https://doi.org/10.1140/epjc/s10052-018-6455-z
Regular Article - Experimental Physics
Signal mixture estimation for degenerate heavy Higgses using a deep neural network
1
Department of Physics, University of Oslo, 0316, Oslo, Norway
2
Blackett Laboratory, Department of Physics, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK
3
Department of Physics and Technology, University of Bergen, 5020, Bergen, Norway
* e-mail: steffen.maeland@uib.no
** e-mail: inga.strumke@uib.no
Received:
9
May
2018
Accepted:
15
November
2018
Published online:
12
December
2018
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 
improvement in the estimate uncertainty.
© The Author(s), 2018
