Description
The Super-Kamiokande experiment (SK) is the water Cherenkov detector which discovered the oscillation of atmospheric neutrinos. The dominant effect of the oscillation of muon neutrinos ($\nu_\mu$) is the appearance of tau neutrinos ($\nu_\tau$). Direct detection of $\nu_\tau$ in the atmospheric neutrino flux provides an unambiguous confirmation of neutrino oscillations. SK uses machine learning techniques of neural networks to segregate $\nu_\tau$ charged-current interactions from the interactions of the atmospheric muon and electron neutrinos ($\nu_e$), with which in 2018, it excluded the hypothesis of no $\nu_\tau$ appearance with a significance level of 4.6$\sigma$. The sub-dominant $\nu_\mu$ oscillation mode, which is the change of $\nu_\mu$ to $\nu_e$, is studied at SK to determine mass hierarchy. Currently, $\nu_\tau$ interactions form the biggest background to the mass hierarchy signal in the SK analysis. This poster will discuss improvements in the $\nu_\tau$ identification algorithm and discuss corresponding improvements in the search for tau neutrinos and the suppression of mass heirarchy backgrounds.
| Collaboration | Super-Kamiokande |
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