RESUMO
Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavelengths1-3. Polycyclic aromatic hydrocarbons (PAHs) are large organic molecules that trace millimetre-size dust grains and regulate the cooling of interstellar gas within galaxies4,5. Observations of PAH features in very distant galaxies have been difficult owing to the limited sensitivity and wavelength coverage of previous infrared telescopes6,7. Here we present James Webb Space Telescope observations that detect the 3.3 µm PAH feature in a galaxy observed less than 1.5 billion years after the Big Bang. The high equivalent width of the PAH feature indicates that star formation, rather than black hole accretion, dominates infrared emission throughout the galaxy. The light from PAH molecules, hot dust and large dust grains and stars are spatially distinct from one another, leading to order-of-magnitude variations in PAH equivalent width and ratio of PAH to total infrared luminosity across the galaxy. The spatial variations we observe suggest either a physical offset between PAHs and large dust grains or wide variations in the local ultraviolet radiation field. Our observations demonstrate that differences in emission from PAH molecules and large dust grains are a complex result of localized processes within early galaxies.
RESUMO
Quantifying image distortions caused by strong gravitational lensing-the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures-and estimating the corresponding matter distribution of these structures (the 'gravitational lens') has primarily been performed using maximum likelihood modelling of observations. This procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. Here we report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the 'singular isothermal ellipsoid' density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.
