The search for particle dark matter
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- Hannah Marsh
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1 Pat Scott Oskar Klein Centre for Cosmoparticle Physics (OKC) & Department of Physics, Stockholm University Collaborators: Joakim Edsjö, Jan Conrad, Lars Bergström, Yashar Akrami (OKC/Stockholm), Sofia Sivertsson (OKC/KTH), Malcolm Fairbairn (King s), Christian Farnier (Montpellier II, LPTA/CNRS-UM2), The Fermi-LAT Collaboration Slides available from pat
2 Outline Background 1 Background Introduction and models Dark matter detection 2 Indirect detection with gamma rays & global fits 3
3 Outline Background Introduction and models Dark matter detection 1 Background Introduction and models Dark matter detection 2 Indirect detection with gamma rays & global fits 3
4 How we know dark matter exists Introduction and models Dark matter detection The only way to consistently explain: 1 rotation curves + vel. dispersions 2 gravitational lensing 3 (Clowe et al., ApJL 2006) cosmological data Large-scale structure (2dF/Chandra/SDSS-BAO) says Ω matter 0.27 BBN says that Ω baryonic 0.04 = Ω non baryonic 5 Ω baryons CMB (WMAP) and SN1a agree; also indicate that Ω total 1 = universe is 23% dark matter, 4% baryonic (visible) matter, 73% something else
5 What we know about it Introduction and models Dark matter detection Must be: massive (gravitationally-interacting) unable to interact via the electromagnetic force (dark) non-baryonic cold(ish) (in order to allow structure formation) stable on cosmological timescales produced with the right relic abundance in the early Universe. Good options: Weakly Interacting Massive Particles (WIMPs) sterile neutrinos gravitinos axions axinos hidden sector dark matter (e.g. WIMPless dark matter)
6 What we know about it Introduction and models Dark matter detection Must be: massive (gravitationally-interacting) unable to interact via the electromagnetic force (dark) non-baryonic cold(ish) (in order to allow structure formation) stable on cosmological timescales produced with the right relic abundance in the early Universe. Good options: Weakly Interacting Massive Particles (WIMPs) sterile neutrinos Bad options: gravitinos primordial black holes axions MAssive Compact Halo Objects (MACHOs) axinos standard model neutrinos hidden sector dark matter (e.g. WIMPless dark matter)
7 What we know about it Introduction and models Dark matter detection Must be: massive (gravitationally-interacting) unable to interact via the electromagnetic force (dark) non-baryonic cold(ish) (in order to allow structure formation) stable on cosmological timescales produced with the right relic abundance in the early Universe. Good options: Weakly Interacting Massive Particles (WIMPs) sterile neutrinos Bad options: gravitinos primordial black holes axions MAssive Compact Halo Objects (MACHOs) axinos standard model neutrinos hidden sector dark matter (e.g. WIMPless dark matter)
8 WIMPs at a glance Background Introduction and models Dark matter detection Dark because no electromagnetic interactions Cold because very massive ( 10 GeV to 10 TeV) Non-baryonic and stable - no problems with BBN or CMB Weak-scale annihilation cross-sections naturally lead to a relic abundance of the right order of magnitude (Kolb & Turner 1990)
9 WIMPs at a glance Background Introduction and models Dark matter detection Many theoretically well-motivated particle candidates Supersymmetric (SUSY) neutralinos χ if R-parity is conserved - lightest mixture of neutral higgsinos and gauginos Inert Higgses - extra Higgs in the Standard Model Kaluza-Klein particles - extra dimensions right-handed neutrinos, sneutrinos, other exotic things... Weak interaction means scattering with nuclei detection channel Many WIMPs are Majorana particles (own antiparticles) = self-annihilation cross-section
