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We briefly review the various proposed scenarios that may lead to nonthermal radio emissions from exoplanetary systems (planetary magnetospheres, magnetosphere-ionosphere and magnetosphere-satellite coupling, and star-planet interactions), and the physical information that can be drawn from their detection. The latter scenario is especially favorable to the production of radio emission above 70,MHz. We summarize the results of past and recent radio searches, and then discuss FAST characteristics and observation strategy, including synergies. We emphasize the importance of polarization measurements and a high duty-cycle for the very weak targets that radio-exoplanets prove to be.
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Vortex Fiber Nulling (VFN) is an interferometric method for suppressing starlight to detect and spectroscopically characterize exoplanets. It relies on a vortex phase mask and single-mode fiber to reject starlight while simultaneously coupling up to 20% of the planet light at separations of $lesssim1lambda/D$, thereby enabling spectroscopic characterization of a large population of RV and transit-detected planets, among others, that are inaccessible to conventional coronagraphs. VFN has been demonstrated in the lab at visible wavelengths and here we present the latest results of these experiments. This includes polychromatic nulls of $5times10^{-4}$ in 10% bandwidth light centered around 790 nm. An upgraded testbed has been designed and is being built in the lab now; we also present a status update on that work here. Finally, we present preliminary K-band (2 $mu$m) fiber nulling results with the infrared mask that will be used on-sky as part of a VFN mode for the Keck Planet Imager and Characterizer Instrument in 2021.
LOUPE, the Lunar Observatory for Unresolved Polarimetry of the Earth, is a small, robust spectro-polarimeter with a mission to observe the Earth as an exoplanet. Detecting Earth-like planets in stellar habitable zones is one of the key challenges of modern exoplanetary science. Characterising such planets and searching for traces of life requires the direct detection of their signals. LOUPE provides unique spectral flux and polarisation data of sunlight reflected by the Earth, the only planet known to harbor life. This data will be used to test numerical codes to predict signals of Earth-like exoplanets, to test algorithms that retrieve planet properties, and to fine-tune the design and observational strategies of future space observatories. From the Moon, LOUPE will continuously see the entire Earth, enabling it to monitor the signal changes due to the planets daily rotation, weather patterns, and seasons, across all phase angles. Here, we present both the science case and the technology behind LOUPEs instrumental and mission design.
Exoplanets, short for `extra solar planets, are planets outside our solar system. They are objects with masses less than around 15 Jupiter-masses that orbit stars other than the Sun. They are small enough so they can not burn deuterium in their cores, yet large enough that they are not so called `dwarf planets like Pluto. To discover life elsewhere in the universe, particularly outside our own solar system, a good starting point would be to search for planets orbiting nearby Sun-like stars, since the only example of life we know of thrives on a planet we call Earth that orbits a G-type dwarf star. Furthermore, understanding the population of exoplanetary systems in the nearby solar neighbourhood allows us to understand the mechanisms that built our own solar system and gave rise to the conditions necessary for our tree of life to flourish. Signal processing is an integral part of exoplanet detection. From improving the signal-to-noise ratio of the observed data to applying advanced statistical signal processing methods, among others, to detect signals (potential planets) in the data, astronomers have tended, and continue to tend, towards signal processing in their quest of finding Earth-like planets. The following methods have been used to detect exoplanets.
In the preparation for ESAs Euclid mission and the large amount of data it will produce, we train deep convolutional neural networks on Euclid simulations classify solar system objects from other astronomical sources. Using transfer learning we are able to achieve a good performance despite our tiny dataset with as few as 7512 images. Our best model correctly identifies objects with a top accuracy of 94% and improves to 96% when Euclids dither information is included. The neural network misses ~50% of the slowest moving asteroids (v < 10 arcsec/h) but is otherwise able to correctly classify asteroids even down to 26 mag. We show that the same model also performs well at classifying stars, galaxies and cosmic rays, and could potentially be applied to distinguish all types of objects in the Euclid data and other large optical surveys.
One of the primary goals of exoplanet science is to find and characterize habitable planets, and direct imaging will play a key role in this effort. Though imaging a true Earth analog is likely out of reach from the ground, the coming generation of giant telescopes will find and characterize many planets in and near the habitable zones (HZs) of nearby stars. Radial velocity and transit searches indicate that such planets are common, but imaging them will require achieving extreme contrasts at very small angular separations, posing many challenges for adaptive optics (AO) system design. Giant planets in the HZ may even be within reach with the latest generation of high-contrast imagers for a handful of very nearby stars. Here we will review the definition of the HZ, and the characteristics of detectable planets there. We then review some of the ways that direct imaging in the HZ will be different from the typical exoplanet imaging survey today. Finally, we present preliminary results from our observations of the HZ of {alpha} Centauri A with the Magellan AO systems VisAO and Clio2 cameras.
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