Raghunath Ganugapati(Newt) && Paolo Desiati

1. Event Topology Studies for detection of prompt muons in the down going muon flux IceCube Collaboration Meeting,March 23rd,2005,Berkeley Raghunath Ganugapati(Newt) && Paolo Desiati

2. Detection With AMANDA-II • Extra Terrestrial Neutrinos • High energy spectrum hypothesis • dF/dE œ E-2 • Backgrounds • Conventional Atmospheric µ • dF/dE œ E-3.7 • Conventional Atmospheric n • from decay of (π± , K± ) • dF/dE œ E-3.7 • Possible nm components from • decay of atmospheric charmed • particles. • dF/dE œ E-2.7

3. The uncertainty~3 orders Need for accelerator data extrapolation Crossover between 40TeV and 3 PeV AMANDA II (neutrinos) Uncertainty in Prompt Lepton Cross Sections ZhVd

4. Constraint on a prompt µ is equivalent to a constraint on prompt n. • Use down going muon data Essentially same to ~100TeV at sea level Ref:GGV,hep-ph/0209111 v1 10 Sep 2002 Neutrino Vs Muon Fluxes

5. Analysis Description

6. Background Data The conventional muons produced from the π± and K± decay is the B.G. CORSIKA 6.02 with the QGSJET01 model of hadron interactions and decay used. 70 days life time worth data taken by the AMANDA II during 2001 will be studied. Signal ,Background Simulation and Data Signal Simulation Single µ with an assumed energy spectrum of prompt µ (RPQM) and isotropic in zenith and azimuth angle at the surface of the earth Standard AMANDA codes used for propagation and detector response. Charm-D model will also be used.

7. Strategies for separation of Signal from Background • Levels (L2 is the standard minimum bias data) • Zenith Angle(L3) • Event Quality Related(L4) • Topology(single muon and a bundle of muons)(L5) • Early Hit(Topology1) • dE/dX method(Topology2) • Energy(L6)

8. Zenith distribution(L3) True track Reco Track(BG) TrueTrack(BG) Reco Track Reco Track(S) True track(S) Cut these out Cos(zenith) B/S vs Cos(zenith) • S/B ratio improves near the horizon • Lots of misreconstructed muon near horizon • Angular resolution very important to see enhancement of S/B near the horizon.

9. Quality Cuts(L4) • Track Length(>120m) Distance between direct hits projected on to the length of the track • Number of Direct Hit(>6) The more the number of direct hits the better the guess track and less likely to converge to a false minimum • Reduced Chi square(<7.3) Chisquare computed using time residuals and divided by total number of hits • Pre and Post hits (prehit<1.5 and posthit<1.5) Well reconstructed muon have very few hits that arrive later or before in time (Peter Stefan's dE/dX method) Singles Singles(after QC) Multiples Multiples(after QC) Improve from 8 to 3.5 degrees Angular Resolution

10. Muon Bundles • The multiple muon background • goes with same slope as the signal • Need to improve the sensitivity • Of our instrument to prompt muon Singles Multiples Signal log10(energy at cpd) GeV

11. B Early Hit A Reconstructed track Muon2 Topology 1 (Early Hit)(L5) • Cherenkov cone BCD from reconstructed track propagating in time relative to the tracks. • Limitations • Random Noise hits • (3.0 photo electron cut) • Misreconstructed single muon • ( Good angular resolution vital ) D Δθ C Muon1 snapshot • The hit at B is earlier by time • length(AB)/cice

12. Topology 1(Eview Earlyhits) Muon Bundle Single Muon (misreco) Reconstructed Track Well reconstructed single muon should not have this Earlyhit Amplitude>3pe (proximity cut) (Noise Hits suppressed) Distance<50m (proximity cut) timedelay<-15ns

13. Time Residuals and Convoluted Pandel Data BG MC Excess Earlyhits in MC Time delay(16 PPandel) Time delay(64 CPandel)

14. 10-25 1 muon 25-50 2 0 degree 3 2 degree Does retain A decent bit of single muon 4-10 5 degree Cut these out Cut these Earlyhits EarlyHits Filtering Efficiency(Topology 1) True Track(Ideal) Result (Reco Track) Muon Multiplicity

