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ABSTRACT Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads. 

Abstract Peatlands play a critical role in the global carbon (C) cycle, encompassing ∼30% of the 1,500 Pg of C stored in soils worldwide. However, this C is vulnerable to climate and land‐use change. Ecosystem models predict the impact of perturbation on C fluxes based on soil C pools, yet responses could vary markedly depending on soil organic matter (SOM) chemistry. Here, we show that one SOM functional group responds strongly to environmental factors and predicts the risk of carbon dioxide (CO₂) release from peatlands. The molecular composition of SOM in 125 peatlands differed markedly at the global scale due to variation in temperature, land‐use, vegetation, and nutrient status. Despite this variation, incubation of peat from a subset of 11 sites revealed thatO‐alkyl C (i.e., carbohydrates) was the strongest predictor of aerobic CO₂production. This explicit link provides a simple parameter that can improve models of peatland CO₂fluxes.