نبذة مختصرة : International audience ; Muography is increasingly used to image the density distribution of volcanic edifices, complementing traditional geophysical tomographies. Here, we present a new muon data processing algorithm, and apply it to a new generation of scintillator-based muon detectors, to image the relative density distribution in La Soufrière de Guadeloupe volcano (Lesser Antilles, France). Our processing method iteratively searches for the best fit of each muon trajectory, accounting for all the hits registered by the detector related to the particular muon event. We test the performance of our algorithm numerically, simulating the interaction of muons with our detector and accounting for its exact assemblage including the scintillator bars and lead shielding. We find that our new data processing mitigates the impact of spurious signals coming from secondary particles, and improves the amount of successfully reconstructed events. The resulting 2-D muon images at La Soufrière have higher angular resolution than previous ones and capture the heterogeneous structure of the dome. They show density anomalies located on the summit southern region, which includes a boiling acid lake and degassing fractures, where the rock is the most porous and fumarolic activity is ongoing. This work shows the importance of combining numerical simulations of muon propagation with precise raw data processing to obtain high-quality results. It is also a first step towards fully assessing the noise contamination sources when performing muon tomography, and their correction, prior to geophysical interpretations.
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