15. Topology 2 (Energy Deposition dE/dX)(L5)

16. Hit Selection and Estimators(L5) • Quality Cuts (already discussed) • I choose only direct hits(-15ns to 75 ns)(less effected by ice properties) • Use hits with in 50m radius cylinder around the track(less scattered) • Take only hits with amplitude greater 3.0 P.E for reconstruction. Estimator1 B= Nphoton Observed Photon Nphoton expected from MIM Estimator1 gives Estimator2 y = σ B/<B>

17. Filtering Efficiency(L5) Result (Reco track) True track(Ideal) Signal BG Cut these out Cut these out y = σ B/<B> y = σ B/<B>

18. Energy Cut(L6) Data observed=16 Signal Expectation (RPQM)=9.4 B.G Expectation=6.0 Event upper limit=22.4 MRFsim=0.70 (30% SYS) MRFdata=22.4/9.4= 2.3(preliminary) Integral Spectra BG Signal Avg Upper limit 0% sys) Data Description 10% sys (20% sys) 2001 exp data 2001 signal (RPQM)+BG (30% sys) MRF (40% sys) Nhits(Energy Observable) Best Cut Nhit=310,Signal=9.4,B.G=6 MRF=0.7(30%SYS) Nhits(Energy Observable)

19. Constraining Charm Neutrino models by analysis of downgoing Muon Data AMANDA II (neutrinos) ZHVd A Restrictive limit means enhance sensitivity to diffuse neutrinos AMANDA II (muons)

20. BACK UP SLIDES

21. Energy Correlation Number of Hits Vs log10(energy at cpd) GeV

22. Note that the distribution of less than 3.0P.E. hits remains almost flat outside 50m. Could be noise?(Randomness) Why than does it fall down as we come close to the track? There is a pile up in amplitude for noise hits inside 50m from the track as the pulse from early noise hit gets smeared out with the actual hits from muons Amplitude-Perpendicular distance to the Hit space Greater than 3.0P.E hits Δt<-15ns only Less than 3.0P.E hits Good hits Random Hits ~10 times greater Dump this space out Perpendicular distance from reconstructed track for BGMC muons(m)

23. Geometrical Effect Reconstructed track in data True track B Leverarm(AB)*Δθ Dust Reconstructed track in simulation Δθ Clear Ice A Dust • The Monte Carlo tracks are reconstructed away from the true track than in the data because of various assumptions and the way the time delay is calculated. • The tracks are reconstructed pivoted about the centre of the detector so any discrepancies in timing tend to scale roughly as the distance from the centre and hence outer strings become more susceptible to the differences than the inner ones.

24. Filtering Efficiency(L5) Singles When all hits are chosen notice what happens? Any possible separation of S-B is destroyed by the fluctuation of ice properties Multiples Keep These Done with all hits (not just direct hits)

25. Amplitude-Time Residual space Data Data Background Background Ignore these Amplitude(P.E) Amplitude(P.E) A projection of the amplitude for a region of space in time residual less than –15ns is shown; there appears to be some disagreement between the data and the simulation in the low amplitude regime. This bin(0-2 P.E) has significantly large number of hits compared with the other neighboring bins. What are these hits?Noise? R2

26. Ice Properties • Ice properties themselves introduce some fluctuations into the observed amplitude • Think what the optical properties of a dust layer could do to the Photo Electron recorded? • May be need to apply corrections to the PE recorded depending on the layer of ice to retrieve information in original form to undo what ice does (for Horizontal muons this gets tricky!!!)

27. Reconstruction Errors True track Large Amplitude Seen when lower is expected from reco track hypothesis B Dust Δθ Reco Track Clear Ice A Dust Small Amplitude Seen when large is expected from reco track hypothesis

28. Data Agreement(16fold-ppandel) Data The Overall Agreement is not extremely good within the limit of systematics (30-40%) A possibility to improve the scenario is to use a 64-iteration Convoluted Pandel and repeat the whole procedure described B.G Signal Number of Hits