Testing machine learning algorithms for the prediction of depositional fluxes of the radionuclides 7Be, 210Pb and 40K.

The monthly depositional fluxes of 7Be, 210Pb and 40K were measured at Malaga, (Southern Spain) from 2005 to 2018. In this work, the depositional fluxes of these radionuclides are investigated and their relations with several atmospheric variables have been studied by applying two popular machine learning methods: Random Forest and Neural Network algorithms. We extensively test different configurations of these algorithms and demonstrate their predictive ability for reproducing depositional fluxes. The models derived with Neural Networks achieve slightly better results, in average, although similar, having into account the uncertainties. The mean Pearson-R coefficients, evaluated with a k-fold cross-validation method, are around 0.85 for the three radionuclides using Neural Network models, while they go down to 0.83, 0.79 and 0.8 for 7Be, 210Pb and 40K, respectively, for the Random Forest models. Additionally, applying the Recursive Feature Elimination technique we determine the variables more correlated with the depositional fluxes of these radionuclides, which elucidates the main dependences of their temporal variability.

Unresolved Gamma-Ray Sky through its Angular Power Spectrum.

The gamma-ray sky has been observed with unprecedented accuracy in the last decade by the Fermi -large area telescope (LAT), allowing us to resolve and understand the high-energy Universe. The nature of the remaining unresolved emission [unresolved gamma-ray background (UGRB)] below the LAT source detection threshold can be uncovered by characterizing the amplitude and angular scale of the UGRB fluctuation field. This Letter presents a measurement of the UGRB autocorrelation angular power spectrum based on eight years of Fermi-LAT Pass 8 data products. The analysis is designed to be robust against contamination from resolved sources and noise systematics. The sensitivity to subthreshold sources is greatly enhanced with respect to previous measurements. We find evidence (with ∼3.7σ significance) that the scenario in which two classes of sources contribute to the UGRB signal is favored over a single class. A double power law with exponential cutoff can explain the anisotropy energy spectrum well, with photon indices of the two populations being 2.55±0.23 and 1.86±0.15.