Special Issues on Neutrino Telescopy

Apostolos G. Tsirigotis

Hellenic Open UniversitySchool of Science & TechnologyParticle and Astroparticle Physics Research Group

Monte Carlo Development : Event Generation

•Neutrino Interaction Events

•Atmospheric Muon Generation

μ

ν

•Atmospheric Neutrinos

ν

•Cosmic Neutrinos

Earth

Monte Carlo Development : Event Generation

Neutrino Interaction (use of Pythia)

Neutrino Interaction Probability Survival probability

Effective Neutrino Flux = Neutrino Flux × Survival Probability × Neutrino Interaction Probability

Nadir Angle P

rob

abil

ity

of

a ν μ

to

cro

ss E

arth

Monte Carlo Development : Event Generation : Example

Muon neutrinos from AGN Jets

1 Diffuse Neutrino Flux (Mannheim 1995)

1

2

3

45

2 Effective Neutrino Flux (horizontal)

3 Effective Neutrino Flux (10 degrees below horizon)

4 Effective Neutrino Flux (20 degrees below horizon)

5 Effective Neutrino Flux (30 degrees below horizon)

GeV

-1cm

-2s-1

sr-1

Neutrino energy (GeV)

Monte Carlo Development : Detector Description (Geant4)

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

Simulation strategy: During the Detector description in the simulation, the whole group of PMTs are divided in nested subgroups:

•All the relevant physics processes are included in the simulation

All PMTs group

subgroup 1 subgroup 2

subgroup 3 subgroup 6subgroup 5subgroup 4

. . . . . . . . . . . . . . . . . . . .

•For each subgroup is defined a sphere that contains all the PMTs of this subgroup

•These spheres are used to speed up the simulation as it will be described later

•Use of Clustering Algorithm that ensures the minimum dispersion of the PMTs of each group

Monte Carlo Development : Detector Description : PMT Clustering Algorithm

find the center of mass m1 of group1

find the center of mass m2 of group2

Define 2 points inside the detector, p1 & p2

For all PMTs

Which point is closer ?

p1 p2

Add PMT to group1 Add PMT to group2

is m1=p1 and m2=p2

yes

no

converge

Monte Carlo Development : Detector Description : Working Example

Detector Geometry (1km3 Grid)

21x21 Strings21 Storey per String2 PMTs per Storey (Looking up and Down)

OM Geometry (15inch)

Charge Current Atmospheric νe (20GeV) interaction

18522 PMTs

50m between PMTs

Monte Carlo Development : Simulation Technique Cherenkov photon emission

Cherenkov photons are emitted only if the are going to hit a PMT

Use the nested groups in order to minimize computer time :

The photons cross the sphere containing a detector subgroup?

NODo not produce photons

YES

For each of the 2 subgroups of the previous group

YES

The photons cross the sphere containing a detector subgroup?

NODo not produce photons

…………….

The photons cross the sphere containing the whole detector?

YES

For each of the 2 subgroups of the previous group

NODo not produce photons

Monte Carlo Development : Fast Simulation

Angular Distribution of Cherenkov Photons

EM Shower Parameterization

Parameterization of EM Shower

•Longitudal profile of shower

•Number of Cherenkov Photons Emitted (~shower energy)

•Angular profile of emitted photons

Signal Simulation

PMT response to optical photons

Collective Efficiency Collective Efficiency

Single Photoelectron Spectrum

mV

Πρότυπος παλμός

Quantum Efficiency

Standard pulse

Monte Carlo Event Production

•Computer Farm with 15 computers (15 double xeons )

•We are currently installing 64 more computers (64 double opterons)

350 Gflops

Reconstruction Algorithms

•Clustering of candidate tracks

•Kalman Filter (novel application in this area)

Angular deviation (degrees)

Angular deviation (degrees)

1TeV muons

1TeV muons

Simulation Example

1 TeV Vertically incident muon

K40 Noise Hits

Signal Hits

(Hit amplitudes > 2p.e.s)

Fast Triggering Algorithms

Estimation of Information Rate

1km3 Grid (18522 15inch PMTs)

Information Rate = PMT Number * K40 Noise Rate * (Bytes/Hit)

= 18522 * 50kHz * 32

≈ 30GB/sec

Cannot be saved directly to any media

Charge & Multiplicity Characteristics

Charge/hit distribution

Number of pes

noise

signal

Multiplicity (signal)

Multiplicity (noise)

Number of active PMTs in 6 μs window

Number of active PMTs in 6 μs window

No Cut

1TeV Vertical Muons

Charge & Multiplicity Characteristics

Selection based on hits with at least 2 photoelectrons

Multiplicity (signal) Multiplicity (noise)

Information Rate = PMT Number * K40 Noise Rate * (Bytes/Hit)

= 18522 * 3kHz * 32

≈ 1.8GB/sec

By Using clustering like DUMAND the background rate is reduced by 75% (450 MByte/sec) and the signal hit has a higher than 80% probability to survive

Fast Triggering Algorithms

Estimation of Information Rate

1km3 Grid (18522 triplets of PMTs)

3 PMTs per hemisphere in coincidence

10nsec time window, 2 out of 3 coincidence

Each triplet’s total photocathode = 15inch PMT photocathode

Information Rate = PMT Number * K40 Noise Rate * (Bytes/Hit)

= 3* 18522 * 17 Hz * 32

≈ 30MB/sec

Triplet coincidence rate=17Hz (17kHz background per PMT)

Number of active triples

Background

Signal

1TeV Vertical Muons

Fast Triggering Algorithms

Use of the number of active triplets as fast selection trigger

Distributions normalized to 1

Fast Triggering Algorithms

Estimation of Event Rate and Efficiency

Eve

nt R

ate

(kH

z)

Cut to the number of active triplets

Eff

icie

ncy

Cut to the number of active triplets

180 kByte/event

10TeV

1TeV

Fast Triggering Algorithms

1TeV Vertical Muons

Use also the Dumand clustering:

Background

Signal

Number of active triples

Fast Triggering Algorithms

Estimation of Event Rate and Efficiency

Eve

nt R

ate

(kH

z)180 kByte/event

Cut to the number of active triplets

Eff

icie

ncy

Cut to the number of active triplets

1TeV

Fast Triggering Algorithms

Raw Hits

Absolute time

TimeStretching

Trigger Level

trigger

Accepted Interval

Triggering Method

36 PMs in 3 subcylinder

35 3” photomultipliers in a cylinder

Determination of photon direction, e.g. via multi-anodic PMs plus a matrix of Winston cones.

Large photocathode area with arrays of small PMTs packed into pressure housings

Alternative Options for photodetection