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Register a new userThe formation of planetary systems with SPICA
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In this era of spatially resolved observations of planet forming disks with ALMA and large ground-based telescopes such as the VLT, Keck and Subaru, we still lack statistically relevant information on the quantity and composition of the material that is building the planets, such as the total disk gas mass, the ice content of dust, and the state of water in planetesimals. SPICA is an infrared space mission concept developed jointly by JAXA and ESA to address these questions. The key unique capabilities of SPICA that enable this research are (1) the wide spectral coverage 10-220 micron, (2) the high line detection sensitivity of (1-2) 10-19 W m-2 with R~2000-5000 in the far-IR (SAFARI) and 10-20 W m-2 with R~29000 in the mid-IR (SMI, spectrally resolving line profiles), (3) the high far-IR continuum sensitivity of 0.45 mJy (SAFARI), and (4) the observing efficiency for point source surveys. This paper details how mid- to far-IR infrared spectra will be unique in measuring the gas masses and water/ice content of disks and how these quantities evolve during the planet forming period. These observations will clarify the crucial transition when disks exhaust their primordial gas and further planet formation requires secondary gas produced from planetesimals. The high spectral resolution mid-IR is also unique for determining the location of the snowline dividing the rocky and icy mass reservoirs within the disk and how the divide evolves during the build-up of planetary systems. Infrared spectroscopy (mid- to far-IR) of key solid state bands is crucial for assessing whether extensive radial mixing, which is part of our Solar System history, is a general process occurring in most planetary systems and whether extrasolar planetesimals are similar to our Solar System comets/asteroids. ... (abbreviated)
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Stars and planets are the fundamental objects of the Universe. Their formation processes, though related, may differ in important ways. Stars almost certainly form from gravitational collapse and probably have formed this way since the first stars lit the skies. Although it is possible that planets form in this way also, processes involving accretion in a circumstellar disk have been favored. High fidelity high resolution images may resolve the question; both processes may occur in some mass ranges. The questions to be answered in the next decade include: By what process do planets form, and how does the mode of formation determine the character of planetary systems? What is the distribution of masses of planets? In what manner does the metallicity of the parent star influence the character of its planetary system? In this paper we discuss the observations of planetary systems from birth to maturity, with an emphasis on observations longward of 100 $mu$m which may illuminate the character of their formation and evolution. Advantages of this spectral region include lower opacity, availability of extremely high resolution to reach planet formation scales and to perform precision astrometry and high sensitivity to thermal emission.
As part of a national scientific network Pathways to Habitability the formation of planets and the delivery of water onto these planets is a key question as water is essential for the development of life. In the first part of the paper we summarize the state of the art of planet formation - which is still under debate in the astronomical community - before we show our results on this topic. The outcome of our numerical simulations depends a lot on the choice of the initial distribution of planetesimals and planetary embryos after gas disappeared in the protoplanetary disk. We also take into account that some of these planetesimals of sizes in the order of the mass of the Moon already contained water; the quantity depends on the distance from the Sun - close-by bodies are dry, but starting from a distance of about 2 AU they can contain substantial amounts of water. We assume that the gas giants and terrestrial planets are already formed when we check the collisions of the small bodies containing water (in the order of a few percent) with the terrestrial planets. We thus are able to give an estimate of the respective contribution to the actual water content (of some Earth-oceans) in the mantle, in the crust and on the surface of Earth. In the second part we discuss in more detail how the formation of larger bodies after a collision may happen as the outcome depends on parameters like collision velocity, impact angle, and the materials involved. We present results obtained by SPH (Smooth Particle Hydrodynamics) simulations. We briefly describe this method and show different scenarios with respect to the formed bodies, possible fragmentation and the water content before and after the collision. In an appendix we discuss detection methods for extrasolar planets (close to 2000 such objects have been discovered so far).
The study of extrasolar planets and of the Solar System provides complementary pieces of the mosaic represented by the process of planetary formation. Exoplanets are essential to fully grasp the huge diversity of outcomes that planetary formation and the subsequent evolution of the planetary systems can produce. The orbital and basic physical data we currently possess for the bulk of the exoplanetary population, however, do not provide enough information to break the intrinsic degeneracy of their histories, as different evolutionary tracks can result in the same final configurations. The lessons learned from the Solar System indicate us that the solution to this problem lies in the information contained in the composition of planets. The goal of the Atmospheric Remote-Sensing Infrared Exoplanet Large-survey (ARIEL), one of the three candidates as ESA M4 space mission, is to observe a large and diversified population of transiting planets around a range of host star types to collect information on their atmospheric composition. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres, which should show minimal condensation and sequestration of high-Z materials and thus reveal their bulk composition across all main cosmochemical elements. In this work we will review the most outstanding open questions concerning the way planets form and the mechanisms that contribute to create habitable environments that the compositional information gathered by ARIEL will allow to tackle
We present here a new robotic telescope called TRAPPIST (TRAnsiting Planets and PlanetesImals Small Telescope). Equipped with a high-quality CCD camera mounted on a 0.6 meter light weight optical tube, TRAPPIST has been installed in April 2010 at the ESO La Silla Observatory (Chile), and is now beginning its scientific program. The science goal of TRAPPIST is the study of planetary systems through two approaches: the detection and study of exoplanets, and the study of comets. We describe here the objectives of the project, the hardware, and we present some of the first results obtained during the commissioning phase.
Despite over three hundred years of effort, no solutions exist for predicting when a general planetary configuration will become unstable. We introduce a deep learning architecture to push forward this problem for compact systems. While current machine learning algorithms in this area rely on scientist-derived instability metrics, our new technique learns its own metrics from scratch, enabled by a novel internal structure inspired from dynamics theory. Our Bayesian neural network model can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both non-resonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to five orders of magnitude faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK package, with training code open-sourced.
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