10 Outline Background Introduction and models Dark matter detection 1 Background Introduction and models Dark matter detection 2 Indirect detection with gamma rays & global fits 3
11 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON
12 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON
13 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON
14 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON
15 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Cross-section [cm 2 ] (normalised to nucleon) WIMP Mass [GeV/c 2 ] Gaitskell,Mandic,Filippini DATA listed top to bottom on plot CRESST kg-day CaWO4 ZEPLIN III (Dec 2008) result CDMS: (reanalysis) Ge XENON (Net 136 kg-d) SuperCDMS (Projected) 2-ST@Soudan Trotta et al 2008, CMSSM Bayesian: 68% contour Trotta et al 2008, CMSSM Bayesian: 95% contour
16 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Direct production missing p T or otherwise LHC, Tevatron
17 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Direct production missing p T or otherwise LHC, Tevatron Event : Run : EventType : DATA Unpresc: 33,34,35,4,6,38,10,11,44,48,50,19,20,52,53,23,55,24,26,60,29,30,62,31 Presc: 33,34,4,6 Missing Et Et=189.5 phi=3.9 List of Tracks Id pt phi eta Cdf Tracks: first To select track type SelectCdfTrack(Id) Et = GeV Particles: first 5 pdg pt phi eta To list all particles ListCdfParticles() Jets(R = 0.7): first 5 Em/Tot et phi eta To list all jets ListCdfJets() Svt Tracks: first To select track type SelectSvtTrack(Id)
18 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Direct production missing p T or otherwise LHC, Tevatron Indirect detection annihilations producing positrons PAMELA, Fermi, ATIC, AMS gamma-rays Fermi, HESS, CTA anti-protons PAMELA, AMS anti-deuterons GAPS neutrinos IceCube, ANTARES
19 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Direct production missing p T or otherwise LHC, Tevatron Indirect detection annihilations producing positrons PAMELA, Fermi, ATIC, AMS gamma-rays Fermi, HESS, CTA anti-protons PAMELA, AMS 100 anti-deuterons GAPS PAMELA neutrinos IceCube, ANTARES E 3 Φ [GeV 2 m -2 s -1 sr -1 ] M DM = 3.65 TeV, Model N3, E F= Positron fraction E e + [GeV] Bergström, Edsjö & Zaharijas 2009 Fermi HESS ( 0.85) HESS LE ( 0.85) Total Background ( 0.85) DM signal Positron energy, E e + [GeV]
20 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Direct production missing p T or otherwise LHC, Tevatron Indirect detection annihilations producing positrons PAMELA, Fermi, ATIC, AMS gamma-rays Fermi, HESS, CTA anti-protons PAMELA, AMS 100 anti-deuterons GAPS PAMELA neutrinos IceCube, ANTARES Dark stars JWST, VLT E 3 Φ [GeV 2 m -2 s -1 sr -1 ] M DM = 3.65 TeV, Model N3, E F= Positron fraction E e + [GeV] Bergström, Edsjö & Zaharijas 2009 Fermi HESS ( 0.85) HESS LE ( 0.85) Total Background ( 0.85) DM signal Positron energy, E e + [GeV]
21 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Direct production missing p T or otherwise LHC, Tevatron Indirect detection annihilations producing positrons PAMELA, Fermi, ATIC, AMS gamma-rays Fermi, HESS, CTA anti-protons PAMELA, AMS 100 anti-deuterons GAPS PAMELA neutrinos IceCube, ANTARES Dark stars JWST, VLT E 3 Φ [GeV 2 m -2 s -1 sr -1 ] M DM = 3.65 TeV, Model N3, E F= Positron fraction E e + [GeV] Bergström, Edsjö & Zaharijas 2009 Fermi HESS ( 0.85) HESS LE ( 0.85) Total Background ( 0.85) DM signal Positron energy, E e + [GeV]
