Search for Dark Matter Captured in the Sun with the IceCube Neutrino Observatory

Search for Dark Matter Captured in the Sun with the IceCube Neutrino Observatory for the IceCube Collaboration The Oscar Klein Centre for Cosmoparticle Physics, Stockholm University 9th International Conference "Identification of Dark Matter, 2012" Chicago, USA on July 23-27, 2012

Results from a search Search for Dark Matter Captured in the Sun with the IceCube Neutrino Observatory in the 79-string configuration for the IceCube Collaboration The Oscar Klein Centre for Cosmoparticle Physics, Stockholm University 9th International Conference "Identification of Dark Matter, 2012" Chicago, USA on July 23-27, 2012

Solar Dark Matter Search with IceCube All processes depend on WIMP mass Annihilation channel (branching ratios) Annihilation cross-section Capture (scattering) Scattering cross-sections (SI & SD) Proposed by: Silk, Olive & Srednicki '85 Gaisser, Steigman & Tilav '86 Freese '86 Krauss, Srednicki & Wilzcek '86 1

Solar Dark Matter Search with IceCube All processes depend on WIMP mass Annihilation channel (branching ratios) Annihilation cross-section Capture (scattering) Scattering cross-sections (SI & SD) 1

Solar Dark Matter Search with IceCube All processes depend on WIMP mass Annihilation channel (branching ratios) main analysis backgrounds: atm. ν Ο (10³ triggering events/day) Annihilation cross-section Capture (scattering) Scattering cross-sections (SI & SD) 1

Solar Dark Matter Search with IceCube All processes depend on WIMP mass main analysis backgrounds: Annihilation channel (branching ratios) atm. ν Ο (10³ triggering events/day) Annihilation cross-section atm. Capture (scattering) Scattering cross-sections (SI & SD) µ Ο (10⁸ triggering events/day) 1

IceCube detector IceCube 86-strings 1.5 km 2.5 km deep typically 125 m spacing between strings (~70 m in DeepCore,10x higher DOM density) 60 Modules per string 1 km 1 Gton instrumented volume O(km) muon tracks from ν µ CC µ O(10m) cascades from ν e CC, low energy ν τ CC, and ν x NC Cherenkov radiation detected by 3D array of optical sensors (DOMs) DeepCore 8 add. densely instrumented strings ν *More details in plenary talk by C. Rott 2

IceCube-79 string analysis details * Analysis for the whole year! Used 317 days livetime w e n (151 days austral winter & 166 days austral summer) * more than 60 billion recorded events At final level ~25000 signal-like events selected in 3 independent samples w e n With DeepCore, analysis reaches neutrino energies as low as 10-20GeV* Up-going 1 No containment Up-going 2 strong containment DC µ ν DC ν µ Down-going 3 strong containment IceCube IceCube IceCube ν * µ DC 3

IceCube-79 string analysis details Dedicated online filters targeting: muon events Low energy muon events (IceCube) DeepCore events DeepCore events (summer) By comparing signal simulation & data, cuts are placed that reduces the content of atmospheric muon events Early analysis cuts: dedicated online-filter offline high-level reconstructions first modest cuts (track quality/direction + containment) Summer 4

IceCube-79 string analysis details By comparing signal simulation & data, cuts are placed that reduces the content of atmospheric muon events Early analysis cuts: dedicated online-filter offline high-level reconstructions first modest cuts (track quality/direction + containment) Signal event topology very different for low & high WIMP mass find geometrical cut to split dataset into 2 non-overlaping datasets 5

Multivariate analysis step (BDT variable) event selection 1 (winter, high energy) event selection 3 (summer, low energy) data atm. ν atm. µ mis -reconstructed atm. µ background signal (scaled) well -reconstructed atm. µ background 1 separate BDT for each event selection training on off-source exp. data + separate signal simulation 6

Multivariate analysis step (final cut applied) event selection 1 (winter, high energy) event selection 3 (summer, low energy) Total Bg data atm. ν atm. µ 1 separate BDT for each event selection training on off-source exp. data + separate signal simulation Optimized final cut on BDT-output: run llh-analysis for various BDT cuts, to determine cut value with best sensitivity (MRF & MDP) 7

Reconstructed zenith (final analysis level) event selection 1 (winter, high energy) event selection 3 (summer, low energy) Total Bg data atm. ν atm. µ 1 separate BDT for each event selection training on off-source exp. data + separate signal simulation Optimized final cut on BDT-output: run llh-analysis for various BDT cuts, to determine cut value with best sensitivity (MRF & MDP) 8

Maximum llh-analysis The observed angle to the Sun is fitted with signal and background pdf:s background expectation from data signal simulation (e.g. 1000 GeV) Angle between event track and direction from the Sun 10

Maximum llh-analysis The observed angle to the Sun is fitted with signal and background pdf:s background expectation from data observation How many signal events can be consistent with the observation? Evaluate shape fit with log likelihood rank (Feldman-Cousins) to construct confidence regions for the number of signal events µs signal simulation (e.g. 1000 GeV) Angle between event track and direction from the Sun where L is the pdf product over the final sample 10

