A new catalogue of solar flare events from soft x-ray GOES signal in the period 1986-2020

TL;DR

This paper introduces a new, efficient algorithm for detecting solar flares from GOES satellite data, resulting in a comprehensive catalogue that surpasses the official list in event count and detail, aiding solar physics research.

Contribution

The paper presents a novel detection algorithm that increases the number of identified solar flares and provides detailed physical parameters, enhancing existing flare catalogues.

Findings

Increased number of detected solar flares compared to official GOES list.

Detailed physical parameters for each flare, including energy and timing.

Preliminary analysis shows consistency with official data from 1998 to 2020.

Abstract

Solar flares, along with other sun-originated events such as Coronal Mass Ejections, fast solar wind streams, and solar energetic particles are among the most relevant events in Space Weather. Moreover, solar flares are the most energetic processes that occur in our solar system. The in-depth study of their occurrence statistics, both over extended periods or during individual solar cycles, allows us to improve and constrain the basic physical models of their origin. Increasing the number of detected events, especially those of lower intensity, and the number of physical parameters that describe the detected flares is, therefore, a mandatory goal. In this paper, we present a computationally efficient algorithm for the detection of solar flares in the soft-X solar flux provided by the GOES (NASA/NOAA) satellite constellation. Our code produces a new flare catalogue increasing the number of events with respect to the official GOES list. In addition to increasing the number of identified events, the catalogue contains information such as: an estimate of the total energy released, start and end time of the event, possible overlap with other events, background level of the GOES X-ray emission close to the revealed event. After a detailed description of the detection algorithm, we carry out a preliminary analysis of the flares reported in our catalogue and compare our results with the official list of GOES for the period from 1998 to 2020.