IceCube Collaboration Meeting Berkeley – 19-23 March 2005

1. understanding the systematicsa starting point for a discussion on assessing theoretical and experimental uncertainties and improve data interpretationPaolo Desiati & Teresa MontaruliUW - Madison IceCube Collaboration Meeting Berkeley – 19-23 March 2005

2. motivation • AMANDA is a mature experiment providing results to the scientific community with higher and higher statistics • primary scope of AMANDA is detect HE neutrinos from astrophysical objects • need to well understand measurements outcome with controlled conditions • use of natural atmospheric muon and neutrino flux; but these calibrationsources (our main background) are known only to a certain degree • our interest is the high energy response ! • Important the understanding of background • reproduce observables within the whole range of values • use consistent simulations for different event types • conditions to separate experimental response from physics input (primary CR spectrum and interaction models - see Teresa’s talk) • Possibily contribute to constrain standard physics to access the unknown sources of HE astrophysics

3. extension to horizon down-going muon flux point-to-point correction: • Trigger rate ~ - 25:30% of exp • Corrected flux ~ + 30% Normalized to first point Angular resolution ~2.5o Slope ~ muon energy spectrum ~ primary spectrum ~ muon energy loss ~detector (ICRC 2001/03)

4. down-going muon flux from Dima’s thesis: * * • fit CORSIKA with and using • Hörandel primary spectrum (Astrop. Phys. 19(2003),193) • QGSJET interaction model • measurement of primary CR spectral index γ • robust measurement against interaction models (DPMJET, HDPM, NEXUS, QJSGET, SIBYLL, VENUS), primary spectra (Wiebel-Sooth, Hörandel) and ice models • not strong dependence to primary spectrum and hadronic interaction from muon channel • proper choice of experimental conditions • robust measurement for detector calibration γ= 2.70  0.02 Φo = 0.106  0.007 cm-2 s-1 sr-1

5. K→νμ π→μ K→μ π→νμ uncertainties from hadronic interactions • K physics higher uncertainties • competition between K and π • E>100GeV K start dominate • importance of K enhanced forνμ • muon not strongly affected by K uncertainties Fraction • prompt physics uncertainties • models different by orders of magnitude • could be the highest neutrino background for E > 0.1 - 1 PeV Log10(Eν)

6. histogram : NUSIM histogram : CORSIKA line : Lipari dots: AMANDA-II data line : Bartol Unfolded neutrino energy spectrum (2000) line : Honda ~x2 atmospheric neutrinos can we use AMANDA-II atmospheric neutrino data to probe these uncertainties ? E3·dN/dE (cm-2 s-1 sr-1GeV2) Log10(Eν) CORSIKA ~ - 30:50% than NUSIM/Lipari

7. TIG (1996) conventional & prompt Naumov (1998) RQPM Naumov (1998) QGSM the charm of atmospheric neutrinos Vertical neutrino flux vertical fluxes Lipari Bartol fit to Corsika + prompts Honda fit to CORSIKA E3·dN/dE (cm-2 s-1 sr-1GeV2) Log10(Eν) • errors (statistics+unfold) still too big, can reduce them with • increase statistics (on the way) • use energy bin ~ energy resolution (unfolded error smaller) • spectrum unfolded @ different zenith angles • unfolding uses detector response for a given energy spectrum (Lipari - NUSIM) • how robust is the measurement ? • energy resolution ~ 0.3 in LogE • improve energy reconstruction – linearity – resolution (?) • extend energy reconstruction at higher energies

8. improve systematics • understanding background • down-going muons as relatively robust probe of detector response • relative rate measurements (seasonal variations, K fraction) • atmospheric neutrino measurement as probe to physics • needs high statistics of high quality data • ice properties • affects hit timing • affects energy estimation for tracks (nhits) • could be main cause of irreproducibility of mis-reconstructed muon tracks • reconstruction algorithms • better reconstruction makes background rejection less dependent on cuts • measure under stable conditions • through-going tracks • maximum distance of OM from track