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

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 Simulation EM Shower Parameterization

•Number of Cherenkov Photons Emitted (~shower energy)

•Angular and Longitudal profile of emitted photons

Visualization

Detector componentsParticle Tracks and Hits

Charge Likelihood – Used in muon energy estimation

hit nohit

hit nohit

N N

i data ii 1 i 1

;E,D, ;E,D,

(N N )

ln P (V ) P (0 )

L

i data PMTresolutiondatan 1

P (V ;E,D, ) F(n;E,D, )G n(V ;n, )

dataV Hit charge in PEs

Probability depends on muon energy, distance from track and PMT orientation

F(n;E,D, ) Not a poisson distribution, due to discrete radiation processes

Ln(F

(n))

Ln(n)

E=1TeVD=37mθ=0deg

Log(E/GeV)

hit nohitL (N N )

Tower Geometry Floor Geometry

45o

45o

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:

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 K40 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 K40 optical noise

Results

Atmospheric Muon background (CORSIKA files – 1 hour lifetime) + Atmospheric Neutrino Background (bartol flux)

2( ) signal

9090

( )bg

s

nK K

n

90 90

0

( ) ( , )!

bg

o

n nbg

bg o bgn o

n en n n

n

Sensitivity

Sourse Declination

NuOne (4800m seabed) NuOne (3500m seabed)

benchmark (3500m seabed)

-60 2.89EnergyCut=3.2TeVRbin=0.35 degrees

2.94EnergyCut=3.2TeVRbin=0.35degrees

4.03EnergyCut=3.2TeVRbin=0.4 degrees

Point Source Sensitivity (x10-9E2(dN/dE)(cm-2s-1GeV) (VERY PRELIMINARY)

Neutrino Energy (log(E/GeV))

Eff

ecti

ve a

rea

(m2 )

Results : E-2 source flux (100GeV – 1000PeV) – 1 year of observation

BenchmarkNuOne

Angular resolution at sensitivity limit

Energy (log(E/GeV))

An

gu

lar

reso

luti

on

(m

edia

n d

egre

es)

Benchmark detector NuOne Detector

Neutrino

Neutrino

Muon

Muon

Energy resolution at sensitivity limit (NuOne detector)

RM

S(L

og

(E/G

eV))

Log(Etrue/GeV)

Muon angular resolution

Neutrino angular resolution

Neutrino Energy (log(E/GeV))

Muon Energy (log(E/GeV))

deg

rees

deg

rees

Effective area (m2)

Neutrino Energy (log(E/GeV))

Comparison between benchmark and SeaWiet

Same cuts applied

•Seawiet with OM-directionality•Seawiet without OM-directionality•Benchmark detector

_ _ _( - ) /muon reco muon real reco

_ _ _( - ) /muon reco muon real reco

Muon reconstruction statistical properties

Chi-square probability

•Estimation of sensitivity errors

•Calculation of point source sensitivity of SeaWiet

•Calculation of diffuse flux sensitivities for all detectors and depths

Coming very soon

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 Χ2 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

dm

L-dm

(Vx,Vy,Vz) pseudo-vertex

dγ

d

Track Parameters

θ : zenith angle φ: azimuth angle (Vx,Vy,Vz): pseudo-vertex coordinates

θc

(x,y,z)

State vector

1

exp

k

k

x x

tH

x

0 0,x CInitial 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.

0 0( , ).x C