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Every 11 years, the Sun goes through periods of activity, with the occurrenceof many solar flares and mass ejections, both energetic phenomena of magneticorigin. Due to its effects on Earth, the study of solar activity is ofparamount importance. POEMAS (Polarization of Millimeter Emission of SolarActivity) is a system of two telescopes, installed at CASLEO (El LeoncitoAstronomical Complex) in Argentina, which monitors the Sun at two millimeterwavelengths (corresponding frequencies of 45 and 90 GHz). The objective of thiswork is to automatically detect solar flares observed by the polarimeter. Firstit is necessary to eliminate the background noise, caused mainly byinstrumental problems, from the light curves of millimeter solar emission. Themethodology used to exclude the noise proposed in this work is to use thetendency of time series. The subtraction of this model from the light curvesprovides the input to automate the detection of solar flares using artificialintelligence techniques. A Neural Network was trained to recognize patterns andanalyze a dataset in order to identify solar flares. Previously, a total of 30flares had been visually identified and analyzed in the POEMAS database between2011/11/22 and 2013/12/10. The methodology presented here confirmed 87% ofthese events, moreover the neural network was able to identify at least 9 newevents. As the neural network was trained to detect impulsive events (lastingless than 5 min), long duration bursts were not automatically detected, norwere they detected visually due to the background noise of the telescope.Visual inspection of the POEMAS data, when comparing with microwave data fromthe RSTN, allowed the identification of an additional 10 long-duration solarflares at 45 GHz. We discuss some problems encountered and possible solutionsfor future work.

Automatic detection of solar flares observed at 45 GHz by the POEMAS telescope