22 Ways to detect WIMPs Introduction and models Dark matter detection Direct detection nuclear collisions and recoils CDMS, DAMA, XENON Direct production missing p T or otherwise LHC, Tevatron Indirect detection annihilations producing positrons PAMELA, Fermi, ATIC, AMS gamma-rays Fermi, HESS, CTA anti-protons PAMELA, AMS 100 anti-deuterons GAPS PAMELA neutrinos IceCube, ANTARES Dark stars JWST, VLT E 3 Φ [GeV 2 m -2 s -1 sr -1 ] M DM = 3.65 TeV, Model N3, E F= Positron fraction E e + [GeV] Bergström, Edsjö & Zaharijas 2009 Fermi HESS ( 0.85) HESS LE ( 0.85) Total Background ( 0.85) DM signal Positron energy, E e + [GeV]
23 Outline Background 1 Background Introduction and models Dark matter detection 2 Indirect detection with gamma rays & global fits 3
24 The idea in a nutshell (cartoon version)
25 The idea in a nutshell (cartoon version)
26 The idea in a nutshell (cartoon version)
27 The idea in a nutshell (cartoon version)
28 The idea in a nutshell (cartoon version)
29 The idea in a nutshell (cartoon version)
30 The idea in a nutshell (cartoon version)
31 Evolutionary tracks - examples Luminosity, log 10 (L/L ) Z = 0.01 ( ) ρχ log 10 GeV cm = 5 ( 3 ) ρχ log 10 GeV cm = 9 ( 3 ) ρχ log 10 GeV cm = 10 3 ZAMS ( Effective (surface) temperature, log Teff ) 10 K
32 Evolutionary tracks - grids 1.8 M 1.8 M Luminosity, log 10 (L/L ) ( LW,max log10 Lnuc(0) 2.77 Gyr 2.82 Gyr 2.69 Gyr 1.0 Gyr 2.0 Gyr 0 yr Z = 0.01 ) M 1.0 M 0.8 M 0.6 M ZAMS Luminosity, log 10 (L/L ) ( LW,max log10 Lnuc(0) 3.0 Gyr 3.1 Gyr 2.9 Gyr 1.0 Gyr 2.1 Gyr 0 yr Z = 0.01 ) M 1.0 M 0.8 M 0.6 M ZAMS ( Effective (surface) temperature, log Teff ) 10 K ( Effective (surface) temperature, log Teff ) 10 K Luminosity, log 10 (L/L ) ( LW,max log10 Lnuc(0) Z = Gyr 2.0 Gyr 4.30 Gyr 4.05 Gyr 3.4 Gyr 0 yr 3.3 Myr ) Myr 1.8 M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS Luminosity, log 10 (L/L ) ( LW,max log10 Lnuc(0) Z = yr ) Myr 1.0 Myr 2.0 Myr 44 Myr 5.0 Myr 1.8 M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS ( Effective (surface) temperature, log Teff ) 10 K ( Effective (surface) temperature, log Teff ) 10 K (PS, Fairbairn & Edsjö, MNRAS 2009; Fairbairn, PS, & Edsjö, Phys. Rev. D 2008)
33 ) ) ) ) Background Evolutionary tracks - central equation of state ( Central temperature, log Tc 10 K Gyr 1.0 Gyr 0 yr 2.69 Gyr 2.77 Gyr ( LW,max log10 Lnuc(0) Z = 0.01 ) Gyr 1.8 M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS ( Central temperature, log Tc 10 K Gyr 1.0 Gyr 0 yr 2.9 Gyr 3.0 Gyr 3.1 Gyr 1.8 M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS ( LW,max log10 Lnuc(0) Z = 0.01 ) ( ) ρc Central density, log 10 g cm ( ) ρc Central density, log 10 g cm 3 ( Central temperature, log Tc 10 K Z = 0.01 ( ) LW,max log10 Lnuc(0) Myr 3.3 Myr 3.4 Gyr 2.0 Gyr 0 yr 4.05 Gyr 4.11 Gyr ( ) ρc Central density, log 10 g cm Gyr 1.8 M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS ( Central temperature, log Tc 10 K 44 Myr (PS, Fairbairn & Edsjö, MNRAS 2009; Fairbairn, PS, & Edsjö, Phys. Rev. D 2008) M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS 5.0 Myr 2.0 Myr 1.0 Myr 0.5 Myr ( LW,max log10 Lnuc(0) Z = yr ) ( ) ρc Central density, log 10 g cm 3
34 Stars on elliptical orbits at the Galactic centre ] [ LW,max WIMP luminosity, log 10 Lnuc(0) M, AC+spike 1.0 M, AC+spike 1.5 M, AC+spike 0.6 M, NFW+spike 1.0 M, NFW+spike 1.5 M, NFW+spike P = 10 yr Z = Modified orbital ellipticity, log 10 (1 e) (PS, Fairbairn & Edsjö, MNRAS 2009; Fairbairn, PS, & Edsjö, Phys. Rev. D 2008)
