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• W3100814352 abstract "If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a $$sim 20%$$ improvement in the estimate uncertainty." @default.
• W3100814352 created "2020-11-23" @default.
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• W3100814352 date "2018-12-01" @default.
• W3100814352 modified "2023-09-26" @default.
• W3100814352 title "Signal mixture estimation for degenerate heavy Higgses using a deep neural network" @default.
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• W3100814352 doi "https://doi.org/10.1140/epjc/s10052-018-6455-z" @default.
• W3100814352 hasPublicationYear "2018" @default.
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