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The key goals of the astrobiology community are to identify environments beyond Earth that may be habitable, and to search for signs of life in those environments. A fundamental aspect of understanding the limits of habitable environments and detectable signatures is the study of where such environments can occur. Thus, the need to study the creation, evolution, and frequency of environments hostile to habitability is an integral part of the astrobiology story. The study of these environments provides the opportunity to understand the bifurcation between habitable and uninhabitable conditions on planetary bodies. The archetype of such a planet is Earths sibling planet, Venus, which provides a unique opportunity to explore the processes that created a completely uninhabitable environment and thus define the conditions that rule out bio-related signatures. We advocate a continued comprehensive study of our neighboring planet, to include models of early atmospheres, compositional abundances, and Venus-analog frequency analysis from current and future exoplanet data. Critically, new missions to Venus that provide in-situ data are necessary to address the major gaps in our current understanding, and to enable us to take the next steps in characterizing planetary habitability.
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This is a white paper submitted to the Planetary Science and Astrobiology Decadal Survey. The deep atmosphere of Venus is largely unexplored and yet may harbor clues to the evolutionary pathways for a major silicate planet with implications across the solar system and beyond. In situ data is needed to resolve significant open questions related to the evolution and present-state of Venus, including questions of Venus possibly early habitability and current volcanic outgassing. Deep atmosphere probe-based in situ missions carrying analytical suites of instruments are now implementable in the upcoming decade (before 2030), and will both reveal answers to fundamental questions on Venus and help connect Venus to exoplanet analogs to be observed in the JWST era of astrophysics.
The quest for other habitable worlds and the search for life among them are major goals of modern astronomy. One way to make progress towards these goals is to obtain high-quality spectra of a large number of exoplanets over a broad range of wavelengths. While concepts currently investigated in the United States are focused on visible/NIR wavelengths, where the planets are probed in reflected light, a compelling alternative to characterize planetary atmospheres is the mid-infrared waveband (5-20~$mu$m). Indeed, mid-infrared observations provide key information on the presence of an atmosphere, the surface conditions (e.g., temperature, pressure, habitability), and the atmospheric composition in important species such as H$_2$O, CO$_2$, O$_3$, CH$_4$, and N$_2$O. This information is essential to investigate the potential habitability of exoplanets and to make progress towards the search for life in the universe. Obtaining high-quality mid-infrared spectra of exoplanets from the ground is however extremely challenging due to the overwhelming brightness and turbulence of Earths atmosphere. In this paper, we present a concept of space-based mid-infrared interferometer that can tackle this observing challenge and discuss the main technological developments required to launch such a sophisticated instrument.
We apply the Bayesian framework to assess the presence of a correlation between two quantities. To do so, we estimate the probability distribution of the parameter of interest, $rho$, characterizing the strength of the correlation. We provide an implementation of these ideas and concepts using python programming language and the pyMC module in a very short ($sim$130 lines of code, heavily commented) and user-friendly program. We used this tool to assess the presence and properties of the correlation between planetary surface gravity and stellar activity level as measured by the log($R_{mathrm{HK}}$) indicator. The results of the Bayesian analysis are qualitatively similar to those obtained via p-value analysis, and support the presence of a correlation in the data. The results are more robust in their derivation and more informative, revealing interesting features such as asymmetric posterior distributions or markedly different credible intervals, and allowing for a deeper exploration. We encourage the reader interested in this kind of problem to apply our code to his/her own scientific problems. The full understanding of what the Bayesian framework is can only be gained through the insight that comes by handling priors, assessing the convergence of Monte Carlo runs, and a multitude of other practical problems. We hope to contribute so that Bayesian analysis becomes a tool in the toolkit of researchers, and they understand by experience its advantages and limitations.
The WGLA of the AAS (http://www.aas.org/labastro/) promotes collaboration and exchange of knowledge between astronomy and planetary sciences and the laboratory sciences (physics, chemistry, and biology). Laboratory data needs of ongoing and next generation planetary science missions are carefully evaluated and recommended in this white paper submitted by the WGLA to Planetary Decadal Survey.
MagAO-X is an entirely new extreme adaptive optics system for the Magellan Clay 6.5 m telescope, funded by the NSF MRI program starting in Sep 2016. The key science goal of MagAO-X is high-contrast imaging of accreting protoplanets at H$alpha$. With 2040 actuators operating at up to 3630 Hz, MagAO-X will deliver high Strehls (>70%), high resolution (19 mas), and high contrast ($< 1times10^{-4}$) at H$alpha$ (656 nm). We present an overview of the MagAO-X system, review the system design, and discuss the current project status.
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