Upgrades of the ESPERTA forecast tool for solar proton events

Abstract

The Empirical model for Solar Proton Events Real Time Alert (ESPERTA) exploits three solar parameters (flare longitude, soft X-ray fluence, and radio fluence) to provide a timely prediction for the occurrence of solar proton events (SPEs, i.e., when the >10MeV proton flux is ≥10 pfu) after the emission of a ≥M2 flare. In addition, it makes a prediction for the most dangerous SPEs for which the >10 MeV proton flux is ≥100 pfu. In this paper, we study two different ways to upgrade the ESPERTA model and implement it in real time: 1) by using ground based observations from the LOFAR stations; 2) by applying a novel machine learning algorithm to flare-based parameters to provide early warnings of SPE occurrence together with a fine-tuned radiation storm level. As a last step, we perform a preliminary study using a neural network to forecast the proton flux 1-hour ahead to complement the ESPERTA tool. We evaluate the models over flare and SPE data covering the last two solar cycles and discuss their performance, limits, and advantages.

Keywords

• solar energetic particles
• machine learning