Model-based SUSY searches and global parameter fits with IceCube

work by:Matthias Danninger, Joakim Edsjö, Klas Hultqvist, Chris Savage, Pat Scott(associate members for this project)

28/04/11 Madison Collaboration Meeting 2

Content: I. Ambitious goal of this projectII.Examples

analysis I: ANTARES as an example (not perfect but better)analysis II: example from Hess

III. Definition of the unbinned IceCube likelihood for this search, incl. terms: individual number of events, energy and direction

IV. Detector response input from IceCube:→ energy→ direction→ signal efficiency (effective Area)

V. Proposed consistency checksVI. Summary and outlook

Content

28/04/11 Madison Collaboration Meeting 3

Goal of this work

Analysis I (“ in-out” model exclusion analysis):● individual SUSY models are tested for consistency (at some set confidence level) with

IceCube data and identified as allowed or excluded by IceCube. Then we can compare with other existing constraints,

Analysis II (“ global fit” including IceCube):● use nested sampling to explore the CMSSM parameter space

(later maybe other models);● simultaneously fitting other relevant constraints from accelerator bounds, the relic

density, electroweak precision observables, the anomalous magnetic moment of the muon and B-physics;

● full likelihood of each experimental result, including IceCube, is combined in a global fit, to give frequentist confidence intervals and Bayesian credible intervals on SUSY parameters.

Proposed datasets:● Establish method with IC22 result (no impact expected)● use from IC79 dataset on (impact on scans expected)

28/04/11 Madison Collaboration Meeting 4

Antares – example for analysis I

KM3NeT

KM3NeT

Vincent Bertin - CPPM-Marseilleon behalf of the ANTARES CollaborationExcludable in 3 years at 90% CL:

all some none (A

0 varied between -3m

0 and +3m

0 and

tan(β) within indicated slice)

28/04/11 Madison Collaboration Meeting 5

example HESS, Sagittarius dwarf galaxyarXiv:1012.3939v1

● CMSSM parameter space scan using SuperBayeS with MultiNest nested sampling algorithm● frequentist profile likelihood: maximising likelihood in the other dimensions of the parameter space● Bayesian posterior: total posterior (prior times likelihood) is integrated over other dim. of the space● linear priors on the CMSSM parameters (also log. priors will be tested)

profile Llh

posterior pdf

No Sgd NFW cored

NFW coredNo Sgdposterior mean

best fit point

28/04/11 Madison Collaboration Meeting 6

Unbinned Likelihood

for parameter estimation only the difference relative to the best-fit point matters

Analysis I: Analysis 2:

normalisation makes a large difference “ or” the inferred level of agreement between the data and any given model

standard binned Poissonian likelihoodIncl. syst error

probability density for observing and

for the ith event when the true

values are and

28/04/11 Madison Collaboration Meeting 7

Unbinned Likelihood

● standard binned Poissonian likelihood:

● consider a total systematic error (WIMP analysis) that has impact of consistently rescaling the observed number of counts (i.e. a constant percentage systematic error);

● assume a Gaussian form with width σ for the PDF;

● total number of predicted events

background flux events observed outside the analysis region

Energy-dependent effective area A

differential neutrino flux at the detector from WIMP annihilation signal (possible to include solar atm. Neutrino Bg)

28/04/11 Madison Collaboration Meeting 8

Unbinned Likelihood

energy component:Comparing spectra

angular component:Including psf of individual events

individual number component:Probability of observing same number of events as in unblinded result

Bins with constant systematic uncertainty:Currently 4 bins,depending on neutrino energy

'probability of observing the unblinded result', assuming specific signal models

28/04/11 Madison Collaboration Meeting 9

• Nchan distributions are norm. to 1 and given in log10(E) bins of 0.2 as indicated in scatter plot

• Individual histos are written to txt-file for input

Nchan as energy estimator

28/04/11 Madison Collaboration Meeting 10

Nchan as energy estimator

28/04/11 Madison Collaboration Meeting 11

• Higher stats not possible• It is already extremely high

stats (45y lifetime)• used for signal only, the E-

bins with low statistics will not be used as no solar WIMP-signal-nu can have that high E

Nchan as energy estimator

28/04/11 Madison Collaboration Meeting 12

True reco error

Paraboloid error

paraboloid – angular uncertanty

• paraboloid error seems better to use than some sort of median angular error for the observed events, binned per observed energy (Nchan → very difficult)

• Only count events within space angle of 10 degrees

28/04/11 Madison Collaboration Meeting 13

effective Area

• published plot of effArea including systematic uncertainty

• recalculated real limit (without syst.)