35 Finding dark stars at the Galactic centre Finding dark stars near the Galactic Centre seems quite possible - not S stars, but low-mass counterparts Any observation of normal stars on these orbits, of a solar mass or below, would provide constraints upon the dark matter density profile at the GC the WIMP mass and spin-dependent nuclear-scattering cross-section - competitive with current direct detection sensitivities VLT/ELT/TMT/GMT observations should reach the required sensitivity in 5 yr, JWST might be useful also for seeing dark stars in the Early Universe DARKSTARS code is publicly available from pat/darkstars
36 Finding dark stars at the Galactic centre Finding dark stars near the Galactic Centre seems quite possible - not S stars, but low-mass counterparts Any observation of normal stars on these orbits, of a solar mass or below, would provide constraints upon the dark matter density profile at the GC the WIMP mass and spin-dependent nuclear-scattering cross-section - competitive with current direct detection sensitivities VLT/ELT/TMT/GMT observations should reach the required sensitivity in 5 yr, JWST might be useful also for seeing dark stars in the Early Universe DARKSTARS code is publicly available from pat/darkstars
37 Finding dark stars at the Galactic centre Finding dark stars near the Galactic Centre seems quite possible - not S stars, but low-mass counterparts Any observation of normal stars on these orbits, of a solar mass or below, would provide constraints upon the dark matter density profile at the GC the WIMP mass and spin-dependent nuclear-scattering cross-section - competitive with current direct detection sensitivities VLT/ELT/TMT/GMT observations should reach the required sensitivity in 5 yr, JWST might be useful also for seeing dark stars in the Early Universe DARKSTARS code is publicly available from pat/darkstars
38 Finding dark stars at the Galactic centre Finding dark stars near the Galactic Centre seems quite possible - not S stars, but low-mass counterparts Any observation of normal stars on these orbits, of a solar mass or below, would provide constraints upon the dark matter density profile at the GC the WIMP mass and spin-dependent nuclear-scattering cross-section - competitive with current direct detection sensitivities VLT/ELT/TMT/GMT observations should reach the required sensitivity in 5 yr, JWST might be useful also for seeing dark stars in the Early Universe DARKSTARS code is publicly available from pat/darkstars
39 Outline Background 1 Background Introduction and models Dark matter detection 2 Indirect detection with gamma rays & global fits 3
40 Population III dark stars First stars form in the centres of 10 6 M dark halos Collapsing gas steepens the potential, draws in dark matter First stars end up with large ρ DM in their cores Heating from annihilation can overcome gas cooling and prevent collapse Spolyar, Freese & Gondolo, Phys. Rev. Lett. 2008; Iocco et al, MNRAS 2008
41 Detecting Population III dark stars with JWST Individual dark stars not detectable with JWST unless viewed through a gravitational lens, e.g. cluster MACS J (µ = 160) Very red spectra = distinguishable from other objects Creates unique signature in integrated galaxy spectra 20 a) z = JWST detection limits m AB F λ (W µm 1 ) log 10 λ (micron) Wavelength (µm) Zackrisson, PS, Rydberg, Iocco, Edvardsson, Östlin, Sivertsson, Zitrin, Broadhurst & Gondolo, arxiv:
42 Outline Background 1 Background Introduction and models Dark matter detection 2 Indirect detection with gamma rays & global fits 3
43 Gamma-rays from dark matter 3 main gamma-ray channels:
44 Gamma-rays from dark matter 2 photons (or Z+photon): monochromatic lines χ 0 1 γ χ 0 1 γ/z χ 0 1 Internal bremsstrahlung: hard gamma-ray spectrum 3 main gamma-ray channels: SM monchromatic lines γ χ 0 1 SM Secondary decay: soft(er) continuum spectrum χ 0 1 SM SM γ Pat Scott Mar 25 Lund SM Astro/Theoretical Colloquium
