Context. The formation and dynamical history of hot Jupiters is currently debated, with wide stellar binaries having been suggested as a potential formation pathway. Additionally, contaminating light from both binary companions and unassociated stars can significantly bias the results of planet characterisation studies, but can be corrected for if the properties of the contaminating star are known. Aim. We search for binary companions to known transiting exoplanet host stars, in order to determine the multiplicity properties of hot Jupiter host stars. We also search for and characterise unassociated stars along the line of sight, allowing photometric and spectroscopic observations of the planetary system to be corrected for contaminating light. Methods. We analyse lucky imaging observations of 97 Southern hemisphere exoplanet host stars, using the Two Colour Instrument on the Danish 1.54 m telescope. For each detected companion star, we determine flux ratios relative to the planet host star in two passbands, and measure the relative position of the companion. The probability of each companion being physically associated was determined using our two-colour photometry. Results. A catalogue of close companion stars is presented, including flux ratios, position measurements, and estimated companion star temperature. For companions that are potential binary companions, we review archival and catalogue data for further evidence. For WASP-77AB and WASP-85AB, we combine our data with historical measurements to determine the binary orbits, showing them to be moderately eccentric and inclined to the line of sight (and hence planetary orbital axis). Combining our survey with the similar Friends of Hot Jupiters survey, we conclude that known hot Jupiter host stars show a deficit of high mass stellar companions compared to the field star population; however, this may be a result of the biases in detection and target selection by ground-based surveys.

We explore the application of machine learning based on mixture density neural networks (MDNs) to the interior characterization of low-mass exoplanets up to 25 Earth masses constrained by mass, radius, and fluid Love number k 2 . We create a dataset of 900 000 synthetic planets, consisting of an iron-rich core, a silicate mantle, a high-pressure ice shell, and a gaseous H/He envelope, to train a MDN using planetary mass and radius as inputs to the network. For this layered structure, we show that the MDN is able to infer the distribution of possible thicknesses of each planetary layer from mass and radius of the planet. This approach obviates the time-consuming task of calculating such distributions with a dedicated set of forward models for each individual planet. While gas-rich planets may be characterized by compositional gradients rather than distinct layers, the method presented here can be easily extended to any interior structure model. The fluid Love number k 2 bears constraints on the mass distribution in the planets' interior and will be measured for an increasing number of exoplanets in the future. Adding k 2 as an input to the MDN significantly decreases the degeneracy of the possible interior structures. In an open repository we provide the trained MDN to be used through a Python Notebook.

We report the discovery of OGLE-2016-BLG-1190Lb, which is likely to be the first Spitzer microlensing planet in the Galactic bulge/bar, an assignation that can be confirmed by two epochs of high-resolution imaging of the combined source-lens baseline object. The planet's mass, M p =13.4±0.9 M J , places it right at the deuteriumburning limit, i.e., the conventional boundary between "planets" and "brown dwarfs." Its existence raises the question of whether such objects are really "planets" (formed within the disks of their hosts) or "failed stars" (lowmass objects formed by gas fragmentation). This question may ultimately be addressed by comparing disk and bulge/bar planets, which is a goal of the Spitzer microlens program. The host is a G dwarf, M host =0.89±0.07 M e , and the planet has a semimajor axis a∼2.0 au. We use Kepler K2 Campaign 9 microlensing data to break the lens-mass degeneracy that generically impacts parallax solutions from Earth-Spitzer observations alone, which is the first successful application of this approach. The microlensing data, derived primarily from near-continuous, ultradense survey observations from OGLE, MOA, and three KMTNet telescopes, contain more orbital information than for any previous microlensing planet, but not quite enough to accurately specify the full orbit. However, these data do permit the first rigorous test of microlensing orbital-motion measurements, which are typically derived from data taken over <1% of an orbital period.