• effArea for nu & nu(bar) per bin to asci-file(included per energy bin:1sigma syst., 1sigma stat.)

28/04/11 Madison Collaboration Meeting 14

Scrambled pdf with the 6946 events on top (unblinded positions) plotted in cos(Psi)

Zoom in close to the Sun: same plot as in publication. The red/blue pdf's correspond to the atm.nu hypothesis as Bg only. The black pdf is created from scrambling the 6946 events x-times as described in last slide

IceCube – 22 data

28/04/11 Madison Collaboration Meeting 15

Consistency check

1000 files each with signal content (6, 60 events on top of data)→ check if excluded or not & which e.g. cross-section the strong signal correspnds

6 eventsfrom pdf

60 eventsfrom pdf

28/04/11 Madison Collaboration Meeting 16

Summary & outlook

Inside 10 degrees, events are considered for llhOutside 10 degrees, pdf's of Nchan and Psi are used for background only estimate:

IceCube-22 data is provided for scan in correct format→ likelihood analysis is being implemented and will be thoroughly tested→ plan to publish method paper from IceCube-22 data → Pat Scott will propose a talk at TeVPA in Stockholm about IC22 – scanning results

IceCube-79 data should be more or less directly applicable→ IceCube-79 results expected to have interesting impact on global fit results (model eclusion result within main IC79 results paper)→ IceCube-79 MSSM scanning results as separate paper timely after main results paper (in parallel?)

Additional bonus:→ data format that is used from IceCube for this work, could be used as Format for publishing detector data for solar WIMP searches in general.

###[En] 3[E] 19[cos(phi)] 0.998914[phi uncert] 3.91596###[En] 4[E] 16[cos(phi)] 0.999345[phi uncert] 3.00473###

28/04/11 Madison Collaboration Meeting 17

Thank you

Papers for more information:

Fermi Segue 1 paper, JCAP 01:031 2010

H.E.S.S. Sagittarius paper, arXiv:1012.3939v1

SuperBayes, Contemporary Physics, ISSN 0010-7514 print, arXiv:0803.4089v1(www.superbayes.org)

Global fits in the CMSSM and nested sampling, arXiv:0809.3792v2

About our likelihood definition etc., please contact me danning@fysik.su.se

28/04/11 Madison Collaboration Meeting 18

Full substituted likelihood

28/04/11 Madison Collaboration Meeting 19

IceCube – 22 data

For making the scrambled pdf's for the observed angle to the Sun, the azimuth is randomized, whereas the zenith is taken randomly from distribution below (corresponds to the zenith pdf for 180d for the Sun)

Scrambled pdf with the 6946 events on top (unblinded positions)

28/04/11 Madison Collaboration Meeting 20

13 events inside 3 deg = thesisThese are the nchan pdf's from the scrambled data and atm.nu simulation (blue/red)Zoom in on the 13 events inside the cone on the right

IceCube – 22 data

28/04/11 Madison Collaboration Meeting 21

example for II Fermi-LAT, Segue 1JCAP 01:031 2010

● CMSSM parameter space scan using SuperBayeS with MultiNest nested sampling algorithm● frequentist profile likelihood: maximising likelihood in the other dimensions of the parameter space● Bayesian posterior: total posterior (prior times likelihood) is integrated over other dim. of the space● linear priors on the CMSSM parameters (also log. priors will be tested)

profile Llh

posterior pdf

posterior mean

best fit pointNo Fermi

No Fermi analysis incl.

analysis incl.

5y analysis

5y analysis