KM3NeT – ORCA Measuring Neutrino Oscillations and the Neutrino Mass Hierarchy in the Mediterranean Sea J. Brunner CPPM

Matter Effects and Neutrino Mass Hierarchy (NMH) DM2 ~ A matter potential must be significant L large enough – use atmospheric neutrinos ! Distinction between neutrinos and anti-neutrinos  cross-sections!

Method to Determine NMH Both muon- and electron-channels contribute to net hierarchy asymmetry electron channel more robust against detector resolution effects: E, θ smearing (kinematics + detector resolution)

KM3NeT: Next generation Neutrino Telescope in the Mediterranean Sea Distributed research infrastructure with 2 main physics topics: Low-Energy studies of atmospheric neutrinos – High-Energy search for cosmic neutrinos  Low-Energy (ORCA) ( ) High-Energy (ARCA)

The ORCA benchmark design 115 lines, 20m spaced, 18 DOMs/line 6m spaced Instrumented volume ~3.8 Mt, 2070 OM 450 m 31 3” PMTs Digital photon counting Directional information Wide angle of view More photocathode than 1 ANTARES storey Cost reduction compared to ANTARES Poster : Ronald Bruijn

Ingredients for NMH measurement Efficient and high purity trigger algorithm for neutrino and atmospheric muon events Exploit excellent photon counting of multi-PMT DOMs Use causality of direct photons  water almost scattering free for visible photons Reconstruction of cascade and track topologies High efficiency down to relevant energies Good resolution in energy and zenith angle Topology Identification (track  cascades) Atmospheric muon rejection (no hardware veto)

Reconstruction Methods Dedicated methods for tracks and cascades have been developed 8 parameters are determined Time, position (3), direction (2), energy, inelasticity Step by step procedure (no brute force fit) Hit selection (similar to trigger) Vertex & Directional fit (timing) Energy & inelasticity fit (light yield & direction/vertex)

Performances ne (cascades) Poster : Jannik Hofestädt 450 m Excellent angular resolution Dominated by kinematics PINGU donne ses performances pour 40 strings; donc un détecteur plus dense que 20 strings. MONOPOD est une boite niore qui donne l’energie de la gerbe et ignore la trace. Mais on peut penser que la plupart de la lumère vue vient de la gerbe? E_nu  PINGU   ORCA  3         0.7         1.0  5         1.2         1.1  7         1.6         1.9  9         2.0         3.2 11        2.7         4.2 13        3.4         5.7 15        3.9         7.5 17        4.7         8.7 19        5.2         9.0 Energy resolution better than 25% in relevant range – close to Gaussian

Performances nm (tracks) Poster : Salvatore Galata 450 m Excellent angular resolution Dominated by kinematics PINGU donne ses performances pour 40 strings; donc un détecteur plus dense que 20 strings. MONOPOD est une boite niore qui donne l’energie de la gerbe et ignore la trace. Mais on peut penser que la plupart de la lumère vue vient de la gerbe? E_nu  PINGU   ORCA  3         0.7         1.0  5         1.2         1.1  7         1.6         1.9  9         2.0         3.2 11        2.7         4.2 13        3.4         5.7 15        3.9         7.5 17        4.7         8.7 19        5.2         9.0 Energy resolution better than 25% in relevant range – close to Gaussian

Effective Mass Above 10 GeV Meff close to instrumented volume Similar for cascades and tracks

ORCA Layout Optimization 20m interline spacing imposed by line deployment (sea operations) Vertical spacing open : 6,9,12,18m spacing inter-DOM shown Median zenith res (°) Median Frac. E res Examples for cascades Resolutions stable Meff 5-10 GeV crucial Effective mass (Mton)

Flavour (mis)-identification KM3NeT/ORCA preliminary Probabilities to classify as track-like The different curves show the energy resolved classification performance for different lower cuts on the fraction of trees in the forest that agree that the event belongs to the class of track like events. As one can see a stricter cut on the tree fraction increases the classification purity on the expense of the rate at constant energy. Clearly, the classification rate is much more sensitive to the event energy than the purity. This indicates that the classification benefits significantly from the increase in light output and track length at higher energies. Discrimination between 2 classes of events: track-like ( nmCC ) and shower-like ( nNC, neCC ) Classification using „Random Decision Forest“ machine-learning algorithm. Discrimination mainly due to event reconstruction observables. Discrimination of track-like ( nmCC ) and cascade-like ( nNC, neCC ) events Classification uses “Random Decision Forest” Better than 80% above 10 GeV for all channels but nmCC

