Physics:Rapidity Mass Matrix
The Rapidity Mass Matrix (RMM) is a method [1] of transforming a list of particles for particle physics experiments into a 2D sparse Matrix for Machine learning algorithms.
The RMMs encapsulate information on single- and two-particle densities of identified particles and jets can lead to a systematic approach for defining input variables ("feature space") for various artificial neural networks (ANNs) used in particle physics, independent of event signatures. By construction, the RMMs are expected to be sensitive to a wide range of popular event signatures of the Physics:Standard Model, and thus can be used for various searches of new signatures. The diagonal elements of RMM represent transverse momenta of all objects, the upper-right elements are invariant masses of each two-particle combination, while the lower-left cells reflect rapidity differences.Event signatures with missing transverse energies and Lorentz factors are also included.
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- ↑ Chekanov, S.V. (2009). "Machine learning using rapidity-mass matrices for event classification problems in HEP". NIMA 931: 92. doi:10.1016/j.nima.2019.04.031.
