Using Multiple Instance Learning for Explainable Solar Flare Prediction
arXiv:2203.13896 · doi:10.1016/j.ascom.2022.100668
Abstract
In this work we leverage a weakly-labeled dataset of spectral data from NASAs IRIS satellite for the prediction of solar flares using the Multiple Instance Learning (MIL) paradigm. While standard supervised learning models expect a label for every instance, MIL relaxes this and only considers bags of instances to be labeled. This is ideally suited for flare prediction with IRIS data that consists of time series of bags of UV spectra measured along the instrument slit. In particular, we consider the readout window around the Mg II h&k lines that encodes information on the dynamics of the solar chromosphere. Our MIL models are not only able to predict whether flares occur within the next 25 minutes with accuracies of around 90%, but are also able to explain which spectral profiles were particularly important for their bag-level prediction. This information can be used to highlight regions of interest in ongoing IRIS observations in real-time and to identify candidates for typical flare precursor spectral profiles. We use k-means clustering to extract groups of spectral profiles that appear relevant for flare prediction. The recovered groups show high intensity, triplet red wing emission and single-peaked h and k lines, as found by previous works. They seem to be related to small-scale explosive events that have been reported to occur tens of minutes before a flare.
References in corpus (32)
- ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
- The Interface Region Imaging Spectrograph (IRIS)
- Multiple Instance Learning: A Survey of Problem Characteristics and Applications
- Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
- Hot Explosions in the Cool Atmosphere of the Sun
- The formation of IRIS diagnostics II. The formation of the Mg II h&k lines in the solar atmosphere
- Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
- Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
- The formation of IRIS diagnostics I. A quintessential model atom of Mg II and general formation properties of the Mg II h&k lines
- Predicting Solar Flares Using a Long Short-Term Memory Network
- The formation of IRIS diagnostics. III. Near-ultraviolet Spectra and Images
- Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Product and Random Forest Algorithm
- Ellerman bombs at high resolution III. Simultaneous observations with IRIS and SST
- Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
- The Formation of IRIS diagnostics. IV. The Mg II triplet lines as a new diagnostic for lower chromospheric heating
- Flare Prediction Using Photospheric and Coronal Image Data
- A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
- Identifying typical Mg II flare spectra using machine learning
- Recovering Thermodynamics from Spectral Profiles observed by IRIS: A Machine and Deep Learning Approach
- How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events
- Asymmetries of the Stokes V profiles observed by HINODE SOT/SP in the quiet Sun
- IRIS Observations of the Mg II h & k Lines During a Solar Flare
- Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression
- A parameter study for modeling MgII h and k emission during solar flares
- Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics
- Modeling Mg II h, k and Triplet Lines at Solar Flare Ribbons
- Real-time flare prediction based on distinctions between flaring and non-flaring active region spectra
- Spectropolarimetric observations of the Ca II 8498 A and 8542 A lines in the quiet Sun
- Spectropolarimetric inversions of the Ca II 8498 A and 8542 A lines in the quiet Sun
- Exploring mutual information between IRIS spectral lines. I. Correlations between spectral lines during solar flares and within the quiet Sun
- Molecular absorption in transition region spectral lines
- Exploring mutual information between IRIS spectral lines. II. Calculating the most probable response in all spectral windows