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Register a new userTRAPPIST: a robotic telescope dedicated to the study of planetary systems
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M. Gillon
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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.
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TRAPPIST-1 is a fantastic nearby (~39.14 light years) planetary system made of at least seven transiting terrestrial-size, terrestrial-mass planets all receiving a moderate amount of irradiation. To date, this is the most observationally favourable system of potentially habitable planets. Since the announcement of the discovery of TRAPPIST-1 planets in 2016, a growing number of techniques and approaches have been used and proposed to reveal its true nature. Here we have compiled a state-of-the-art overview of all the observational and theoretical constraints that have been obtained so far using these techniques and approaches. The goal is to get a better understanding of whether or not TRAPPIST-1 planets can have atmospheres, and if so, what they are made of. For this, we surveyed the literature on TRAPPIST-1 about topics as broad as irradiation environment, orbital architecture, transit observations, density measurements, stellar contamination, and numerical climate and escape models. Each of these topics adds a brick to our understanding of the likely atmospheres of the seven planets. We show that (i) HST transit observations, (ii) density measurements, (iii) atmospheric escape modelling, and (iv) gas accretion modelling altogether offer solid evidence against the presence of H2-dominated atmospheres around TRAPPIST-1 planets. This means they likely have either (i) a high molecular weight atmosphere or (ii) no atmosphere at all. There are several key challenges ahead to characterize the bulk compositions of the atmospheres (if present) of TRAPPIST-1 planets. The main one so far is characterizing and correcting for the effects of stellar contamination. Fortunately, a new wave of observations with the James Webb Space Telescope and near-infrared high-resolution ground-based spectrographs on very large telescopes will bring significant advances in the coming decade.
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 study the evolution of protoplanetary discs that would have been precursors of a Trappist-1 like system under the action of accretion and external photoevaporation in different radiation environments. Dust grains swiftly grow above the critical size below which they are entrained in the photoevaporative wind, so although gas is continually depleted, dust is resilient to photoevaporation after only a short time. This means that the ratio of the mass in solids (dust plus planetary) to the mass in gas rises steadily over time. Dust is still stripped early on, and the initial disc mass required to produce the observed $4,M_{oplus}$ of Trappist-1 planets is high. For example, assuming a Fatuzzo & Adams (2008) distribution of UV fields, typical initial disc masses have to be $>30,$per cent the stellar (which are still Toomre $Q$ stable) for the majority of similar mass M dwarfs to be viable hosts of the Trappist-1 planets. Even in the case of the lowest UV environments observed, there is a strong loss of dust due to photoevaporation at early times from the weakly bound outer regions of the disc. This minimum level of dust loss is a factor two higher than that which would be lost by accretion onto the star during 10 Myr of evolution. Consequently even in these least irradiated environments, discs that are viable Trappist-1 precursors need to be initially massive ($>10,$per cent of the stellar mass).
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)
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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