Use of event-level IceCube data in SUSY scans

Transcription

1 Use of event-level IceCube data in SUSY scans Pat Scott Department of Physics, McGill University July 3, 202 Based on: PS, Chris Savage, Joakim Edsjö and The IceCube Collaboration (esp. Matthias Danninger and Klas Hultqvist), arxiv:207.soon Slides available from patscott

2 Outline How to find DM with neutrino telescopes How to find DM with neutrino telescopes 2

3 Introduction The short version:

4 Introduction The short version: Halo WIMPs crash into the Sun

5 Introduction The short version: Halo WIMPs crash into the Sun 2 Some lose enough energy in the scatter to be gravitationally bound

6 Introduction The short version: Halo WIMPs crash into the Sun 2 Some lose enough energy in the scatter to be gravitationally bound 3 Scatter some more, sink to the core

7 Introduction The short version: Halo WIMPs crash into the Sun 2 Some lose enough energy in the scatter to be gravitationally bound 3 Scatter some more, sink to the core 4 Annihilate with each other, producing neutrinos

8 Introduction The short version: Halo WIMPs crash into the Sun 2 Some lose enough energy in the scatter to be gravitationally bound 3 Scatter some more, sink to the core 4 Annihilate with each other, producing neutrinos 5 Propagate+oscillate their way to the Earth, convert into muons in ice/water

9 Introduction The short version: Halo WIMPs crash into the Sun 2 Some lose enough energy in the scatter to be gravitationally bound 3 Scatter some more, sink to the core 4 Annihilate with each other, producing neutrinos 5 Propagate+oscillate their way to the Earth, convert into muons in ice/water 6 Look for Čerenkov radiation from the muons in IceCube, ANTARES, etc

10 What can the muon signal tell me? Roughly: Number how much annihilation is going on in the Sun = info on σ SD, σ SI and σv Spectrum sensitive to WIMP mass m χ and branching fractions BF into different annihilation channels X 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 σ SD, σ SI, σv, m χ and BF (X) as possible to directly constrain specific points and regions in WIMP model parameter spaces (+LHC+DD+... )

11 What can the muon signal tell me? The focus here is supersymmetry (SUSY) but this talk is on a framework, applicable to any model. Goal: Use as much of this information on σ SD, σ SI, σv, m χ and BF (X) as possible to directly constrain specific points and regions in WIMP model parameter spaces (+LHC+DD+... )

12 What can the muon signal tell me? The focus here is supersymmetry (SUSY) but this talk is on a framework, applicable to any model. All the methods discussed here are available in DarkSUSY 5.: Goal: Use as much of this information on σ SD, σ SI, σv, m χ and BF (X) as possible to directly constrain specific points and regions in WIMP model parameter spaces (+LHC+DD+... )

13 What can the muon signal tell me? The focus here is supersymmetry (SUSY) but this talk is on a framework, applicable to any model. All the methods discussed here are available in DarkSUSY 5.: All IceCube data used are available at (and in DarkSUSY, for convenience) Goal: Use as much of this information on σ SD, σ SI, σv, m χ and BF (X) as possible to directly constrain specific points and regions in WIMP model parameter spaces (+LHC+DD+... )

14 Outline How to find DM with neutrino telescopes How to find DM with neutrino telescopes 2

15 SUSY Scanning with IceCube Simple Likelihood Simplest way to do anything is to make it a counting problem... Compare observed number of events n and predicted number θ for each model, taking into account error σ ɛ on acceptance: L num(n θ BG + θ sig ) = 2πσɛ 0 (θ BG + ɛθ sig ) n e (θ BG+ɛθ sig ) n! [ ɛ exp 2 ( ) ] ln ɛ 2 dɛ. σ ɛ () Nuisance parameter ɛ takes into account systematic errors on effective area, from theory, etc. σ ɛ 20% for IceCube.

