Probing the tunable multi-cone bandstructure in Bernal bilayer graphene

Controlling the bandstructure of Dirac materials is of wide interest in current research but has remained an outstanding challenge for systems such as monolayer graphene. In contrast, Bernal bilayer graphene (BLG) offers a highly flexible platform for tuning the bandstructure, featuring two distinct regimes. In one regime, which is well established and widely used, a tunable bandgap is induced by a large enough transverse displacement field. Another is a gapless metallic band occurring near charge neutrality and at not too strong fields, featuring rich 'fine structure' consisting of four linearly-dispersing Dirac cones with opposite chiralities in each valley and van Hove singularities. Even though BLG was extensively studied experimentally in the last two decades, the evidence of this exotic bandstructure is still elusive, likely due to insufficient energy resolution. Here, rather than probing the bandstructure by direct spectroscopy, we use Landau levels as markers of the energy dispersion and carefully analyze the Landau level spectrum in a regime where the cyclotron orbits of electrons or holes in momentum space are small enough to resolve the distinct mini Dirac cones. We identify the presence of four distinct Dirac cones and map out complex topological transitions induced by electric displacement field. These findings introduce a valuable addition to the toolkit for graphene electronics

Combining official and Google Trends data to forecast the Italian youth unemployment rate

The increased availability of online information in recent years has aroused interest in the possibility of deriving indications for many kinds of phenomena. In the more specific economic and statistical context, numerous studies suggest the use of online search data to improve the forecasting and nowcasting of official economic indicators with a view to increasing the promptness of their circulation. The purpose of this work is to investigate if the use of big data can improve the forecasting of the youth unemployment rate – estimated in Italy on a monthly basis by the Italian National Institute of Statistics – by means of time series models. The time series used are those of the Google Trends query share for the keyword offerte di lavoro (job offers) and the official labour force survey data for the Italian youth unemployment rate since 2004. Two different models are estimated: an ARIMA model using only the official youth unemployment rate series and a VAR model combining the former series with the Google Trends query share. The results show that the use of Google Trends information leads to an average decrease in the forecast error