45 γ Gamma-rays from dark matter Background 2 photons (or Z+photon): Selected monochromatic results lines χ 0 1 χ 0 1 γ/z 2 photons (or Z+photon): monochromatic lines Internal bremsstrahlung: hard gamma-ray spectrum χ 0 1 γ χ 0 1 SM γ χ 0 1 γ/z χ 0 1 SM χ 0 1 χ 0 1 Internal bremsstrahlung: hard gamma-ray spectrum Secondary decay: soft(er) continuum spectrum SM γ χ 0 1 Secondary decay: soft(er) continuum spectrum 3 main gamma-ray χ channels: 0 SM 1 SM monchromatic lines SM internal bremsstrahlung (FSR + VIB) SM SM SM SM π γ γ γ χ 0 1 SM SM γ Pat Scott Mar 25 Lund SM Astro/Theoretical Colloquium
46 γ Gamma-rays from dark matter Internal bremsstrahlung: Background 2 photons (or Z+photon): hard gamma-ray spectrum Selected monochromatic results lines χ 0 1 χ 0 1 SM γ χ 0 1 γ/z χ 0 1 SM 2 photons (or Z+photon): monochromatic lines Internal bremsstrahlung: hard gamma-ray spectrum Secondary decay: soft(er) continuum spectrum χ 0 1 γ χ 0 1 SM χ 0 1 SM SM SM γ γ SM χ 0 1 χ 0 1 χ 0 1 Internal bremsstrahlung: hard gamma-ray spectrum γ/z Secondary decay: soft(er) continuum spectrum χ 0 1 Secondary decay: soft(er) continuum spectrum 3 main gamma-ray χ channels: 0 SM 1 SM monchromatic lines SM internal bremsstrahlung (FSR + VIB) SM γ continuum from secondary decay SM γ χ 0 SM SM 1 π SM SM γ γ χ 0 1 SM SM π γ γ χ 0 1 SM SM γ Pat Scott Mar 25 Lund SM Astro/Theoretical Colloquium
47 Targets Background Φ annihilation rate ρ 2 DM Likely targets: Galactic centre - large signal, large BG Galactic halo - moderate signal, moderate BG dark clumps - low statistics, low BG dwarf galaxies - low statistics, low BG clusters/extragalactic - large modelling uncertainties, low signal, BG??
48 Targets Background Φ annihilation rate ρ 2 DM Likely targets: Galactic centre - large signal, large BG Galactic halo - moderate signal, moderate BG dark clumps - low statistics, low BG dwarf galaxies - low statistics, low BG clusters/extragalactic - large modelling uncertainties, low signal, BG??
49 Targets Background Φ annihilation rate ρ 2 DM Likely targets: Galactic centre - large signal, large BG Galactic halo - moderate signal, moderate BG dark clumps - low statistics, low BG dwarf galaxies - low statistics, low BG clusters/extragalactic - large modelling uncertainties, low signal, BG??
50 Targets Background Φ annihilation rate ρ 2 DM Likely targets: Galactic centre - large signal, large BG Galactic halo - moderate signal, moderate BG dark clumps - low statistics, low BG dwarf galaxies - low statistics, low BG clusters/extragalactic - large modelling uncertainties, low signal, BG??
51 Targets Background Φ annihilation rate ρ 2 DM Likely targets: Galactic centre - large signal, large BG Galactic halo - moderate signal, moderate BG dark clumps - low statistics, low BG dwarf galaxies - low statistics, low BG clusters/extragalactic - large modelling uncertainties, low signal, BG??
52 Targets Background Φ annihilation rate ρ 2 DM Likely targets: Galactic centre - large signal, large BG Galactic halo - moderate signal, moderate BG dark clumps - low statistics, low BG dwarf galaxies - low statistics, low BG clusters/extragalactic - large modelling uncertainties, low signal, BG??
53 Targets Background Φ annihilation rate ρ 2 DM Likely targets: Galactic centre - large signal, large BG Galactic halo - moderate signal, moderate BG dark clumps - low statistics, low BG dwarf galaxies - low statistics, low BG clusters/extragalactic - large modelling uncertainties, low signal, BG??
