HOU Reconstruction & Simulation (HOURS): A complete simulation and

reconstruction package for Very Large Volume underwater neutrino Telescopes.

A.Leisos, A. G. Tsirigotis, S.E.Tzamarias

In the framework of the KM3NeT Design Study

VLVnT 2009 - Athens, 15 October 2009

The HOURS software chain

Underwater Neutrino Detector

•Generation of atmospheric muons and neutrino events (F77–CORSIKA,Pythia)

•Detailed detector simulation (GEANT4-GDML) (C++)

•Optical noise and PMT response simulation (F77)

•Filtering Algorithms (F77 –C++)

•Muon reconstruction (C++)

•Effective areas, angular resolution and sensitivities calculation (scripts)

Extensive Air Shower Calibration Detector (Sea Top)

•Atmospheric Shower Simulation (CORSIKA) – Unthinning Algorithm (F77)

•Detailed EAS detector response Simulation (GEANT4) (C++)

•Reconstruction of the shower direction (F77)

•Muon Transportation to the Underwater Detector (C++)

•Estimation of: resolution, offset (F77)

CORSIKA(Extensive Air

Shower Simulation)

All Flavor NeutrinoInteraction Events

(Secondary Particles Generation)

Atmospheric MuonGeneration from

CORSIKA

GEANT4(KM3NeT Detector Description

and Simulation)

GEANT4(Muon Propagation

to KM3NeT)

Optical Noise, PMT response and Electronics Simulation

Prefit & Filtering Algorithms

Muon Reconstruction

EAS detector Simulation

Shower direction reconstruction

The HOURS software chain Flow Chart

SeaTop CalibrationNeutrino Telescope

Performance

Event Generation – Flux Parameterization

•Neutrino Interaction Events(PYTHIA)

•Atmospheric Muon Generation(CORSIKA)

μ

•Atmospheric Neutrinos(Bartol Flux)

ν

ν

•Cosmic Neutrinos(AGN – GRB – GZK and more)

Earth

Event Generation – Shadowing of neutrinos by Earth

Earth Density Profile

Column Depth

Survival probability

Nadir Angle P

rob

abil

ity

of

a ν μ

to

cro

ss E

arth

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 photonsVisualization

Particle Tracks and Hits

Detector components

Angular Distribution of Cherenkov Photons

Simulation of the PMT response to optical photons

Single Photoelectron Spectrum

mV

Quantum Efficiency

Πρότυπος παλμός

Standard electrical pulse for a response to a single p.e.

Arrival Pulse Time resolution

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)

Kalman Filter – Muon Track Reconstruction - Algorithm

Extrapolate the state vector

Extrapolate the covariance matrix

Calculate the residual of predictionsDecide to include or not the measurement (rough criterion)

Update the state vector

Update the covariance matrix

2Calculate the contribution of the filtered pointDecide to include or not the measurement (precise criterion)

Prediction Filtering

Initial estimates for the state vector and

covariance matrix

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)

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

Kalman Filter – Muon Track Reconstruction - Algorithm

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

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

Muon Track Reconstruction

Chi-square probability

Energy (log(E/GeV))

Neutrino

Muon

Angular resolution

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 )

Log(Etrue/GeV)

Lo

g(E

reco

/GeV

)

Log(E/GeV)

Simulation data from E-2 neutrino flux and SeaWiet detector (300 Strings, 130m between strings, 20 MultiOMs/string, 40m MultiOM distance)True muon energy at the closest approach to the detector center (impact point)

Δ(Log(E/GeV))

True and reconstructed muon energy distribution

Muon energy reconstruction Results 1

Muon energy reconstruction Results 2

Log(Etrue/GeV) Log(Etrue/GeV)

RM

S(Δ

(Lo

g(E

/GeV

)))

Mea

n(Δ

(Lo

g(E

/GeV

)))

Neutrino Detector Performance

Atmospheric Muon background + Atmospheric Neutrino Background

( ) a signal

9090

( )bg

s

nK K

n

90 90

0

( ) ( , )!

bg

o

n nbg

bg o bgn o

n en n n

n

Point Source Sensitivity

m2

Neutrino Energy (log(E/GeV))

Angular resolution (median)

deg

rees

Neutrino Energy (log(E/GeV))

Neutrino Effective area

9 2 2 1 110 ( )E cm s GeV

Sea top Detector Simulation Tools

CORSIKA(Extensive Air Shower

Simulation)

GEANT4(Scintillation, WLS & PMT response)

Fast Simulation also available

Unthinning Algorithm

DAQSIM(DAQ Simulation)

HOUANA(Analysis &

Track Reconstruction)

Zentih (degrees)

Sea top Detector Simulation Tools

Charge (in units of mean p.e. charge)

At the Detector Center

Data

- Monte Carlo Prediction

GEANT4Muon Propagation to KM3

HOU-KM3Muon track (s) reconstruction

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)

Sea top Detector Simulation Tools

Conclusions

•The study of the response of a km3-scale underwater neutrino

telescope is a crucial point both for the design of the detector

and for the experimental analysis.

•The HOU Reconstruction & Simulation (HOURS) software

package is a complete simulation package of the detector

response from the expected neutrino fluxes to the event

reconstruction and sensitivity estimation for different neutrino

sources.

GEANT4 Simulation – Detector Description

• Any detector geometry can be described in a very effective way

Use of Geomery Description Markup Language (GDML, version 2.5.0) software package

•All the relevant physics processes are included in the simulation

Particle Physical Processes

All Decays (unstable), multiple scattering (charged), continuous energy loss, ionization and δ-ray production (charged), Cherenkov effect (charged)

μ-/μ+ Bremsstrahlung, photonuclear interaction, direct (e+,e-) pair production

e-/e+ Bremsstrahlung, e+e- annihilation

γ γ conversion, photoelectric effect, Compton scattering

Hadrons Hadronic Interactions

Optical Photons

Absorption, medium boundary interactions