Atmospheric muon rejection Instrumental veto not mandatory Atmospheric n (E<20 GeV) Atmospheric m Few % contamination achievable without too strong signal loss Poster : Luigi Fusco

Systematic Effects Various systematic effects taking into account Oscillation parameters Δm2, θ12 fixed; θ13 fitted within its error ΔM2, θ23, δCP  fitted unconstrained Flux, cross section , detector related (average fluctuation w.r.t. nominal) Overall normalisation (2.0%) ν/v ratio (4.0%) e/μ ratio (1.2%) NC scaling (11.0%) Energy slope (0.5%) Fitted unconstrained

Sensitivity to Neutrino Mass Hierarchy Poster : Martijn Jongen Dependence of sensitivity on time for fixed θ23 values dCP fixed to zero for easy comparison with other experiments Track vs shower event classification Full MC detector response matrices including misidentified and NC events Atmospheric muon contamination Neutral current event contamination Various Systematic uncertainties Each type of events (determined by neutrino flavour, interaction type and particle ID) now has dedicated resolutions.

Sensitivity to Neutrino Mass Hierarchy Dependency of sensitivity on θ23 for 3 years NH easier to determine than IH Second octant easier than first octant When fixing dCP to zero sensitivity increases by ~0.5σ Track vs shower event classification Full MC detector response matrices including misidentified and NC events Atmospheric muon contamination Neutral current event contamination Various Systematic uncertainties Each type of events (determined by neutrino flavour, interaction type and particle ID) now has dedicated resolutions.

Sensitivity to Neutrino Mass Hierarchy Dependency of sensitivity on θ23 and dCP for NH and 3 years Best case : large θ23 and δCP = 0o Worst case : small θ23 and δCP = 180o Each type of events (determined by neutrino flavour, interaction type and particle ID) now has dedicated resolutions.

Sensitivity to PMNS parameters ΔM2 – unconstrained fit in conjunction to mass hierarchy hypothesis testing  Significant improvement of precision achievable PDG 2014 The above figure shows the precision with which theta23 can be measured by ORCA. Note that a 2 degree prior has been included, so this does not include a determination of the octant.

Sensitivity to PMNS parameters Θ23 – unconstrained fit in conjunction to mass hierarchy hypothesis testing World best measurement after few years of data taking The above figure shows the precision with which theta23 can be measured by ORCA. Note that a 2 degree prior has been included, so this does not include a determination of the octant. PDG 2014

ORCA costs & timeline Modular ring of up to 5-6 nodes via two cables to shore for up to 120 detection units + sea science instruments Possibility to redirect the ANTARES cable to ORCA as second main cable Phase 1 (funded) Deploy new main cable, junction box and a 6-7 string array in the ORCA configuration to demonstrate detection method in the GeV range.

ORCA costs & timeline Next Step -- 24-30 lines & second junction box -- equipment for sea science

Conclusion ORCA started detector construction Budget situation promising Full simulation and reconstruction framework available Systematic uncertainties under control Further improvements possible using inelasticity NMH determination feasible on unequalled time scale Improvement of measurement precision for atmospheric oscillation parameters

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Sensitivity studies 1) Fit parameters assuming NH To optimally distinguish between IH and NH: likelihood ratio test with nuisance parameters → deal with degeneracies by fitting Parameter estimates possibly Using constraints 1) Fit parameters assuming NH 2) Fit parameters assuming IH 3) Compute DlogL = log( L(NH)/L(IH) ) q23 , Dm2large and normalization fitted from data

ORCA shower reconstruction (ne) Res. (s): 0.5-1 m

ORCA Sensitivity to Inelasticity Use PDFs on the time residuals under the track (low-y) and shower (high-y) hypothesis Select y-interval corresponding to highest likelihood “total significance … may increase by (20 - 50)%, thus effectively increasing the volume … by factor 1.5 – 2” Ribordy & Smirnov PRD, 87. 113007 (muon channel only) Work in progress Should be further exploited PID, NC rejection, neutrinos/anti-neutrinos…