Magnetic reconnection at the magnetopause (MP) energises ambient plasma via the< release of magnetic energy and produces an “open” magnetosphere allowing solar wind particles to directly enter the system. At Saturn, the nature of MP reconnection remains unclear. The current study examines electron bulk heating at MP crossings, in order to probe the relationship between observed and predicted reconnection heating proposed by Phan et al. (2013) under open and closed MP, and how this may pertain to the position of the crossings in the Δβ-magnetic shear parameter space. The electron heating for 70 MP crossings made by the Cassini spacecraft from April 2005 to July 2007 was found using 1d and 3d moment methods. Minimum variance analysis was used on the magnetic field data to help indicate whether the MP is open or closed. We found better agreement between observed and predicted heating for events suggestive of locally ‘open’ MP. For events suggestive of locally ‘closed’ MP, we observed a cluster of points consistent with no electron heating, but also numerous cases with significant heating. Examining the events in the Δβ-magnetic shear parameter space, we find 83% of events without evidence of energisation were situated in the ‘reconnection suppressed’ regime, whilst between 43% to 68% of events with energisation lie in the ‘reconnection possible’ regime depending on the threshold used. The discrepancies could be explained by a combination of spatial and temporal variability which makes it possible to observe heated electrons with different conditions from the putative reconnection site.

Statistical studies of the properties of different plasma regions, such as the magnetosheath and outer magnetosphere found near the boundaries of planetary magnetospheres, require knowledge of boundary (bow shock and magnetopause) crossings for purposes of classification. These are commonly detected by visual inspection of the magnetic field and / or particle data sampled by the relevant spacecraft. Automation of this type of activity would thus improve the efficiency of boundary and region studies, which benefit from large crossing datasets, and could also have implications for future development of onboard data-processing protocols in the pre-downlink stage. The Cassini mission at Saturn (2004-2017) provided an invaluable dataset for testing the viability of automated boundary classification. The training dataset consists of BS and MP crossings for the time period 2004 to 2016 (Jackman et al. (2019)). We have employed a series of techniques which involve pre-processing the calibrated magnetometer data, unsupervised training of a LSTM recurrent neural network on magnetometer data to filter magnetosheath regions where crossings are most likely to be found, isolating large rotations in magnetic field using minimum variance analysis (MVA), feature engineering such as magnetic field strength ratio either side of the field rotation to form a ‘feature vector’ for each candidate, and finally applying a gradient-boosting decision-tree-based algorithm to predict the probability that a given interval of data contains the signature of a bow shock (BS), a magnetopause (MP), or None (not a boundary crossing). The resulting model performs better on bow shock events, with a precision (fraction of true events in the retrieved sample) and recall (fraction of the total true events which were retrieved) of ~86% and ~90% respectively, as compared to ~50% and ~68% for the MP. The ongoing work focuses on augmenting the feature space for improved classification of MP, based on a magnetic pressure model of MP crossings derived using a local pressure balance condition (e.g. Pilkington et al. 2015) and using the distinct energetic particle flux changes across the MP in MIMI data (e.g. Liou et al. 2021). We expect that these promising new features will help us to better constrain the retrieval of candidate events which are true MP crossings.