Jupiter as an exoplanet: UV to NIR transmission spectrum reveals hazes, a Na layer and possibly stratospheric H2O-ice clouds

Currently, the analysis of transmission spectra is the most successful technique to probe the chemical composition of exoplanet atmospheres. But the accuracy of these measurements is constrained by observational limitations and the diversity of possible atmospheric compositions. Here we show the UV-VIS-IR transmission spectrum of Jupiter, as if it were a transiting exoplanet, obtained by observing one of its satellites, Ganymede, while passing through Jupiter's shadow i.e., during a solar eclipse from Ganymede. The spectrum shows strong extinction due to the presence of clouds (aerosols) and haze in the atmosphere, and strong absorption features from CH4. More interestingly, the comparison with radiative transfer models reveals a spectral signature, which we attribute here to a Jupiter stratospheric layer of crystalline H2O ice. The atomic transitions of Na are also present. These results are relevant for the modeling and interpretation of giant transiting exoplanets. They also open a new technique to explore the atmospheric composition of the upper layers of Jupiter's atmosphere.Comment: Accepted for publication in ApJ Letter

Photon emission as a source of coherent behaviour of polaritons

We show that the combined effect of photon emission and Coulomb interactions may drive an exciton-polariton system towards a dynamical coherent state, even without phonon thermalization or any other relaxation mechanism. Exact diagonalization results for a finite system (a multilevel quantum dot interacting with the lowest energy photon mode of a microcavity) are presented in support to this statement

Modeling and forecasting gender-based violence through machine learning techniques

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world