Solar Jet Hunter: a citizen science initiative to identify coronal jets in EUV data sets

TL;DR

This paper presents a citizen science project called Solar Jet Hunter that leverages volunteers to identify and catalog solar coronal jets in EUV data, creating a valuable dataset for scientific analysis and machine learning training.

Contribution

The study introduces a novel citizen science approach to accurately identify and catalog solar jets in EUV data, improving upon automated detection methods.

Findings

883 jets identified from 120,000 images

Only 21% of analyzed data contained jets

The resulting catalog is publicly available for research

Abstract

Context. Solar coronal jets seen in EUV are ubiquitous on the Sun, have been found in and at the edges of active regions, at the boundaries of coronal holes, and in the quiet Sun. Jets have various shapes, sizes, brightness, velocities and duration in time, which complicates their detection by automated algorithms. So far, solar jets reported in the Heliophysics Event Knowledgebase (HEK) have been mostly reported by humans looking for them in the data, with different levels of precision regarding their timing and positions. Aims. We create a catalogue of solar jets observed in EUV at 304 {\AA} containing precise and consistent information on the jet timing, position and extent. Methods. We designed a citizen science project, "Solar Jet Hunter", on the Zooniverse platform, to analyze EUV observations at 304 {\AA} from the Solar Dynamic Observatory/Atmospheric Imaging Assembly (SDO/AIA). We created movie strips for regions of the Sun in which jets have been reported in HEK and ask the volunteers to 1) confirm the presence of at least one jet in the data and 2) report the timing, position and extent of the jet. Results. We report here the design of the project and the results obtained after the analysis of data from 2011 to 2016. 365 "coronal jet" events from HEK served as input for the citizen science project, equivalent to more than 120,000 images distributed into 9,689 "movie strips". Classification by the citizen scientists resulted with only 21% of the data containing a jet, and 883 individual jets being identified. Conclusions. We demonstrate how citizen science can enhance the analysis of solar data with the example of Solar Jet Hunter. The catalogue of jets thus created is publicly available and will enable statistical studies of jets and related phenomena. This catalogue will also be used as a training set for machines to learn to recognize jets in further data sets.