16 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal θbg 0 E disp(n i E i ) dpsignal de i (E i, Ξ) de i (3) dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i

17 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts L = L num(n ) with Number of lit channels (energy estimator) L spec,i (N i, Ξ) = θbg dp BG (N i )+ θsignal E disp(n i E dn i and L ang,i ( cos φ i ) = θbg n L spec,i L ang,i (2) i= 0 i ) dpsignal de i (E i, Ξ) de i (3) dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i

18 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts L = L num(n ) with Number of lit channels (energy estimator) L spec,i (N i, Ξ) = θbg dp BG (N i )+ θsignal E disp(n i E dn i and L ang,i ( cos φ i ) = θbg n L spec,i L ang,i (2) i= 0 i ) dpsignal de SUSY parameters i (E i, Ξ) de i (3) dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i

19 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal θbg Predicted signal spectrum (from theory) 0 E disp(n i E i ) dpsignal de i (E i, Ξ) de i (3) dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i

20 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal θbg Predicted signal spectrum (from theory) E disp(n i E (E de i i, Ξ) de i (3) Instrument response function 0 i ) dpsignal dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i

21 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal Observed BG distribution θbg Predicted signal spectrum (from theory) 0 E disp(n i E (E de i i, Ξ) de i (3) Instrument response function i ) dpsignal dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i

22 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal θbg Event arrival angle 0 E disp(n i E i ) dpsignal de i (E i, Ξ) de i (3) dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i

23 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal θbg 0 E disp(n i E i ) dpsignal de i (E i, Ξ) de i (3) dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i Predicted signal direction (δ function at Sun)

24 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal θbg 0 E disp(n i E (E de i i, Ξ) de i (3) Instrument response function i ) dpsignal dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i Predicted signal direction (δ function at Sun)

25 SUSY Scanning with IceCube Full Likelihood Full unbinned likelihood with number (L num ), spectral (L spec ) and angular (L ang ) parts with L spec,i (N i, Ξ) = and L ang,i ( cos φ i ) = L = L num(n ) n L spec,i L ang,i (2) i= θbg dp BG dn i (N i )+ θsignal Observed BG distribution θbg 0 E disp(n i E (E de i i, Ξ) de i (3) Instrument response function i ) dpsignal dp BG ( cos φ i ) + θsignal PSF( cos φ i ) (4) d cos φ i Predicted signal direction (δ function at Sun)

26 SUSY Scanning with IceCube Statistics 0 Why simple IN/OUT analyses are not enough:... Only partial goodness of fit, no measure of convergence, no idea how to generalise to regions or whole space. Frequency/density of models in IN/OUT scans means essentially nothing. More information comes from a global statistical fit. = parameter estimation exercise 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 (only roughly implemented for now)

27 Example: SUSY Scanning with IceCube Global Fits CMSSM, IceCube-22 events m 0 m /2 and m χ 0 nuclear scattering cross-sections Scott, Danninger, Savage, Edsjö & Hultqvist Scott, Danninger, Savage, Edsjö & Hultqvist Scott, Danninger, Savage, Edsjö & Hultqvist 202 IC22 global fit IC22 global fit m0 (TeV) 3 2 Mean IC22 global fit log 0 ( σsi,p/cm 2 ) log 0 ( σsd,p/cm 2 ) Mean Mean m (TeV) m χ 0 (TeV) 0.0 m χ 0 (TeV) Contours indicate σ and 2σ credible regions Grey contours correspond to fit without IceCube data Shading+contours indicate relative probability only, not overall goodness of fit

28 Example: SUSY Scanning with IceCube Global Fits Base Observables Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) 42 Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) 38 Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) Old data Old data m0 (TeV) 3 2 Mean Old data log 0 ( σsi,p/cm 2 ) log 0 ( σsd,p/cm 2 ) Mean Mean m (TeV) m χ 0 (TeV) 0.0 m χ 0 (TeV)

29 Example: SUSY Scanning with IceCube Global Fits Base Observables + XENON-00 Grey contours correspond to Base Observables only Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) 42 Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) 38 Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) XENON00 XENON00 m0 (TeV) 3 2 Mean XENON00 log 0 ( σsi,p/cm 2 ) log 0 ( σsd,p/cm 2 ) Mean Mean m (TeV) m χ 0 (TeV) 0.0 m χ 0 (TeV)