&lt;p&gt;We explore the application of machine-learning, based on mixture density neural networks (MDNs), to the interior characterization of low-mass exoplanets up to 25 Earth masses constrained by mass, radius, and fluid Love number k&lt;sub&gt;2&lt;/sub&gt;. MDNs are a special subset of neural networks, able to predict the parameters of a Gaussian mixture distribution instead of single output values, which enables them to learn and approximate probability distributions. With a dataset of 900,000 synthetic planets, consisting of an iron-rich core, a silicate mantle, a high-pressure ice shell, and a gaseous H/He envelope, we train an MDN using planetary mass and radius as inputs to the network. We show that the MDN is able to infer the distribution of possible thicknesses of each planetary layer from mass and radius of the planet. This approach obviates the time-consuming task of calculating such distributions with a dedicated set of forward models for each individual planet.&lt;/p&gt;&lt;p&gt;The fluid Love number k&lt;sub&gt;2&lt;/sub&gt; bears constraints on the mass distribution in the planets' interior and will be measured for an increasing number of exoplanets in the future. Adding k&lt;sub&gt;2&lt;/sub&gt; as an input to the MDN significantly decreases the degeneracy of possible interior structures.&lt;/p&gt;

&lt;p&gt;Liquid water is generally assumed to be the most important factor for the emergence of life, and so a major goal in exoplanet science is the search for planets with water oceans. On terrestrial planets, the silicate mantle is a large source of water, which can be outgassed into the atmosphere via volcanism. Outgassing is subject to a series of feedback processes between atmosphere and interior, which continually shape both atmospheric composition, pressure, and temperature, as well as interior dynamics. For example, water has a high solubility in surface lava, which can strongly limit its outgassing into the atmosphere even at low atmospheric pressures. In contrast, CO&lt;sub&gt;2&lt;/sub&gt; can be easily outgassed. This drives up the surface pressure and temperature, potentially preventing further water outgassing [1].&lt;/p&gt; &lt;p&gt;We present the results of an extensive parameter study, where we use a newly developed 1D numerical model to simulate the coupled evolution of the atmosphere and interior of terrestrial exoplanets up to 5 Earth masses around Sun-like stars, with internal structures ranging from Moon- to Mercury-like. The model accounts for the main mechanisms controlling the global-scale, long-term evolution of stagnant-lid rocky planets (i.e. bodies without plate tectonics), and it includes a large number of atmosphere-interior feedback processes, such as a CO&lt;sub&gt;2&lt;/sub&gt; weathering cycle, volcanic outgassing based on the pressure-dependent solubility of volatiles in surface lava, a water cycle between ocean and atmosphere, greenhouse heating, as well as the influence of a primordial H&lt;sub&gt;2&lt;/sub&gt; atmosphere, which can be lost through escape processes. While many atmosphere-interior feedback processes have been studied before in detail (e.g. [2, 3]), we present here a comprehensive model combining the important planetary processes across a wide range of terrestrial planets.&lt;/p&gt; &lt;p&gt;We find that a significant majority of high-density exoplanets (i.e. Mercury-like planets with large cores) are able to outgas and sustain water on their surface. In contrast, most planets with intermediate, Earth-like densities either transition into a runaway greenhouse regime due to strong CO&lt;sub&gt;2&lt;/sub&gt; outgassing, or retain part of their primordial atmosphere, which prevents water from being outgassed. This suggests that high-density planets could be the most promising targets when searching for suitable candidates for hosting liquid water. Furthermore, the degeneracy of the interior structures of high-density planets is limited compared to that of planets with Earth-like density, which further facilitates the characterization of these bodies, and our results predict largely uniform atmospheric compositions across the range of high-density planets, which could be verified by future spectroscopic measurements.&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;References:&lt;/p&gt; &lt;p&gt;[1] Tosi, N. &lt;em&gt;et al.&lt;/em&gt; The habitability of a stagnant-lid earth. &lt;em&gt;A&amp;A&lt;/em&gt; &lt;strong&gt;605&lt;/strong&gt;, A71 (2017).&lt;/p&gt; &lt;p&gt;[2] Noack, L., Rivoldini, A. &amp; Van Hoolst, T. Volcanism and outgassing of stagnant-lid planets: Implications for the habitable zone. &lt;em&gt;Physics of the Earth and Planetary Interiors&lt;/em&gt; &lt;strong&gt;269&lt;/strong&gt;, 40&amp;#8211;57 (2017).&lt;/p&gt; &lt;p&gt;[3] Foley, B. J. &amp; Smye, A. J. Carbon Cycling and Habitability of Earth-Sized Stagnant Lid Planets. &lt;em&gt;Astrobiology&lt;/em&gt; &lt;strong&gt;18&lt;/strong&gt;, 873&amp;#8211;896 (2018).&lt;/p&gt;