Earth and Planetary Astrophysics (astro-ph.EP)

Abstract: We investigate the orbital evolution of planetesimals in the inner disk in the presence of nebula gas and a (proto-) cold Jupiter. By varying the mass, eccentricity, and semi-major axis of the planet, we study the dependence of the relative velocities of the planetesimals on these parameters. For classic small planetesimals ($10^{16}-10^{20} $g) whose mutual gravitational interaction is negligible, gas drag introduces a size-dependent alignment of orbits and keeps the relative velocity low for similar-size bodies, while preventing orbital alignment for different-size planetesimals. Regardless of the location and the mass ratio of the planetesimals, increasing the mass and eccentricity or decreasing the orbital distance of the planet always leads to higher relative velocities of planetesimals. However, for massive planetesimals, the interplay of viscous stirring, gas damping, and secular perturbation results in lower velocity dispersion of equal-size planetesimals when the planet is more massive or when it is located on a closer or more eccentric orbit. The random velocities of such planetesimals remain almost unperturbed when the planet is located beyond Jupiter's current orbit, or when it is less massive or less eccentric than Jupiter. Unlike small planetesimals, such large planetesimals can grow in a runaway fashion as in the unperturbed case. Our results imply that the presence of a cold Jupiter does not impede the formation of inner rocky planets through planetesimal accretion, provided that the planetesimals are initially large.

Abstract: Atmospheric characterisation of exoplanets from the ground is an actively growing field of research. In this context we have created the ATMOSPHERIX consortium: a research project aimed at characterizing exoplanets atmospheres using ground-based high resolution spectroscopy. This paper presents the publicly-available data analysis pipeline and demonstrates the robustness of the recovered planetary parameters from synthetic data. Simulating planetary transits using synthetic transmission spectra of a hot Jupiter that were injected into real SPIRou observations of the non-transiting system Gl 15 A, we show that our pipeline is successful at recovering the planetary signal and input atmospheric parameters. We also introduce a deep learning algorithm to optimise data reduction which proves to be a reliable, alternative tool to the commonly used principal component analysis. We estimate the level of uncertainties and possible biases when retrieving parameters such as temperature and composition and hence the level of confidence in the case of retrieval from real data. Finally, we apply our pipeline onto two real transits of HD~189733 b observed with SPIRou and obtain similar results than in the literature. In summary, we have developed a publicly available and robust pipeline for the forthcoming studies of the targets to be observed in the framework of the ATMOSPHERIX consortium, which can easily be adapted to other high resolution instruments than SPIRou (e.g. VLT-CRIRES, MAROON-X, ELT-ANDES)

Abstract: In a companion paper, we introduced a publicly-available pipeline to characterise exoplanet atmospheres through high-resolution spectroscopy. In this paper, we use this pipeline to study the biases and degeneracies that arise in atmospheric characterisation of exoplanets in near-infrared ground-based transmission spectroscopy. We inject synthetic planetary transits into sequences of SPIRou spectra of the well known M dwarf star Gl 15 A, and study the effects of different assumptions on the retrieval. We focus on (i) mass and radius uncertainties, (ii) non isothermal vertical profiles and (iii) identification and retrieval of multiple species. We show that the uncertainties on mass and radius should be accounted for in retrievals and that depth-dependent temperature information can be derived from high-resolution transmission spectroscopy data. Finally, we discuss the impact of selecting wavelength orders in the retrieval and the issues that arise when trying to identify a single species in a multi-species atmospheric model. This analysis allows us to understand better the results obtained through transmission spectroscopy and their limitations in preparation to the analysis of actual SPIRou data.

Abstract: Crater mapping using neural networks and other automated methods has increased recently with automated Crater Detection Algorithms (CDAs) applied to planetary bodies throughout the solar system. A recent publication by Benedix et al. (2020) showed high performance at small scales compared to similar automated CDAs but with a net positive diameter bias in many crater candidates. I compare the publicly available catalogs from Benedix et al. (2020) and Lee & Hogan (2021) and show that the reported performance is sensitive to the metrics used to test the catalogs. I show how the more permissive comparison methods indicate a higher CDA performance by allowing worse candidate craters to match ground-truth craters. I show that the Benedix et al. (2020) catalog has a substantial performance loss with increasing latitude and identify an image projection issue that might cause this loss. Finally, I suggest future applications of neural networks in generating large scientific datasets be validated using secondary networks with independent data sources or training methods.