Maximum llh-analysis The observed angle to the Sun is fitted with signal and background pdf:s background expectation from data observation How many signal events can be consistent with the observation? Evaluate shape fit with log likelihood rank (Feldman-Cousins) to construct confidence regions for the number of signal events µs where L is the pdf product over the final sample signal simulation (e.g. 1000 GeV) Angle between event track and direction from the Sun Scale to multiple datasets 10

Unblinding results (expected sensitivity) 11

Unblinding results (events observed) event selection 1 (winter, high energy) event selection 2 (winter, low energy) event selection 3 (summer, low energy) observed events background expectation Upper limit on number of signal events (example) 12

Unblinding results (observed results) 13

Systematic uncertainties Preliminary evaluation of systematic uncertainties Source of uncertainty Estimated effect Neutrino oscillations pending DOM sensetivity spread* pending Interaction cross-section 3.6 7.1 % Muon propagation in ice <1% Ice model* 1 20 % Absolute DOM efficiency* 14 51 % time/position callibration <3 5% Statistical error on signal <2% * Full analysis performed with an alternative signal simulation, including maximum llh-analysis (change in acceptance + PSF) 14

Unblinding results (muon flux limit) 15

Unblinding results (SI-cross-section limit) 16

Unblinding results (SD-cross-section limit) 17

Summary New results from 79-string data (~1y livetime) First Dark Matter analysis including DeepCore First full year-round IceCube solar Dark Matter search No excess of events from the Sun over expected backgrounds New very competitive SD-cross-section limits most stringent limits in large parts of WIMP mass range new LKP limits with same search (not discussed in talk) The near future... IceCube already took data for more than 1 year in the full 86-string configuration 2 more DeepCore strings (even lower energy threshold) many many years of data to come... 18

Additional slides

Multivariate analysis step (BDT variable) event selection 2 (winter, low energy) data signal atm. ν atm. µ 1 separate BDT for each event selection training on off-source exp. data + separate signal simulation 6

Reconstructed zenith & BDT output (final analysis level) event selection 2 (winter, low energy) event selection 2 (winter, low energy) Total Bg data atm. ν atm. µ 1 separate BDT for each event selection training on off-source exp. data + separate signal simulation Optimized final cut on BDT-output: run llh-analysis for various BDT cuts, to determine cut value with best sensitivity (MRF & MDP) 8

Analysis: final cut on BDT-output 1000 GeV hard Still very efficient Optimized final cut on BDT-output: run full llh-analysis for various BDT cuts, to determine the cut value with the best sensitivity: each dataset individually calculate MRF calculate MDP (Punzi) check for many mass/channel combinations Want to find 1 single cut per dataset (robustness rather than fine-tuning)

IceCube 79 string sensitivity Sensitivity extends to WIMP masses of 20 GeV Only 1 year of data Data unblinding soon! also search for UED models (not shown here) ary n i lim pre

New model independent method for theories of new physics (Solar Dark Matter searches)

New SUSY analysis with IceCube What can the muon signal tell me? More details: P.Scott, C.Savage, J. Edsjö & the IceCube Collaboration, arxiv:1207.0810 Roughly: Number how much annihilation is going on in the Sun info on σsd, σsi and <σv> Spectrum sensitive to WIMP mass mχ and branching fractions BF into different annihilation channels χ Direction how likely it is that they come from the Sun In model-independent analyses a lot of this information is either discarded or not given with final limits Goal: Use as much of this information on σsd, σsi, <σv>, mχ and BF (χ ) as possible to directly constrain specific points and regions in WIMP model parameter spaces

Global SUSY analysis with IceCube More details: P.Scott, C.Savage, J. Edsjö & the IceCube Collaboration, arxiv:1207.0810 Include IceCube event level data in a global statistical fit. parameter estimation rather than model exclusion Composite likelihood made up of observations from all over: Dark matter relic density from WMAP Precision electroweak tests at LEP & LEP limits on sparticle masses B-factory data (rare decays, b sγ) Muon anomalous magnetic moment LHC searches, direct detection (not yet included in examples) + IceCube unbinned likelihood

Global SUSY analysis with IceCube More details: P.Scott, C.Savage, J. Edsjö & the IceCube Collaboration, arxiv:1207.0810 CMSSM, IceCube-22 Contours indicate 1σ and 2σ credible regions Grey contours correspond to fit without IceCube data Shading+contours indicate relative probability only, not overall goodness of fit

Global SUSY analysis with IceCube More details: P.Scott, C.Savage, J. Edsjö & the IceCube Collaboration, arxiv:1207.0810 CMSSM, IceCube-22 with 100x boosted effective area (indication for IceCube-79 and 86-string prospects) Contours indicate 1σ and 2σ credible regions Grey contours correspond to fit without IceCube data Shading+contours indicate relative probability only, not overall goodness of fit