GLOBAL-SCALE ENERGY FLOW THROUGHOUT EARTH S MAGNETOSPHERE IS CATALYZED BY PROCESSES THAT OCCUR IN THE ELECTRON DIFFUSION REGION (EDR) OF MAGNETIC RECONNECTION1. LARGE-SCALE FEATURES OCCURRING IN THE ION DIFFUSION REGION HAVE BEEN STUDIED EXTENSIVELY AT THE MAGNETOPAUSE (MP) AND MAGNETOTAIL BUT ONLY RARE FORTUITOUS CIRCUMSTANCES HAVE PERMITTED A GLIMPSE OF THE ELECTRON DYNAMICS THAT BREAK MAGNETIC FIELD LINES AND ENERGIZE PLASMA2 4. THIS PROJECT FACILITATES EDR DISCOVERY BY USING MACHINE LEARNING TO IDENTIFY AND CLASSIFY RECONNECTING CURRENT SHEETS. THE MAGNETOSPHERIC MULTISCALE (MMS) MISSION WAS DESIGNED TO HAVE THE SPATIAL AND TEMPORAL RESOLUTION REQUIRED TO RESOLVE ELECTRON-SCALE PHYSICS ASSOCIATED WITH RECONNECTION5. UNFORTUNATELY BECAUSE OF TELEMETRY RESTRICTIONS ONLY A SMALL FRACTION OF THE HIGH TIME-RESOLUTION DATA IS TRANSMITTED TO THE GROUND. TO RESOLVE THIS ISSUE MMS EMPLOYS AUTOMATED BURST TRIGGERS ONBOARD THE SPACECRAFT AND A SCIENTIST-IN-THE-LOOP (SITL) ON THE GROUND TO SELECT INTERVALS LIKELY TO CONTAIN DIFFUSION REGIONS6 7. ONLY LOW-RESOLUTION SURVEY DATA IS AVAILABLE TO THE SITL WHICH IS INSUFFICIENT TO RESOLVE ELECTRON DYNAMICS. A STRATEGY FOR THE SITL THEN IS TO SELECT ALL MP CROSSINGS6. THIS HAS RESULTED IN>32 POTENTIAL MP EDR ENCOUNTERS8 BUT IS LABOR- AND RESOURCE-INTENSIVE; JUST ~0.7% OF MP CROSSINGS OR ~0.0001% OF THE MISSION LIFETIME DURING ITS FIRST TWO YEARS CONTAINED AN EDR. OUR WORK WILL REFOCUS MMS OPERATIONS COSTS TO RESEARCH BY TRANSFERRING KEY SITL PROCESSES INTO AN AUTOMATED GROUND-LOOP. THE WORK PLAN (SECTION 2) AIMS FOR PROGRESSIVE AUTONOMY9 IN MMS SITL OPERATIONS BY USING HIERARCHICAL BAYESIAN MODELS NEURAL NETWORKS AND HIDDEN MARKOV MODELS TO 1) IMPROVE AN EXISTING MP DETECTOR 2) BUILD A BOW SHOCK DETECTOR AND 3) IDENTIFY AND CLASSIFY RECONNECTING CURRENT SHEETS. TRAINING AND TEST DATASETS ARE EXTRACTED FROM THE MMS MISSION EVENTS ARCHIVE. THE RESULTING MODELS ARE INSTALLED AND RAN AT THE MMS SCIENCE DATA CENTER FROM WHERE THE AUTOMATED SELECTIONS CAN BE ACCESSED BY THE SITL DURING EVENT SELECTION. THIS PROJECT REPRESENTS A KEY ADVANCEMENT MISSION COMPLEXITY BY 1) FACILITATING LARGER DATA RATES AND MORE SPACECRAFT THROUGH CONSOLIDATION OF EVENT SELECTION PROCESSES INTO A NEAR REAL-TIME EXPANDABLE AND ADAPTABLE MACHINE LEARNING FRAMEWORK AND 2) ACCURATELY IDENTIFYING AND CLASSIFYING EVENTS ASSOCIATED WITH PRIME SCIENCE OBJECTIVES.