M. Kowalski Search for Neutrino-Induced Cascades in AMANDA II Marek Kowalski DESY-Zeuthen Workshop on Ultra High Energy Neutrino Telescopes Chiba,

M. Kowalski Outline Introduction Reconstruction of cascade-like events Searching for cascade-like events in the AMANDA II data Summary

M. Kowalski Neutrino-Induced Cascades: Signature of  e and  are hadronic and electro- magnetic cascades. Neutral Current interactions of all neutrino flavors produce hadronic cascades Background consists of atmospheric muons, emitting energetic secondaries S Signal and Background ~ 5 m

M. Kowalski Advantages : Large Sensitivity for e and  Local events, therefore better energy resolution Less intrinsic background of atmospheric muons & neutrinos Nearly 4  sensitivity Disadvantages: Less signal than in the muon channel due to very large muon range Worse angular resolution Local events, therefore better energy resolution Less background of atmospheric neutrinos Less signal than in the muon channel since muon range very large Why search for Neutrino-Induced Cascades?

M. Kowalski With scattering 0  t tt far track close track 0  t Reconstructing Cascades: Vertex Position Without scattering

M. Kowalski Vertex Resolution Reconstruction of 1 TeV EM cascades which trigger AMANDA II Vertex resolution of cascades in the detector: (radius 100 m, height = 200 m)  ~ 5 m for x,y,z coordinates and large range of energies.

M. Kowalski Energy Reconstruction Parameterization of hit- probability with MC. Function is random walk inspired: Construction of Likelihood function:

M. Kowalski Resolution of Energy Reconstruction  Reconstruction of EM cascades of energies: 10 2, 10 3, 10 4,10 5,10 6 GeV.  Vertex within AMANDA II. (radius = 100m, height =200m) Vertex fitted with time-likelihood.  logE) < 0.2 <7.1

M. Kowalski Vertex reconstruction: Reconstructing position of YAG laser light emitters (position known to ~ 1 m). Energy reconstruction: LEDs (UV 370 nm) run at different intensities. Reconstructing energy of LED events (20 % resolution). Absolute intensity not known, but relative Intensities reconstructed correctly. Testing Reconstruction with In-Situ Light Sources data mc

M. Kowalski The cascade filter Final cut Starting with 1.2 x 10 9 events (in the 2000 data set) 7 cuts to reduce background The full likelihood reconstruction is performed after cut # 2

M. Kowalski Final cut variable Variables merged into one “Bayesian Discriminator” (thereby neglecting correl.) [m]

M. Kowalski Optimizing the Final Cut in L-logE space Cuts are optimized on MC to obtain best sensitivity. Sensitivity is defined as average upper limit on:  (E)= const x E -2 / (GeV s sr cm 2 ) L-logE space scanned and sensitivity calculated (performing a counting rate experiment)

M. Kowalski Final energy spectrum Energy cut chosen by MC Optimization 2 events passed all cuts BackgroundExpectation Atmospheric muons Conventional atmospheric Prompt charm Sum (w/o charm)

M. Kowalski The highest energy event (~200 TeV) 300 m

M. Kowalski Effective Volume for e,  and 

M. Kowalski Upper limits on the diffuse flux N obs =2; N bg = Upper bounds on the diffuse flux of astrophysical neutrinos (at 90% CL) for different assumed spectras:    Limit on tau neutrinos % worse than for electron neutrinos Glashow resonance at 6.3 PeV results in differential e limit [ ]  =3.0,2.5,2.0,1.5,1.0

M. Kowalski [ Comparision with other Limits and Models Model e e      1e-6 x E SSDS (92) SS QC (95) SS BJ (95) P p  (96) MPR (98) units: model rejection factor * assuming a flavor ratio 1:1:1 SSDS MPR Preliminary (2000 data) [ [

M. Kowalski Conclusions  Cascades interacting within AMANDA can be reconstructed with a resolutions:  x,y,z =5 m,   =30 o - 40 o and  logE =  A search for neutrino-induced cascades in the data of the first year of AMANDA II was performed. No significant excess over background was seen!  Upper limits set on the diffuse flux of neutrinos, ruling out several AGN flux models.  AMANDA can be considered an all flavor neutrino detector!

M. Kowalski Back Up

M. Kowalski Angular detector sensitivity nearly uniform. Depletion due to propagation through the earth. Example: 1 PeV

M. Kowalski

M. Kowalski The AMANDA detector at the South Pole Instalation of 10 strings in 1996/97 (referred to as AMANDA-B10) Comissioning of AMANDA II in 2000 consisting of 19 strings and 677 OMs Detector deployed ~2 km deep into Antarctic ice

M. Kowalski First Level Cascade Filter Late hits (but causal) Direct hits: c (t i -200 ns) < d < c t i Early hits (non causal!): d > c t i The discriminating variables are based on fast estimate of vertex position & time

M. Kowalski First Level Cascade Filter N early /N hits N direct

M. Kowalski Final Level Cascade Filter Energy spectrum of remaining events

M. Kowalski Systematic uncertainty on signal sensitivity Type and size of uncertainty Unertainty in event rate ( e ) Variation of ice models 10 % OM sensitivity (+/- 20 %) 10 % Energy scale (+/- 20 %) 10 % Cut variation 5 % MC Statistics 3 % Shower simulation 1 % Quadratic sum~ 20 %