Report of the HOU contribution to KM3NeT TDR (WP2) A. G. Tsirigotis In the framework of the KM3NeT Design Study WP2 Meeting - Erlangen, May 2009

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

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.

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)

8m 20 floors per tower 30m seperation Between floors 30m Tower Geometry Floor Geometry 45 o Detector Geometry: OMs in a hexagonal grid. 91 Towers seperated by 130m, 20 floors each. 30m between floors Working Example 60 meters maximum abs. length no optical photon scattering

Optical Module 10 inch PMT housed in a 17inch benthos sphere 35%Maximum Quantum Efficiency Genova ANTARES Parametrization for OM angular acceptance 50KHz of K 40 optical noise Working Example

Results Atmospheric Muons (Corsika files - 1 hour of generated showers) Cos(theta) (Time in days) Without any cuts (21000/day misrec. as upgoing) With optimal cuts (0 misrec. as upgoing) #hits>=12 #solutions>=20 #compatible solutions>=10 #compatible / #sol >0.5 Mild likelihood cut depended on reconstructed energy

Neutrino effective area (E -2 spectrum) Results Effective area (m 2 ) Neutrino Energy (log(E/GeV)) Without any cuts With optimal cuts

Neutrino Angular resolution (median in degrees) Results Neutrino Energy (log(E/GeV)) Angular resolution (median degrees) Without any cuts With optimal cuts

Results Muon Angular resolution (For the neutrino induced muon at impact point) Energy (log(E/GeV)) Angular resolution (median degrees) With optimal cuts Neutrino Muon

Chi-square probability Investigating

Results Atmospheric neutrinos (Bartol Flux) Cos(theta) (Time in days) With optimal cuts (130/day rec.) Without any cuts (200/day rec.) 65% cut efficiency Neutrino Energy (log(E/GeV)) (Time in days) Neutrino Energy (log(E/GeV)) cut efficiency

Results Muon energy reconstruction (at the closest approach to the detector) Simulation data from E -2 neutrino flux Log(E true /GeV) Log(E rec /GeV)

Results Muon energy reconstruction resolution Log(E true /GeV) RMS(Log(E/GeV)) Δ(Log(E/GeV))

Calculation of point source and diffuse flux sensitivities for varius depths (with reconstructed energy cut application) Simulation of more KM3NeT Detector Geometries We are finalizing

Conclusions Kalman Filter is a promising new way for filtering and reconstruction for KM3NeT However for the rejection of badly misreconstructed tracks additional cuts must be applied Charge Likelihood can be used effectively in energy reconstruction Presented by Apostolos G. Tsirigotis

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)

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

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)