Exoplanets and Stellar Physics

Abstract:

This talk will cover the state of the art in whole-planet subNeptune modelling, and needs for the future.  Inferences about the composition of the deep envelope can be made on the basis of the way chemical transformations in the deep envelope may be evidenced in the observable atmosphere, such as has been attempted, for example, regarding the presence or absence of NH3 in the observable atmospheres of subNeptunes.  Such inferences require an understanding not only of deep envelope chemistry, but also of vertical mixing processes. The mixing process engages a number of poorly understood phenomena, such as mixing rates through stably stratified (nonconvective) internal radiative layers.  The occurrence of such radiative layers can be induced by compositional suppression of convection (e.g. due to high molecular weight H2O in an H2-rich atmosphere). We will review our modelling studies regarding this phenomenon.  Typically, the envelope-silicate interface is hot enough that the interface takes the form of a magma ocean, so compositional interchange with the magma ocean becomes crucial. This exchange includes rock vapours as well as lower molecular weight volatiles.  Our work on magma ocean exchanges will be reviewed. We highlight the importance of mineral physics experiments and molecular dynamics to provide crucially needed (and largely absent) thermodynamic parameters, particularly at high pressure.  At sufficiently high temperatures, silicate itself can become supercritical so that the distinction between silicate melt and silicate vapour disappears and the silicate substance becomes completely miscible with the lower molecular weight envelope.  Modeling and experiment regarding this novel and largely unexplored regime is particularly needed.

Abstract:

Abstract Limb-resolved transmission spectroscopy has the potential to transform our understanding of exoplanetary atmospheres. By separately measuring the transmission spectra of the evening and morning limbs, these atmospheric regions can be individually characterized, shedding light into the global distribution and transport of key atmospheric properties from transit observations alone. In this work, we follow up the recent detection of limb asymmetry on the exoplanet WASP-107 b by reanalyzing literature observations of WASP-107 b using all of James Webb Space Telescope’s science instruments (Near Infrared Imager and Slitless Spectrograph (NIRISS), Near-Infrared Camera, Near Infrared Spectrograph (NIRSpec), and Mid-Infrared Instrument) to measure its limb transmission spectra from ∼1 to 12 μ m. We confirm the evening–morning temperature difference inferred previously and find that it is qualitatively consistent with predictions from global circulation models. We find evidence for evening–morning variation in SO 2 and CO 2 abundance, and significant cloud coverage only on WASP-107 b’s morning limb. We find that the NIRISS and NIRSpec observations are potentially contaminated by occulted starspots, which we leverage to investigate stellar contamination’s impact on limb asymmetry measurements. We find that starspot crossings can significantly bias the inferred evening and morning transmission spectra depending on when they occur during the transit, and develop a simple correction model which successfully brings these instruments’ spectra into agreement with the uncontaminated observations.

Abstract:

Automated planetary transit detection has become vital to identify and prioritize candidates for expert analysis and verification given the scale of modern telescopic surveys. Current methods for short-period exoplanet detection work effectively due to periodicity in the transit signals, but a robust approach for detecting single-transit events is lacking. However, volunteer-labeled transits collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find that this model recovers planet candidates (TESS objects of interest; TOIs) at a precision and recall rate exceeding those of volunteers, with a 20% improvement in the area under the precision-recall curve and 10% more TOIs identified in the top 500 predictions on average per sector. Importantly, the model also recovers almost all planet candidates found by volunteers but missed by current automated methods (PHT community TOIs). Finally we retrospectively utilise the model to simulate live deployment in PHT to reprioritize candidates for analysis. We also find that multiple promising planet candidates, originally missed by PHT, would have been found using our approach, showing promise for upcoming real-world deployment.