Abstract

The recent developments in space instrumentation for solar observations and telemetry have caused the necessity of advanced pattern recognition tools for the different classes of solar events. The Extreme ultraviolet Imaging Telescope (EIT) of solar corona on-board SOHO spacecraft has uncovered a new class of eruptive events which are often identified as signatures of Coronal Mass Ejection (CME) initiations on solar disk. It is evident that a crucial task is the development of an automatic detection tool of CMEs precursors. The Novel EIT wave Machine Observing (NEMO) (http://sidc.be/nemo) code is an operational tool that detects automatically solar eruptions using EIT image sequences. NEMO applies techniques based on the general statistical properties of the underlying physical mechanisms of eruptive events on the solar disc. In this work, the most recent updates of NEMO code - that have resulted to the increase of the recognition efficiency of solar eruptions linked to CMEs - are presented. These updates provide calculations of the surface of the dimming region, implement novel clustering technique for the dimmings and set new criteria to flag the eruptive dimmings based on their complex characteristics. The efficiency of NEMO has been increased significantly resulting to the extraction of dimmings observed near the solar limb and to the detection of small-scale events as well. As a consequence, the detection efficiency of CMEs precursors and the forecasts of CMEs have been drastically improved. Furthermore, the catalogues of solar eruptive events that can be constructed by NEMO may include larger number of physical parameters associated to the dimming regions.

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