Exoplanets and Stellar Physics

Abstract:

We analyse the ro-vibrational absorption bands of various molecular cations (HCO, HCNH, and NH) and neutral species (HCN, HNC, and HCN) detected in the James Webb Space Telescope/Mid-Infrared Instrument Medium Resolution Spectrometer spectrum (4.9–27.9 μm) of the local ultraluminous infrared galaxy IRAS 07251-0248. We find that the molecular absorptions are blueshifted by 160 km s relative to the systemic velocity of the target. Using local thermal equilibrium excitation models, we derive rotational temperatures () from 42 to 185 K for these absorption bands. This range of measured can be explained by infrared radiative pumping as a by-product of the strength, effective critical density, and opacity of each molecular band. Thus, these results suggest that these absorptions originate in a warm expanding gas shell (90–330 yr), which might be the base of the larger scale cold molecular outflow detected in this source. Finally, the elevated abundance of molecular cations can be explained by a high cosmic ray ionization rate, with log(/n in the range of -18.2 (from H) to -19.1 (inferred from HCO and NH, which are likely tracing denser gas), consistent with a cosmic ray dominated chemistry as predicted by chemical models.

Abstract:

In recent years, supergranulation has emerged as one of the biggest challenges for the detection of Earth-twins in radial velocity planet searches. We used eight years of Sun-as-a-star radial velocity observations from HARPS-N to measure the quiet-Sun’s granulation and supergranulation properties of most of its 11-yr activity cycle, after correcting for the effects of magnetically active regions using two independent methods. In both cases, we observe a clear, order of magnitude variation in the time-scale of the supergranulation component, which is largest at activity minimum and is strongly anticorrelated with the relative Sunspot number. We also explored a range of observational strategies which could be employed to characterize supergranulation in stars other than the Sun, showing that a comparatively long observing campaign of at least 23 nights is required, but that up to 10 stars can be monitored simultaneously in the process. We conclude by discussing plausible explanations for the ‘supergranulation’ cycle.

Abstract:

To aid the search for atmospheres on rocky exoplanets, we should know what to look for. An unofficial paradigm is to anticipate CO2 present in these atmospheres, through analogy to the solar system and through theoretical modelling. This CO2 would be outgassed from molten silicate rock produced in the planet’s mostly-solid interior—an ongoing self-cooling mechanism that should proceed, in general, so long as the planet has sufficient internal heat to lose.Outgassing of CO2 requires relatively oxidising conditions. Previous work has noted the importance of how oxidising the planet interior is (the oxygen fugacity), which depends strongly on its rock composition. Current models presume that redox reactions between iron species control oxygen fugacity. However, iron alone need not be the sole dictator of how oxidising a planet is. Indeed, carbon itself is a powerful redox element, with great potential to feed back upon the mantle redox state as it melts. Whilst Earth is carbon-poor, even a slightly-higher volatile endowment could trigger carbon-powered geochemistry.We offer a new framework for how carbon is transported from solid planetary interior to atmosphere. The model incorporates realistic carbon geochemistry constrained by recent experiments on CO2 solubility in molten silicate, as well as redox couplings between carbon and iron that have never before been applied to exoplanets. We also incorporate a coupled 1D energy- and mass-balance model to provide first-order predictions of the rate of volcanism.We show that carbon-iron redox coupling maintains interior oxygen fugacity in a narrow range: more reducing than Earth magma, but not reducing enough to destabilise CO2 gas. We predict that most secondary atmospheres, if present, should contain CO2, although the total pressure could be low. An atmospheric non-detection may indicate a planet either born astonishingly dry, or having shut off its internal heat engine.

Abstract:

In the context of large scale photometric surveys, monitoring hundreds of thousands of stars in the search for exoplanets, one of the main bottlenecks remains reliable and rapid identification of exoplanet candidates. As it stands, the detection of exoplanets in light curves remains a complicated process, which can be thrown off by stellar activity, or instrument systematics. The task becomes increasingly harder for long period planets, taking away the ability to search for periodic signals within the high precision light curves. In an effort to find Earth-analogs, which are by definition long period planets, often with shallow transits, our ability to avoid periodicity in the detection process is key. Additionally, since current filtering methods are not well suited to filter unique, shallow, transits, they risk erasing the presence of these signals altogether before the detection step can be run. Such cases not only lead to missed planets, but they also induce a bias in the final distribution, by removing key planets in our sample.To this end, we develop the Panopticon deep learning model, trained to identify transits individually in unfiltered light curves. First trained on simulated PLATO data [1], we report the model’s ability to correctly identify >99% of the light curves containing transits with a SNR>3 (Fig.1), while keeping a false alarm rate of less than 0.01% [2]. When applied on a new, independent, dataset in a blind search scenario, we are able to confidently recover the transiting planets in >98% of the cases. In a second time, a dedicated version of the model was trained on TESS data to measure the impact of real world data on the model. As for previously, we find the model to be highly effective at recovering transits, correctly reporting >93% of the light curves containing transits, while achieving a false alarm rate of