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We report the detection of the ground state rotational emission of ammonia, ortho-NH3 (JK = 10 → 00) in a gravitationally lensed intrinsically hyperluminous star-bursting galaxy at z = 2.6. The integrated line profile is consistent with other molecular and atomic emission lines which have resolved kinematics well modelled by a 5 kpc-diameter rotating disc. This implies that the gas responsible for NH3 emission is broadly tracing the global molecular reservoir, but likely distributed in pockets of high density (n ≳ 5 × 104 cm−3). With a luminosity of 2.8 × 106 L⊙, the NH3 emission represents 2.5 × 10−7 of the total infrared luminosity of the galaxy, comparable to the ratio observed in the Kleinmann–Low nebula in Orion and consistent with sites of massive star formation in the Milky Way. If $L_{\rm NH_3}/L_{\rm IR}$ serves as a proxy for the ‘mode’ of star formation, this hints that the nature of star formation in extreme starbursts in the early Universe is similar to that of Galactic star-forming regions, with a large fraction of the cold interstellar medium in this state, plausibly driven by a storm of violent disc instabilities in the gas-dominated disc. This supports the ‘full of Orions’ picture of star formation in the most extreme galaxies seen close to the peak epoch of stellar mass assembly.

Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.