54 Dark clumps: Ultracompact minihalos Small-scale, large amplitude density perturbations in the early Universe can create ultracompact minihalos (Ricotti & Gould, arxiv: ) Known phase transitions could have generated the enhanced perturbations Clump mass is set by horizon scale at time of transition = specific clump mass scale Non-baryonic, diffuse MACHOs Also excellent indirect detection targets (PS & Sivertsson, Phys. Rev. Lett. 2009) Integrated flux above 100 MeV (cm 2 s 1 ) EGRET e + e annihilation epoch QCD phase transition electroweak phase transition F = 10 2 F = 10 3 No AC Fermi-LAT b b: no boost µ + µ : boost = 100 Scott & Sivertsson WIMP mass (GeV) b b µ + µ b b µ + µ b b µ + µ
55 Annihilation cross-section (all observables + Segue 1) No Fermi data 9 months of real data 5 year projection log 10 [ <σ v> (cm 3 s 1 ) ] Scott et al Profile likelihood Flat priors CMSSM, µ>0 All other constr m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] Scott et al Profile likelihood Flat priors CMSSM, µ>0 Segue 9 mth + All, BF= m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] Scott et al Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF= m 0 (TeV) χ1 (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP 2010)
56 Scanning supersymmetric parameter spaces Goal: given a particular version of supersymmetry, determine which parameter combinations fit all experiments, and how well Issue 1: Combining fits to different experiments Easy composite likelihood (L 1 L 2 χ χ2 2 ) 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 Issue 2: Finding the points with the best likelihoods Tough grid scans, MCMCs, nested sampling or genetic algorithms Public codes: SuperBayeS, SFitter, Fittino
57 Global fits with Fermi & Segue 1 Existing 9 month dataset does constrain supersymmetry by itself, but only weakly 5 years of data will provide significantly better constraints, but... Only *just* good enough to start impacting models which are not already disfavoured by other constraints (eg relic density) In the event of a signal from a dwarf or the Galactic Centre, we can zero in on the preferred supersymmetric model and cross-section very quickly, and provide confidence intervals Results are consistent with a simple upper limit analysis FLATLIB source freely available from pat/flatlib
58 Genetic Algorithms (GAs) Evolutionary algorithms based on natural selection Individuals (points in the parameter space) are selected and cross-bred to create offspring (new points) Selection and breeding occur according to ranking by a fitness function (the likelihood in our case) Do not use the likelihood gradient = good for messy parameters spaces, with e.g. holes, spikes, etc. Scale better than MCMCs/nested sampling with dimensionality Highly optimised for finding maxima (frequentist) rather than mapping the full likelihood surface/integral (Bayesian)
59 [ 1 [ 1 Background Direct detection with GAs GAs find better fits than traditional (Bayesian) methods like nested sampling (χ 2 = 9.35 vs. χ 2 = 13.51). Best-fit point is actually ruled out by direct detection (under standard halo assumptions). Secondary maximum still OK. (pb)] σ SI 10 p log Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels XENON10 CDMS-II m 0 ~ (GeV) χ (pb)] σ SI 10 p log Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels XENON10 CDMS-II m 0 ~ (GeV) χ (Akrami, PS, Edsjö, Conrad & Bergström, JHEP in press, arxiv: )
60 3 v [ 1 3 v [ 1 Background Indirect detection with GAs )] -1 (cm s 10 σ log Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels m 0 ~ (GeV) χ )] -1 (cm s 10 σ log Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels m 0 ~ (GeV) χ (Akrami, PS, Edsjö, Conrad & Bergström, JHEP in press, arxiv: ) Global best-fit point should be probed soon by Fermi. The GA turns up a new region at moderate σv, around 400 GeV. This region is a high-m 0 stau coannihilation region, apparently missed in other scans.
61 3 v [ 1 3 v [ 1 Background Indirect detection with GAs )] -1 (cm s 10 σ log Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels m 0 ~ (GeV) χ )] -1 (cm s 10 σ log Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels m 0 ~ (GeV) χ (Akrami, PS, Edsjö, Conrad & Bergström, JHEP in press, arxiv: ) Global best-fit point should be probed soon by Fermi. The GA turns up a new region at moderate σv, around 400 GeV. This region is a high-m 0 stau coannihilation region, apparently missed in other scans.
62 Mass predictions for the LHC with GAs Best-fit point gives: Lightest neutralino mass 140 GeV Higgs mass 115 GeV Gluino mass 900 GeV Akrami, Scott, Edsjö, Conrad & Bergström (2009) Akrami, Scott, Edsjö, Conrad & Bergström (2009) Akrami, Scott, Edsjö, Conrad & Bergström (2009) PL /PL max PL /PL max PL /PL max m 0 χ ~ 1 (GeV) m h (GeV) m g ~(GeV) (Akrami, PS, Edsjö, Conrad & Bergström, JHEP in press, arxiv: )
63 Background The identity of dark matter can be probed in many complementary ways Stellar evolution can test the nuclear scattering cross-section and self-annihilating property Indirect detection probes masses and self-annihilation cross-sections Ultracompact minihalos present an exciting way to also probe early-universe phase transitions at the same time The different probes can (and should) be put together into global fits to gain a consistent picture. This will be required for a credible detection to be claimed! Global fits in supersymmetry present unique technical challenges - genetic algorithms can help tackle them.
