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Filtering by: Creator Manchester, Ward IV Remove constraint Creator: Manchester, Ward IV Keyword solar flare prediction Remove constraint Keyword: solar flare prediction Discipline Engineering Remove constraint Discipline: Engineering

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  • Data and Data products for machine learning applied to solar flares

    work
    Creator:
    Chen, Yang and Manchester, Ward IV
    Description:
    GOES_flare_list: contains a list of more than 10,000 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time, GOES_B_flare_list: contains time series data of SDO/HMI SHARP parameters for B class solar flares , GOES_MX_flare_list: contains time series data of SDO/HMI SHARP parameters for M and X class solar flares, SHARP_B_flare_data_300.hdf5 and SHARP_MX_flare_data_300.hdf5 files contain time series more than 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model., and B_HARPs_CNNencoded_part_xxx.hdf5 and M_X HARPs_CNNencoded_part_xxx.hdf5 include neural network encoded features derived from vector magnetogram images derived from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI). These data files typically contains one or two sequences of magnetograms covering an active region for a period of 24h with a 1 hour cadence. We encode each magnetogram with frames of a fixed size of 8x16 with 512 channels.
    Keyword:
    machine learning, data science, and solar flare prediction
    Citation to related publication:
    Chen, Y., Manchester, W., Hero, A., Toth, G., DuFumier, B. Zhou, T., Wang, X., Zhu, H., Sun, Zeyu, Gombosi, T., Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters, Space Weather, 17, 1404–1426.  https://doi.org/10.1029/2019SW002214 and Jiao, Z., Chen, Y., Manchester, W. (2020). Data for Solar Flare Intensity Prediction with Machine Learning Models [Data set]. University of Michigan - Deep Blue.  https://doi.org/10.7302/b07j-bj08
    Discipline:
    Engineering and Science