Bayesian neutrino oscillation analysis with 715 kton-yr of KM3NeT/ORCA

AbstraktAbstract

KM3NeT/ORCA is a water-Cherenkov neutrino telescope currently under construction in the Mediterranean Sea, aimed at measuring atmospheric neutrino oscillations and determining the neutrino mass ordering. The detector consists of a three-dimensional array of detection units equipped with 18 digital optical modules, each of which houses 31 photomultiplier tubes. The Cherenkov light induced by charged particles produced in neutrino interactions in the 1 − 100 GeV range is used to reconstruct the parent neutrino’s direction and energy, allowing for constraints on the oscillation parameters Δm12 and θ23. This work presents the first Bayesian neutrino oscillation analysis performed with KM3NeT, using 715 kton-years of exposure from the ORCA detector. An adaptive Metropolis-Hastings algorithm is employed to build a Markov Chain Monte Carlo, which samples the posterior probability density in the multidimensional space spanned by oscillation and nuisance parameters. The credible region at 90% Confidence Level for the oscillation parameters, Δm312 ∈ [−2.62, −1.80] × 10−3 eV2 assuming Inverted Ordering (Δm312 ∈ [1.92, 2.72] × 10−3 eV2 for Normal Ordering) and sin2 θ23 ∈ [0.38, 0.62], are inferred from the marginal posterior PDF.

KM3NeT/ORCA is a water-Cherenkov neutrino telescope currently under construction in the Mediterranean Sea, aimed at measuring atmospheric neutrino oscillations and determining the neutrino mass ordering. The detector consists of a three-dimensional array of detection units equipped with 18 digital optical modules, each of which houses 31 photomultiplier tubes. The Cherenkov light induced by charged particles produced in neutrino interactions in the 1 − 100 GeV range is used to reconstruct the parent neutrino’s direction and energy, allowing for constraints on the oscillation parameters Δm12 and θ23. This work presents the first Bayesian neutrino oscillation analysis performed with KM3NeT, using 715 kton-years of exposure from the ORCA detector. An adaptive Metropolis-Hastings algorithm is employed to build a Markov Chain Monte Carlo, which samples the posterior probability density in the multidimensional space spanned by oscillation and nuisance parameters. The credible region at 90% Confidence Level for the oscillation parameters, Δm312 ∈ [−2.62, −1.80] × 10−3 eV2 assuming Inverted Ordering (Δm312 ∈ [1.92, 2.72] × 10−3 eV2 for Normal Ordering) and sin2 θ23 ∈ [0.38, 0.62], are inferred from the marginal posterior PDF.