Earth and Planetary Astrophysics (astro-ph.EP)

Abstract: Thousands of exoplanet detections have been made over the last twenty-five years using Doppler observations, transit photometry, direct imaging, and astrometry. Each of these methods is sensitive to different ranges of orbital separations and planetary radii (or masses). This makes it difficult to fully characterize exoplanet architectures and to place our solar system in context with the wealth of discoveries that have been made. Here, we use the EXtreme PREcision Spectrograph (EXPRES) to reveal planets in previously undetectable regions of the mass-period parameter space for the star $\rho$ Coronae Borealis. We add two new planets to the previously known system with one hot Jupiter in a 39-day orbit and a warm super-Neptune in a 102-day orbit. The new detections include a temperate Neptune planet ($M{\sin{i}} \sim 20$ M$_\oplus$) in a 281.4-day orbit and a hot super-Earth ($M{\sin{i}} = 3.7$ M$_\oplus$) in a 12.95-day orbit. This result shows that details of planetary system architectures have been hiding just below our previous detection limits; this signals an exciting era for the next generation of extreme precision spectrographs.

Abstract: Escaping exoplanet atmospheres have been observed as deep transit signatures in a few specific spectral lines. Detections have been made in the hydrogen Ly-$\alpha$ line, the metastable helium line at 10830 {\AA} and some UV lines of metallic species. Observational challenges, unexpected non-detections and model degeneracies have generally made it difficult to draw definitive conclusions about the escape process for individual planets. Expanding on the suite of spectral tracers used may help to mitigate these challenges. We present a new framework for modeling the transmission spectrum of hydrodynamically escaping atmospheres. We predict FUV to NIR spectra for systems with different planet and stellar types and identify new lines that can potentially be used to study their upper atmospheres. Measuring the radius in the atmosphere at which the strongest lines form puts them into context within the upper atmospheric structure. Targeting a set of complementary spectral lines for the same planet will help us to better constrain the outflow properties.

Abstract: The mechanism of dust emission from a cometary nucleus is still an open question and thermophysical models have problems reproducing outgassing and dust productions rates simultaneously. In this study, we investigate the capabilities of a rather simple thermophysical model to match observations from Rosetta instruments at comet 67P/Churyumov-Gerasimenko and the influence of model variations. We assume a macro-porous surface structure composed of pebbles and investigate the influence of different model assumptions. Besides the scenario in which dust layers are ejected when the vapour pressure overcomes the tensile strength, we use artificial ejection mechanisms, depending on ice-depletion of layers. We find that dust activity following the pressure criterion is only possible for reduced tensile strength values or reduced gas diffusivity and is inconsistent with observed outgassing rates, because activity is driven by CO$_2$. Only when we assume that dust activity is triggered when the layer is completely depleted in H$_2$O, the ratio of CO$_2$ to H$_2$O outgassing rates is in the expected order of magnitude. However, the dust-to-H$_2$O ratio is never reproduced. Only with decreased gas diffusivity, the slope of the H$_2$O outgassing rate is matched, however absolute values are too low. To investigate maximum reachable pressures, we adapted our model equivalent to a gas-impermeable dust structure. Here, pressures exceeding the tensile strength by orders of magnitude are possible. Maximum activity distances of $3.1 \,\mathrm{au}$, $8.2 \,\mathrm{au}$, and $74 \,\mathrm{au}$ were estimated for H$_2$O-, CO$_2$-, and CO-driven activity of $1 \,\mathrm{cm}$-sized dust, respectively. In conclusion, the mechanism behind dust emission remains unclear.

Abstract: In this work, we investigated Bayesian methodologies for constraining in the Solar System a Yukawa suppression of the Newtonian potential -- which we interpret as the effect of a non-null graviton mass -- by considering its impact on planetary orbits. Complementary to the previous results obtained with INPOP planetary ephemerides, we consider here a Markov Chain Monte Carlo approach associated with a Gaussian Process Regression for improving the resolution of the constraints driven by planetary ephemerides on the graviton mass in the Solar System. At the end of the procedure, a posterior for the mass of the graviton is presented, providing an upper bound at $1.01 \times 10^{-24} \; eV c^{-2}$ (resp. $\lambda_g \geq 122.48 \times 10^{13} \; km$) with a $99.7\%$ confidence level. The threshold value represents an improvement of 1 order of magnitude relative to the previous estimations. This updated determination of the upper bound is mainly due to the Bayesian methodology, although the use of new planetary ephemerides (INPOP21a used here versus INPOP19a used previously) already induces a gain of a factor 3 with respect to the previous limit. The INPOP21a ephemerides is characterized by the addition of new Juno and Mars orbiter data, but also by a better Solar System modeling, with notably a more realistic model of the Kuiper belt. Finally, by testing the sensitivity of our results to the choice of the $\textit{a priori}$ distribution of the graviton mass, it turns out that the selection of a prior more favorable to zero-mass graviton (that is, here, General Relativity) seems to be more supported by the observations than non-zero mass graviton, leading to a possible conclusion that planetary ephemerides are more likely to favor General Relativity.

Abstract: In this era of exoplanet characterisation with JWST, the need for a fast implementation of classical forward models to understand the chemical and physical processes in exoplanet atmospheres is more important than ever. Notably, the time-dependent ordinary differential equations to be solved by chemical kinetics codes are very time-consuming to compute. In this study, we focus on the implementation of neural networks to replace mathematical frameworks in one-dimensional chemical kinetics codes. Using the gravity profile, temperature-pressure profiles, initial mixing ratios, and stellar flux of a sample of hot-Jupiters atmospheres as free parameters, the neural network is built to predict the mixing ratio outputs in steady state. The architecture of the network is composed of individual autoencoders for each input variable to reduce the input dimensionality, which is then used as the input training data for an LSTM-like neural network. Results show that the autoencoders for the mixing ratios, stellar spectra, and pressure profiles are exceedingly successful in encoding and decoding the data. Our results show that in 90% of the cases, the fully trained model is able to predict the evolved mixing ratios of the species in the hot-Jupiter atmosphere simulations. The fully trained model is ~1000 times faster than the simulations done with the forward, chemical kinetics model while making accurate predictions.