Reconstructing magnetotail reconnection events using data mining is feasible and repeatable (Dataset)

Dataset accompanying manuscript titled "Reconstructing magnetotail reconnection events using data mining is feasible and repeatable"

Abstract:

Recently, Stephens et al. (2023) utilized a data mining (DM) algorithm, applied to 26 years of magnetospheric magnetometer observations coupled with a flexible formulation of the magnetospheric magnetic field, to reconstruct the global configuration of the magnetotail when the Magnetospheric MultiScale (MMS) mission observed tail reconnection in situ. Of the 26 DM-reconstructed MMS reconnection events, 16 had a Bz=0 isocontour within ≈2 Earth radii (RE) of the observed reconnection location. Another eight had a Bz minimum region, identified using Bz=2 nT isocontours, within ≈2 RE. This consistency suggests that the structure of tail reconnection is correlated with the substorm/storm state of the magnetosphere, as reflected by geomagnetic indices and solar wind conditions. We verify these results using new validation methods and by comparing in-sample (including event data) to out-of-sample (excluding event data) reconstructions. We first benchmark the architecture of the reconstructed magnetic field using 100 randomly generated magnetic fields containing tail X- and O-lines, resolving 77 of them with three false positives. Next, we quantify the consistency of the reconstructions in resolving the reconnection location using a skill score relative to random chance. 88% of the in-sample and 75% of the out-of-sample scores are positive, confirming that the reconstructions resolve the location of tail reconnection better than random chance. Last, a bootstrapping analysis, which refits the model architecture to 100 random resamples of data, shows standard deviations in Bz of ⪅1 nT, indicating that the DM approach is not overly sensitive to the particular sampling of magnetometer records.