1. Report of the HOU contribution to KM3NeT TDR (WP2) A. G. Tsirigotis In the framework of the KM3NeT Design Study WP2 Meeting - Marseilles, 29June-3 July 2009

2. GEANT4 Simulation – Detector Description Any detector geometry can be described in a very effective way Use of Geomery Description Markup Language (GDML-XML) software package All the relevant physics processes are included in the simulation (OPTICAL PHOTON SCATTERING ALSO) Fast SimulationEM Shower Parameterization Number of Cherenkov Photons Emitted (~shower energy) Angular and Longitudal profile of emitted photons Visualization Detector components Particle Tracks and Hits

3. Charge Likelihood – Used in muon energy estimation Hit charge in PEs Probability depends on muon energy, distance from track and PMT orientation Not a poisson distribution, due to discrete radiation processes Ln(F(n)) Ln(n) E=1TeV D=37m θ=0deg Log(E/GeV)

4. Tower GeometryFloor Geometry 45 o NuOne: 91 Towers in a hexagonal grid, seperated by 130m. 20 floors each Tower. 30m between floors. Bar length 8m Detectors 67 meters maximum abs. length no optical photon scattering 30m SieWiet: 91 Strings in a hexagonal grid, seperated by 130m, 20 MultiPMTs per floor, 30m between floors. Benchmark Detector, and:

5. Single PMT Optical Module 10 inch PMT housed in a 17inch benthos sphere 35%Maximum Quantum Efficiency Genova ANTARES Parametrization for OM angular acceptance 75KHz of K 40 optical noise Optical Modules Multi PMTOptical Module 31- 3inch multi PMT OM housed in a 17inch benthos sphere 35%Maximum Quantum Efficiency NIKHEF Parametrization for PMT angular acceptance (0.1+0.9cos(θ)) 7KHz of K 40 optical noise

6. Results Atmospheric Muon background (CORSIKA files – 1 hour lifetime) + Atmospheric Neutrino Background (bartol flux) signal Sensitivity

7. Sourse Declination NuOne (4800m seabed)NuOne (3500m seabed) benchmark (3500m seabed) -60 2.89 EnergyCut=3.2TeV Rbin=0.35 degrees 2.94 EnergyCut=3.2TeV Rbin=0.35degrees 4.03 EnergyCut=3.2TeV Rbin=0.4 degrees Point Source Sensitivity (x10 -9 E 2 (dN/dE)(cm -2 s -1 GeV) (VERY PRELIMINARY) Neutrino Energy (log(E/GeV)) Effective area (m 2 ) Results : E -2 source flux (100GeV – 1000PeV) – 1 year of observation Benchmark NuOne

8. Angular resolution at sensitivity limit Energy (log(E/GeV)) Angular resolution (median degrees) Benchmark detectorNuOne Detector Neutrino Muon

9. Energy resolution at sensitivity limit (NuOne detector) RMS(Log(E/GeV)) Log(E true /GeV)

10. Muon angular resolution Neutrino angular resolution Neutrino Energy (log(E/GeV)) Muon Energy (log(E/GeV)) degrees Effective area (m 2 ) Neutrino Energy (log(E/GeV)) Comparison between benchmark and SeaWiet Same cuts applied Seawiet with OM-directionality Seawiet without OM-directionality Benchmark detector

11. Muon reconstruction statistical properties Chi-square probability

12. Estimation of sensitivity errors Calculation of point source sensitivity of SeaWiet Calculation of diffuse flux sensitivities for all detectors and depths Coming very soon

13. The HOU software chain Underwater Neutrino Detector Generation of atmospheric muons and neutrino events (F77) Detailed detector simulation (GEANT4-GDML) (C++) Optical noise and PMT response simulation (F77) Filtering Algorithms (F77 –C++) Muon reconstruction (C++) Effective areas, resolution and more (scripts) Extensive Air Shower Calibration Detector (Sea Top) Atmospheric Shower Simulation (CORSIKA) – Unthinning Algorithm (F77) Detailed Scintillation Counter Simulation (GEANT4) (C++) – Fast Scintillation Counter Simulation (F77) Reconstruction of the shower direction (F77) Muon Transportation to the Underwater Detector (C++) Estimation of: resolution, offset (F77)

14. Event Generation – Flux Parameterization Neutrino Interaction Events Atmospheric Muon Generation (CORSIKA Files, Parametrized fluxes ) μ Atmospheric Neutrinos (Bartol Flux) ν ν Cosmic Neutrinos (AGN – GRB – GZK and more) Earth Survival probability Shadowing of neutrinos by Earth

15. Prefit, filtering and muon reconstruction algorithms Local (storey) Coincidence ( Applicable only when there are more than one PMT looking towards the same hemisphere) Global clustering (causality) filter Local clustering (causality) filter Prefit and Filtering based on clustering of candidate track segments (Direct Walk technique) Combination of Χ 2 fit and Kalman Filter (novel application in this area) using the arrival time information of the hits Charge – Direction Likelihood using the charge (number of photons) of the hits dmdm L-d m (V x,V y,V z ) pseudo-vertex dγdγ d Track Parameters θ : zenith angle φ: azimuth angle (Vx,Vy,Vz): pseudo-vertex coordinates θcθc (x,y,z)

16. State vector Initial estimation Update Equations Kalman Gain Matrix Updated residual and chi- square contribution (rejection criterion for hit) Kalman Filter application to track reconstruction timing uncertainty Many (40-200) candidate tracks are estimated starting from different initial conditions The best candidate is chosen using the chi- square value and the number of hits each track has accumulated.