Signal mixture estimation for degenerate heavy Higgses using a deep neural network
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 challen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2018-12-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | http://link.springer.com/article/10.1140/epjc/s10052-018-6455-z |
| _version_ | 1818179719989821440 |
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| author | Anders Kvellestad Steffen Maeland Inga Strümke |
| author_facet | Anders Kvellestad Steffen Maeland Inga Strümke |
| author_sort | Anders Kvellestad |
| collection | DOAJ |
| description | 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 $$\sim 20\%$$ ∼20% improvement in the estimate uncertainty. |
| first_indexed | 2024-12-11T21:08:21Z |
| format | Article |
| id | doaj.art-fba7f05f56cd4c72847797adf1fa7141 |
| institution | Directory Open Access Journal |
| issn | 1434-6044 1434-6052 |
| language | English |
| last_indexed | 2024-12-11T21:08:21Z |
| publishDate | 2018-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj.art-fba7f05f56cd4c72847797adf1fa71412022-12-22T00:50:48ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60441434-60522018-12-01781211110.1140/epjc/s10052-018-6455-zSignal mixture estimation for degenerate heavy Higgses using a deep neural networkAnders Kvellestad0Steffen Maeland1Inga Strümke2Department of Physics, University of OsloDepartment of Physics and Technology, University of BergenDepartment of Physics and Technology, University of BergenAbstract 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 $$\sim 20\%$$ ∼20% improvement in the estimate uncertainty.http://link.springer.com/article/10.1140/epjc/s10052-018-6455-z |
| spellingShingle | Anders Kvellestad Steffen Maeland Inga Strümke Signal mixture estimation for degenerate heavy Higgses using a deep neural network European Physical Journal C: Particles and Fields |
| title | Signal mixture estimation for degenerate heavy Higgses using a deep neural network |
| title_full | Signal mixture estimation for degenerate heavy Higgses using a deep neural network |
| title_fullStr | Signal mixture estimation for degenerate heavy Higgses using a deep neural network |
| title_full_unstemmed | Signal mixture estimation for degenerate heavy Higgses using a deep neural network |
| title_short | Signal mixture estimation for degenerate heavy Higgses using a deep neural network |
| title_sort | signal mixture estimation for degenerate heavy higgses using a deep neural network |
| url | http://link.springer.com/article/10.1140/epjc/s10052-018-6455-z |
| work_keys_str_mv | AT anderskvellestad signalmixtureestimationfordegenerateheavyhiggsesusingadeepneuralnetwork AT steffenmaeland signalmixtureestimationfordegenerateheavyhiggsesusingadeepneuralnetwork AT ingastrumke signalmixtureestimationfordegenerateheavyhiggsesusingadeepneuralnetwork |