activity
20182022
most citedA count-based imaging model for the Spectrometer/Telescope for Imaging X-rays (STIX) in Solar Orbiter

30 citations · 31 across the 5 of their papers we have counts for

collaborators

13 papers

astro-ph.SR20221 cited

Operational solar flare forecasting via video-based deep learning

Sabrina Guastavino, Francesco Marchetti, Federico Benvenuto +2

Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence. Thi…

cs.LG2021

Prediction of severe thunderstorm events with ensemble deep learning and radar data

Sabrina Guastavino, Michele Piana, Marco Tizzi +5

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelli…

astro-ph.SR2021

The Flare Likelihood and Region Eruption Forecasting (FLARECAST) Project: Flare forecasting in the big data & machine learning era

M. K. Georgoulis, D. S. Bloomfield, M. Piana +25

The EU funded the FLARECAST project, that ran from Jan 2015 until Feb 2018. FLARECAST had a R2O focus, and introduced several innovations into the discipline of solar flare forecas…

astro-ph.SR2021

Flare Forecasting Algorithms Based on High-Gradient Polarity Inversion Lines in Active Regions

Domenico Cicogna, Francesco Berrilli, Daniele Calchetti +6

Solar flares emanate from solar active regions hosting complex and strong bipolar magnetic fluxes. Estimating the probability of an active region to flare and defining reliable pre…

cs.LG2021

Bad and good errors: value-weighted skill scores in deep ensemble learning

Sabrina Guastavino, Michele Piana, Federico Benvenuto

In this paper we propose a novel approach to realize forecast verification. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidenc…

math.NA2020

Predictive risk estimation for the Expectation Maximization algorithm with Poisson data

Paolo Massa, Federico Benvenuto

In this work, we introduce a novel estimator of the predictive risk with Poisson data, when the loss function is the Kullback-Leibler divergence, in order to define a regularizatio…