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Missing information search with deep learning for mass estimation
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DOI：10.1103/PhysRevResearch.5.043186.png)
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Abstract
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We introduce DeeLeMa, a deep learning-based network for the analysis of energy and momentum in highenergy particle collisions. This novel approach is specifically designed to address the challenge of analyzing collision events with multiple invisible particles, which are prevalent in many high-energy physics experiments. DeeLeMa is constructed based on the kinematic constraints and symmetry of the event topologies. We show that DeeLeMa can robustly estimate mass distribution even in the presence of combinatorial uncertainties and detector smearing effects. The approach is flexible and can be applied to various event topologies by leveraging the relevant kinematic symmetries. This work opens up exciting opportunities for the analysis of high-energy particle collision data, and we believe that DeeLeMa has the potential to become a valuable tool for the highenergy physics community.
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OAAIShareSaveShareSaveMeasuring masses of semi-invisibly decaying particle pairs produced at hadron colliders.png)
PHYSICS LETTERS BOAAI
PHYSICS LETTERS BIF4.5
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