results of a detailed study of the variation of dust temperature within the cloud. Studies of the temperature structure of the Brick show that the gas temperature is uniformly higher than the dust temperature and that those two components are heated by different mechanisms. The observed dust temperatures of T D ∼ 19–27 K (Longmore et al. 2012 ) are consistent with heating by the local interstellar radiation field (ISRF), assuming that the latter is ∼1000 times stronger than that in the solar vicinity (Clark et al. 2013 ). However, the gas (Güsten, Walmsley Pauls 1981 ; Mills Morris 2013 , T G ∼ 60–100 K) is most likely heated by a combination of cosmic rays (Clark et al. 2013 ) and turbulent compression (Ginsburg et al. 2016 ), with the former dominating at high densities. Our study of the dust temperature distribution makes use of the PPMAP procedure (Marsh, Whitworth Lomax 2015 ), whose purpose is to generate resolution-enhanced image cubes of differential column density as a function of dust temperature, T D , and sky position ( x , y ), given sets of observed images of dust continuum emission at multiple wavelengths. Differential column density, in this case, is defined as the column density of material (gas plus dust) per unit interval of dust temperature, and is expressed in units of hydrogen molecules per square centimetre per degree Kelvin. PPMAP is a Bayesian technique whose output is the expectation value of the differential column density in each cell of an image cube whose axes are ( x , y , T D ), given the input data. The a priori probability of cell occupancy is controlled by a ‘dilution’ parameter, η, such that the smaller the value of η, the more the algorithm tries to fit the data with as few source components as possible. The exact value of η is not critical, but the most appropriate value is one which results in a reduced chi-squared value of the order of unity, indicating that the data have been fit within the error bars using a minimum number of source components. The procedure also yields a corresponding image cube of uncertainty values. Errors are Poisson-like, and increase in regions of high total column density. A key underlying assumption is that the dust is