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Efficient labelling of solar flux evolution videos by a deep learning model

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Abstract

Machine learning is becoming a critical tool for the interrogation of large, complex data. Labelling, defined as the process of adding meaningful annotations, is a crucial step of supervised machine learning. However, labelling datasets is time consuming. Here we show that convolutional neural networks (CNNs) trained on crudely labelled astronomical videos can be leveraged to improve the quality of data labelling and reduce the need for human intervention. We use videos of the solar magnetic field that are divided into two classes—emergence or non-emergence of bipolar magnetic regions (BMRs)—on the basis of their first detection on the solar disk. We train CNNs using crude labels, manually verify, correct disagreements between the labelling and CNN, and repeat this process until convergence is reached. Traditionally, flux emergence labelling is done manually. We find that a high-quality labelled dataset derived through this iterative process reduces the necessary manual verification by 50%. Furthermore, by gradually masking the videos and looking for maximum changes in CNN inference, we locate BMR emergence time without retraining the CNN. This demonstrates the versatility of CNNs for simplifying the challenging task of labelling complex dynamic events.

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Fig. 1: Dataset and model architecture.
Fig. 2: Data management used to build, train, validate and test different models for iterative relabelling, detection threshold estimation and performance evaluation.
Fig. 3: Sequence of iterative relabelling and convergence of performance.
Fig. 4: Model ensemble used to estimate the classification accuracy on the test set.
Fig. 5: Identifying the emergence epoch by frame stacking.
Fig. 6: Emergence epoch identification using an ensemble of models.
Fig. 7: Accuracy of emergence epoch identification.

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Data availability

The SoHO/MDI magnetograms, used to create the flux emergence videos for this study, are available from the Joint Science Operations Center (http://jsoc.stanford.edu/ajax/lookdata.html?ds=mdi.fd_M_96m_lev182). All the flux evolution videos with their emergence or non-emergence labels can be accessed through Harvard Dataverse at https://doi.org/10.7910/DVN/6F25MG. Source data are provided with this paper.

Code availability

The iterative relabelling algorithm has been explicitly depicted in the Methods. The code for data preparation and training the CNN can be accessed in the form of a python notebook via GitHub at https://github.com/subhamoysgit/flux_emergence/.

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Acknowledgements

This research was funded by NASA grant numbers 80NSSC19M0165 and 80NSSC18K0671.

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Author notes

  1. These authors contributed equally: Subhamoy Chatterjee, Andrés Muñoz-Jaramillo.

Authors and Affiliations

  1. Southwest Research Institute, Boulder, CO, USA

    Subhamoy Chatterjee, Andrés Muñoz-Jaramillo & Derek A. Lamb

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  1. Subhamoy Chatterjee
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  2. Andrés Muñoz-Jaramillo
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Contributions

S.C. and A.M.-J. planned the experiments and wrote the paper. S.C. set up and ran the experiments. A.M.-J. provided the list of events that was analysed. D.A.L. assembled the video sequences used in this work and helped edit the manuscript.

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Correspondence to Subhamoy Chatterjee.

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The authors declare no competing interests.

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Nature Astronomy thanks Andong Hu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Source data

Source Data Fig. 1

Raw data for all video frames with header information in FITS format.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data for all of the rows and columns.

Source Data Fig. 4

Statistical source data for creating the plots.

Source Data Fig. 5

Statistical source data for creating the plots.

Source Data Fig. 6

Statistical source data for colouring the video frames.

Source Data Fig. 7

Statistical source data for creating the plots.

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Chatterjee, S., Muñoz-Jaramillo, A. & Lamb, D.A. Efficient labelling of solar flux evolution videos by a deep learning model. Nat Astron 6, 796–803 (2022). https://doi.org/10.1038/s41550-022-01701-3

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