1. Special Issues on Neutrino Telescopy Apostolos G. Tsirigotis Hellenic Open University School of Science & Technology Particle and Astroparticle Physics Research Group The project is co-funded by the European Social Fund & National Resources EPEAEK-II (PYTHAGORAS) KM3NeT Kick-off Meeting 11. - 13. April 2006 University of Erlangen-Nuremberg

2. Monte Carlo Development : Event Generation Neutrino Interaction Events Atmospheric Muon Generation Atmospheric Neutrinos Cosmic Neutrinos Neutrino Interaction (use of Pythia) Neutrino Interaction Probability (cross-sections) Earth Absorption Effective Neutrino Flux = Neutrino Flux × Survival Probability × Neutrino Interaction Probability Nadir Angle Probability of a ν μ to cross Earth

3. Monte Carlo Development : Event Generation : Example Muon neutrinos from AGN Jets 1 Diffuse Neutrino Flux (Mannheim 1995) 1 2 3 4 5 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 -1 cm -2 s -1 sr -1 Neutrino energy (GeV)

4. 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 1subgroup 2 subgroup 3subgroup 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

5. 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 ? p1p2 Add PMT to group1 Add PMT to group2 is m1=p1 and m2=p2 yes no converge

6. Monte Carlo Development : Detector Description : Working Example Detector Geometry (1km 3 Grid) 21x21 Strings 21 Storey per String 2 PMTs per Storey (Looking up and Down) OM Geometry (15inch) CC Atmospheric ν e (20GeV) interaction (EM Shower) 18522 PMTs 50m between PMTs

7. Monte Carlo Development : Simulation TechniqueCherenkov 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? NO Do not produce photons YES For each of the 2 subgroups of the previous group YES The photons cross the sphere containing a detector subgroup? NO Do not produce photons ……………. The photons cross the sphere containing the whole detector? YES For each of the 2 subgroups of the previous group NO Do not produce photons

8. 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

9. Signal Simulation PMT response to optical photons Quantum Efficiency Collective Efficiency Single Photoelectron Spectrum mV

10. Monte Carlo Event Production Computer Farm with 15 computers (15 double xeons ) We are currently installing 64 more computers (64 double opterons) 350 Gflops

11. Reconstruction Algorithms Direct Walk Filter Χ 2 fit without charge Kalman Filter (novel application in this area)

12. Simulation Example 1 TeV Vertically incident muon K 40 Noise Hits Signal Hits (Hit amplitudes > 2p.e.s)

13. Fast Triggering Algorithms Estimation of Information Rate 1km 3 Grid (18522 15inch PMTs) Information Rate = PMT Number * K 40 Noise Rate * (Bytes/Hit) = 18522 * 50kHz * 32 ≈ 30GB/sec Cannot be saved directly to any media

14. Charge & Multiplicity Characteristics Charge/hit distribution Number of pes noise signal Multiplicity (signal) Multiplicity (noise) Number of active PMTs in 6 μs window No Cut 1TeV Vertical Muons

15. Charge & Multiplicity Characteristics Selection based on hits with at least 2 photoelectrons Multiplicity (signal) Multiplicity (noise) Information Rate = PMT Number * K 40 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 60% probability to survive

16. Fast Triggering Algorithms Estimation of Information Rate 1km 3 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 * K 40 Noise Rate * (Bytes/Hit) = 3* 18522 * 17 Hz * 32 ≈ 30MB/sec Triplet coincidence rate=17Hz (17kHz background per PMT)

17. Number of active triples Background Signal 1TeV Vertical Muons Fast Triggering Algorithms Use of the number of activate triplets as fast selection trigger Distributions normalized to 1

18. Fast Triggering Algorithms Estimation of Event Rate and Efficiency Event Rate (kHz) Cut to the number of active triplets Efficiency Cut to the number of active triplets 180 kByte/event 10TeV 1TeV

19. Fast Triggering Algorithms Raw Hits Absolute time Time Stretching Trigger Level trigger Accepted Interval Triggering Method