No Arabic abstract
As a consequence of the large (and growing) number of near-Earth objects discovered, some of them are lost before their orbit can be firmly established to ensure long-term recovery. A fraction of these present non-negligible chances of impact with the Earth. We present a method of targeted observations that allowed us to eliminate that risk by obtaining deep images of the area where the object would be, should it be on a collision orbit. 2006 QV89 was one of these objects, with a chance of impact with the Earth on 2019 September 9. Its position uncertainty (of the order of 1 degree) and faintness (below V$sim$24) made it a difficult candidate for a traditional direct recovery. However, the position of the virtual impactors could be determined with excellent accuracy. In July 2019, the virtual impactors of 2006 QV89 were particularly well placed, with a very small uncertainty region, and an expected magnitude of V$<$26. The area was imaged using ESOs Very Large Telescope, in the context of the ESA/ESO collaboration on Near-Earth Objects, resulting in very constraining a non-detection. This resulted in the elimination of the virtual impactor, even without effectively recovering 2006 QV89, indicating that it did not represent a threat. This method of deep non-detection of virtual impactors demonstrated a large potential to eliminate the threat of other-wise difficult to recover near-Earth objects
The formation of Uranus regular moons has been suggested to be linked to the origin of its enormous spin axial tilt (~98o). A giant impact between proto-Uranus and a 2-3 M_Earth impactor could lead to a large tilt and to the formation of a debris disc, where prograde and circular satellites are accreted. The most intriguing features of the current regular Uranian satellite system is that it possesses a positive trend in the mass-distance distribution and likely also in the bulk density, implying that viscous spreading of the debris disc after the giant impact plays a crucial role in shaping the architecture of the final system. In this paper, we investigate the formation of Uranus satellites by combining results of SPH simulations for the giant impact, a 1D semi-analytic disc model for viscous spreading of the post-impact debris disc, and N-body simulations for the assembly of satellites from a disc of moonlets. Assuming the condensed rock (i.e., silicate) remains small and available to stick onto the relatively rapid growing condensed water-ice, we find that the best case in reproducing the observed mass and bulk composition of Uranus satellite system is a pure-rocky impactor with 3 M_Earth colliding with the young Uranus with an impact parameter b = 0.75. Such an oblique collision could also naturally explain Uranus large tilt and possibly, its low internal heat flux. The giant impact scenario can naturally explain the key features of Uranus and its regular moons. We therefore suggest that the Uranian satellite system formed as a result of an impact rather than from a circumplanetary disc. Objects beyond the water snow-line could be dominated by rocky objects similar to Pluto and Triton. Future missions to Uranus and its satellite system would further constrain the properties of Uranus and its moons and provide further insight on their formation processes.
A profound shift in the study of cosmology came with the discovery of thousands of exoplanets and the possibility of the existence of billions of them in our Galaxy. The biggest goal in these searches is whether there are other life-harbouring planets. However, the question which of these detected planets are habitable, potentially-habitable, or maybe even inhabited, is still not answered. Some potentially habitable exoplanets have been hypothesized, but since Earth is the only known habitable planet, measures of habitability are necessarily determined with Earth as the reference. Several recent works introduced new habitability metrics based on optimization methods. Classification of potentially habitable exoplanets using supervised learning is another emerging area of study. However, both modeling and supervised learning approaches suffer from drawbacks. We propose an anomaly detection method, the Multi-Stage Memetic Algorithm (MSMA), to detect anomalies and extend it to an unsupervised clustering algorithm MSMVMCA to use it to detect potentially habitable exoplanets as anomalies. The algorithm is based on the postulate that Earth is an anomaly, with the possibility of existence of few other anomalies among thousands of data points. We describe an MSMA-based clustering approach with a novel distance function to detect habitable candidates as anomalies (including Earth). The results are cross-matched with the habitable exoplanet catalog (PHL-HEC) of the Planetary Habitability Laboratory (PHL) with both optimistic and conservative lists of potentially habitable exoplanets.
Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets. One technique to detect these distant worlds is through the direct detection of their thermal emission. The so-called direct imaging technique, is suitable for observing young planets far from their star. These are very low signal-to-noise-ratio (SNR) measurements and limited ground truth hinders the use of supervised learning approaches. In this paper, we combine deep generative and discriminative models to bypass the issues arising when directly training on real data. We use a Generative Adversarial Network to obtain a suitable dataset for training Convolutional Neural Network classifiers to detect and locate planets across a wide range of SNRs. Tested on artificial data, our detectors exhibit good predictive performance and robustness across SNRs. To demonstrate the limits of the detectors, we provide maps of the precision and recall of the model per pixel of the input image. On real data, the models can re-confirm bright source detections.
We present high-precision linear polarization observations of four bright hot Jupiter systems ($tau$ Boo, HD 179949, HD 189733 and 51 Peg) and use the data to search for polarized reflected light from the planets. The data for 51 Peg are consistent with a reflected light polarization signal at about the level expected with 2.8$sigma$ significance and a false alarm probability of 1.9 per cent. More data will be needed to confirm a detection of reflected light in this system. HD 189733 shows highly variable polarization that appears to be most likely the result of magnetic activity of the host star. This masks any polarization due to reflected light, but a polarization signal at the expected level of $sim$20 ppm cannot be ruled out. $tau$ Boo and HD 179949 show no evidence for polarization due to reflected light. The results are consistent with the idea that many hot Jupiters have low geometric albedos. Conclusive detection of polarized reflected light from hot Jupiters is likely to require further improvements in instrument sensitivity.
Deep learning techniques have been well explored in the transiting exoplanet field, however previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well-proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields 94% precision and 95% recall for transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). Giving a slightly lower confidence threshold, recall can reach higher than 97%, which makes our model applicable for large-scale search. We also transfer the trained model to the TESS data and obtain similar performance. The results of our algorithm match the intuition of the human visual perception and make it easy to find single transiting candidates. Moreover, the parameters of the output bounding boxes can also help to find multiplanet systems. Our network and detection functions are implemented in the Deep-Transit toolkit, which is an open-source Python package hosted on GitHub and PyPI.