Search

Brown dwarfs serve as ideal laboratories for studying the atmospheres of giant exoplanets on wide orbits, as the governing physical and chemical processes within them are nearly identical1,2. Understanding the formation of gas-giant planets is challenging, often involving the endeavour to link atmospheric abundance ratios, such as the carbon-to-oxygen (C/O) ratio, to formation scenarios3. However, the complexity of planet formation requires further tracers, as the unambiguous interpretation of the measured C/O ratio is fraught with complexity4. Isotope ratios, such as deuterium to hydrogen and 14N/15N, offer a promising avenue to gain further insight into this formation process, mirroring their use within the Solar System5–7. For exoplanets, only a handful of constraints on 12C/13C exist, pointing to the accretion of 13C-rich ice from beyond the CO iceline of the disks8,9. Here we report on the mid-infrared detection of the 14NH3 and 15NH3 isotopologues in the atmosphere of a cool brown dwarf with an effective temperature of 380 K in a spectrum taken with the Mid-Infrared Instrument (MIRI) of JWST. As expected, our results reveal a 14N/15N value consistent with star-like formation by gravitational collapse, demonstrating that this ratio can be accurately constrained. Because young stars and their planets should be more strongly enriched in the 15N isotope10, we expect that 15NH3 will be detectable in several cold, wide-separation exoplanets.Observations from the JWST MIRI showed the detection of 14NH3 and 15NH3 isotopologues in the atmosphere of a cool brown dwarf, along with a 14N/15N value consistent with star-like formation by gravitational collapse. [ABSTRACT FROM AUTHOR]

Context. Retrieving the physical parameters from spectroscopic observations of exoplanets is key to understanding their atmospheric properties. Exoplanetary atmospheric retrievals are usually based on approximate Bayesian inference and rely on sampling-based approaches to compute parameter posterior distributions. Accurate or repeated retrievals, however, can result in very long computation times due to the sequential nature of sampling-based algorithms. Aims. We aim to amortize exoplanetary atmospheric retrieval using neural posterior estimation (NPE), a simulation-based inference algorithm based on variational inference and normalizing flows. In this way, we aim (i) to strongly reduce inference time, (ii) to scale inference to complex simulation models with many nuisance parameters or intractable likelihood functions, and (iii) to enable the statistical validation of the inference results. Methods. We evaluated NPE on a radiative transfer model for exoplanet spectra (petitRADTRANS), including the effects of scattering and clouds. We trained a neural autoregressive flow to quickly estimate posteriors and compared against retrievals computed with MultiNest. Results. We find that NPE produces accurate posterior approximations while reducing inference time down to a few seconds. We demonstrate the computational faithfulness of our posterior approximations using inference diagnostics including posterior predictive checks and coverage, taking advantage of the quasi-instantaneous inference time of NPE. Our analysis confirms the reliability of the approximate posteriors produced by NPE. Conclusions. The inference results produced by NPE appear to be accurate and reliable, establishing this algorithm as a promising approach for atmospheric retrieval. Its main benefits come from the amortization of posterior inference: once trained, inference does not require on-the-fly simulations and can be repeated several times for many observations at a very low computational cost. This enables efficient, scalable, and testable atmospheric retrieval. [ABSTRACT FROM AUTHOR]