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Rene Heller
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Is there any hope for us to draw a plausible picture of the future of exoplanet research? Here we extrapolate from the first 25 years of exoplanet discovery into the year 2050. If the power law for the cumulative exoplanet count continues, then almost 100,000,000 exoplanets would be known by 2050. Although this number sounds ridiculously large, we find that the power law could plausibly continue until at least as far as 2030, when Gaia and WFIRST will have discovered on the order of 100,000 exoplanets. After an early era of radial velocity detection, we are now in the transit era, which might be followed by a transit and astrometry era dominated by the WFIRST and Gaia missions. And then? Maybe more is not better. A small and informal survey among astronomers at the Exoplanet Vision 2050 workshop in Budapest suggests that astrobiological topics might influence the future of exoplanet research.
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This white paper proposes that AMBITION, a Comet Nucleus Sample Return mission, be a cornerstone of ESAs Voyage 2050 programme. We summarise some of the most important questions still open in cometary science after the successes of the Rosetta mission, many of which require sample analysis using techniques that are only possible in laboratories on Earth. We then summarise measurements, instrumentation and mission scenarios that can address these questions, with a recommendation that ESA select an ambitious cryogenic sample return mission. Rendezvous missions to Main Belt comets and Centaurs are compelling cases for M-class missions, expanding our knowledge by exploring new classes of comets. AMBITION would engage a wide community, drawing expertise from a vast range of disciplines within planetary science and astrophysics. With AMBITION, Europe will continue its leadership in the exploration of the most primitive Solar System bodies.
A dedicated mission to investigate exoplanetary atmospheres represents a major milestone in our quest to understand our place in the universe by placing our Solar System in context and by addressing the suitability of planets for the presence of life. EChO -the Exoplanet Characterisation Observatory- is a mission concept specifically geared for this purpose. EChO will provide simultaneous, multi-wavelength spectroscopic observations on a stable platform that will allow very long exposures. EChO will build on observations by Hubble, Spitzer and groundbased telescopes, which discovered the first molecules and atoms in exoplanetary atmospheres. EChO will simultaneously observe a broad enough spectral region -from the visible to the mid-IR- to constrain from one single spectrum the temperature structure of the atmosphere and the abundances of the major molecular species. The spectral range and resolution are tailored to separate bands belonging to up to 30 molecules to retrieve the composition and temperature structure of planetary atmospheres. The target list for EChO includes planets ranging from Jupiter-sized with equilibrium temperatures Teq up to 2000 K, to those of a few Earth masses, with Teq ~300 K. We have baselined a dispersive spectrograph design covering continuously the 0.4-16 micron spectral range in 6 channels (1 in the VIS, 5 in the IR), which allows the spectral resolution to be adapted from several tens to several hundreds, depending on the target brightness. The instrument will be mounted behind a 1.5 m class telescope, passively cooled to 50 K, with the instrument structure and optics passively cooled to ~45 K. EChO will be placed in a grand halo orbit around L2. We have also undertaken a first-order cost and development plan analysis and find that EChO is easily compatible with the ESA M-class mission framework.
Starshade in formation flight with a space telescope is a rapidly maturing technology that would enable imaging and spectral characterization of small planets orbiting nearby stars in the not-too-distant future. While performance models of the starshade-assisted exoplanet imaging have been developed and used to design future missions, their results have not been verified from the analyses of synthetic images. Following a rich history of using community data challenges to evaluate image-processing capabilities in astronomy and exoplanet fields, the Starshade Technology Development to TRL5 (S5), a focused technology development activity managed by the NASA Exoplanet Exploration Program, is organizing and implementing a starshade exoplanet data challenge. The purpose of the data challenge is to validate the flow down of requirements from science to key instrument performance parameters and to quantify the required accuracy of noisy background calibration with synthetic images. This data challenge distinguishes itself from past efforts in the exoplanet field in that (1) it focuses on the detection and spectral characterization of small planets in the habitable zones of nearby stars, and (2) it develops synthetic images that simultaneously include multiple background noise terms -- some specific to starshade observations -- including residual starlight, solar glint, exozodiacal light, detector noise, as well as variability resulting from starshades motion and telescope jitter. In this paper, we provide an overview of the design and rationale of the data challenge. Working with data challenge participants, we expect to achieve improved understanding of the noise budget and background calibration in starshade-assisted exoplanet observations in the context of both Starshade Rendezvous with Roman and HabEx.
KMOS (K-Band Multi Object Spectrograph) is a novel integral field spectrograph installed in the VLTs ANTU unit. The instrument offers an ability to observe 24 2.8$times$2.8 sub-fields positionable within a 7.2 patrol field, each sub-field producing a spectrum with a 14$times$14-pixel spatial resolution. The main science drivers for KMOS are the study of galaxies, star formation, and molecular clouds, but its ability to simultaneously measure spectra of multiple stars makes KMOS an interesting instrument for exoplanet atmosphere characterization via transmission spectroscopy. We set to test whether transmission spectroscopy is practical with KMOS, and what are the conditions required to achieve the photometric precision needed, based on observations of a partial transit of WASP-19b, and full transits of GJ 1214b and HD 209458b. Our analysis uses the simultaneously observed comparison stars to reduce the effects from instrumental and atmospheric sources, and Gaussian processes to model the residual systematics. We show that KMOS can, in theory, deliver the photometric precision required for transmission spectroscopy. However, this is shown to require a) pre-imaging to ensure accurate centering and b) a very stable night with optimal observing conditions (seeing $sim$0.8). Combining these two factors with the need to observe several transits, each with a sufficient out-of-transit baseline (and with the fact that similar or better precision can be reached with telescopes and instruments with smaller pressure,) we conclude that transmission spectroscopy is not the optimal science case to take advantage of the abilities offered by KMOS and VLT.
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit some of these methods to improve the conventional algorithm based approaches presently used in astrophysics to detect exoplanets. Using the time-series analysis library TSFresh to analyse light curves, we extracted 789 features from each curve, which capture the information about the characteristics of a light curve. We then used these features to train a gradient boosting classifier using the machine learning tool lightgbm. This approach was tested on simulated data, which showed that is more effective than the conventional box least squares fitting (BLS) method. We further found that our method produced comparable results to existing state-of-the-art deep learning models, while being much more computationally efficient and without needing folded and secondary views of the light curves. For Kepler data, the method is able to predict a planet with an AUC of 0.948, so that 94.8 per cent of the true planet signals are ranked higher than non-planet signals. The resulting recall is 0.96, so that 96 per cent of real planets are classified as planets. For the Transiting Exoplanet Survey Satellite (TESS) data, we found our method can classify light curves with an accuracy of 0.98, and is able to identify planets with a recall of 0.82 at a precision of 0.63.
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