30 Example: SUSY Scanning with IceCube Global Fits Base Observables + XENON-00 + CMS 5 fb Grey contours correspond to Base Observables only m0 (TeV) 3 2 Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) Mean XENON00 +CMS 5 fb log 0 ( σsi,p/cm 2 ) Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) XENON00 +CMS 5 fb Mean log 0 ( σsd,p/cm 2 ) Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) XENON00 +CMS 5 fb Mean m (TeV) m χ 0 (TeV) 0.0 m χ 0 (TeV)

31 Example: SUSY Scanning with IceCube Global Fits Base Observables + XENON-00 + CMS 5 fb + IC22 00 Grey contours correspond to Base Observables only m0 (TeV) 3 2 Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) Mean IC XENON00 +CMS 5 fb log 0 ( σsi,p/cm 2 ) Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) IC XENON00 +CMS 5 fb Mean log 0 ( σsd,p/cm 2 ) Scott, Danninger, Savage, Edsjö, Hultqvist & The IceCube Collab. (202) Mean IC XENON00 +CMS 5 fb m (TeV) m χ 0 (TeV) 0.0 m χ 0 (TeV) CMSSM, IceCube-22 with 00 boosted effective area (kinda like IceCube-86+DeepCore)

32 Example: Model Recovery m0 (TeV) 3 2 Scott, Danninger, Savage, Edsjö & Hultqvist 202 Mean IC22 00 Predicted signal events IC22 00 Scott, Danninger, Savage, Edsjö & Hultqvist 202 Mean True value CMSSM, IceCube signal reconstruction 60 signal events, 500 GeV, χχ W + W m (TeV) m χ 0 (GeV) 42 Scott, Danninger, Savage, Edsjö & Hultqvist Scott, Danninger, Savage, Edsjö & Hultqvist 202 IC22 00 IC22 00 log 0 ( σsi,p/cm 2 ) Mean log 0 ( σsd,p/cm 2 ) Mean Grey contours correspond to reconstruction without energy information m χ 0 (GeV) m χ 0 (GeV)

33 Closing remarks A framework for directly comparing event-level neutrino telescope data to individual points in theory parameter spaces is in place These iclike extensions are available in DarkSUSY 5. IceCube event data has been released in a form digestible by DarkSUSY 5. Direct SUSY analyses of IC79 data are on the way Many models exist that only IC86 will be sensitive to The extensions can be used equally well for non-susy BSM scenarios too

34 Outline Backup Slides 3 Backup Slides

35 Advertisement Backup Slides If you liked any of the plots in this talk... Flat priors pippi v Best fit Mean True value ) 3.5 log 0 (m /TeV 2 log 0 (m0/tev) Best fit Mean True value pippi v Flat priors ) 3.5 log 0 (m /TeV 2 A0 (TeV) Flat priors Prof. likelihood Best fit Mean pippi v tan β Profile likelihood ratio Λ = L/Lmax Pippi parse it, plot it arxiv: Generic pdflatex sample parser, post-processor & plotter

36 CMS 5 fb analyses Backup Slides [GeV] m / = LSP τ CMS Preliminary m(q ~ ) = 500 m(q ~ ) = 000 SS Dilepton Razor L int m(q ~ ) = = 4.98 fb, m(g ~ ) = 500 MT2 m( ~ q) = 2500 s = 7 TeV tan(β)=0 A 0 = 0 GeV µ > 0 m t = 73.2 GeV ~ ± LEP2 l ± LEP2 χ m(g ~ ) = m(g ~ ) = 500 OS Dilepton Multi-Lepton Lepton Jets+MHT Non-Convergent RGE's [GeV] m 0 No EWSB

37 Backup Slides XENON day analysis WIMP-Nucleon Cross Section [cm 2 ] DAMA/Na CoGeNT DAMA/I CDMS (20) CDMS (200) XENON0 (S2 only, 20) EDELWEISS (20) XENON00 (20) observed limit (90% CL) Expected limit of this run: ± σ expected ± 2 σ expected XENON00 (200) Trotta et al Buchmueller et al WIMP Mass [GeV/c ]