64 Background The identity of dark matter can be probed in many complementary ways Stellar evolution can test the nuclear scattering cross-section and self-annihilating property Indirect detection probes masses and self-annihilation cross-sections Ultracompact minihalos present an exciting way to also probe early-universe phase transitions at the same time The different probes can (and should) be put together into global fits to gain a consistent picture. This will be required for a credible detection to be claimed! Global fits in supersymmetry present unique technical challenges - genetic algorithms can help tackle them.
65 Background The identity of dark matter can be probed in many complementary ways Stellar evolution can test the nuclear scattering cross-section and self-annihilating property Indirect detection probes masses and self-annihilation cross-sections Ultracompact minihalos present an exciting way to also probe early-universe phase transitions at the same time The different probes can (and should) be put together into global fits to gain a consistent picture. This will be required for a credible detection to be claimed! Global fits in supersymmetry present unique technical challenges - genetic algorithms can help tackle them.
66 Background The identity of dark matter can be probed in many complementary ways Stellar evolution can test the nuclear scattering cross-section and self-annihilating property Indirect detection probes masses and self-annihilation cross-sections Ultracompact minihalos present an exciting way to also probe early-universe phase transitions at the same time The different probes can (and should) be put together into global fits to gain a consistent picture. This will be required for a credible detection to be claimed! Global fits in supersymmetry present unique technical challenges - genetic algorithms can help tackle them.
67 Background The identity of dark matter can be probed in many complementary ways Stellar evolution can test the nuclear scattering cross-section and self-annihilating property Indirect detection probes masses and self-annihilation cross-sections Ultracompact minihalos present an exciting way to also probe early-universe phase transitions at the same time The different probes can (and should) be put together into global fits to gain a consistent picture. This will be required for a credible detection to be claimed! Global fits in supersymmetry present unique technical challenges - genetic algorithms can help tackle them.
68 Background The identity of dark matter can be probed in many complementary ways Stellar evolution can test the nuclear scattering cross-section and self-annihilating property Indirect detection probes masses and self-annihilation cross-sections Ultracompact minihalos present an exciting way to also probe early-universe phase transitions at the same time The different probes can (and should) be put together into global fits to gain a consistent picture. This will be required for a credible detection to be claimed! Global fits in supersymmetry present unique technical challenges - genetic algorithms can help tackle them.
69 Extras 1: DarkStars code Lots of options and switches: different velocity distributions, widths, stellar orbits, WIMP conductive transport / internal distribution schemes, particle data, stellar masses and metallicities, numerical options... Save and restart - good for evolving part-way then trying different late-stage scenarios DARKSTARS 2.0 coming soon: conversion to full Z = 0 (new opacities, equation of state) DARKSTARS 1.01 can only do Z = 0 on pre-ms Future options for expansion to include alternative form factors and/or WIMP evaporation DARKSTARS 1.01 publicly available from pat/darkstars
70 Extras 2: Including Segue 1 in supersymmetric scans Same cuts as previous upper-limit analysis DIFFUSE event class 105 zenith angle cut 10 ROI 14 energy bins from 100 MeV 300 GeV Binned Poissonian likelihood Spatial-spectral fit to inner 6 6 bins of region of interest Segue MeV 300 GeV Dark matter halo profile from best-fit Einasto profile from stellar kinematic data (Martinez et al., JCAP 2009)
71 Extras 2: Including Segue 1 in SUSY scans Galactic diffuse BG from preliminary Fermi all-sky GALPROP fits Isotropic powerlaw extragalactic BG (as seen by EGRET) BG normalisations from dwarf UL fits (i.e. full ) Fast integration over energy-dependent IRFs (P6v3) with FLATLIB (dwarf UL analysis skips energy dispersion) Inclusion of systematic errors from effective area and theoretical calculations (dwarf UL analysis skips systematics) Integration into SUPERBAYES, upgraded with DARKSUSY 5 (including internal bremsstrahlung), bug fixes, etc. 515 data points in new global fit, vs 11 previously with SUPERBAYES 1.35 (admittedly not such a fair comparison)
72 Extras 3: Comparison with posterior PDFs No Fermi data 9 months of real data 5 year projection log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al Profile likelihood Flat priors CMSSM, µ> All other constr m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al Profile likelihood Flat priors CMSSM, µ> Segue 9 mth + All, BF= m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al Profile likelihood Flat priors CMSSM, µ> Segue 5 yr + All, BF= m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al Posterior pdf Flat priors CMSSM, µ> All other constr m 0 (TeV) χ1 1 Relative Probability Density 0 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al Posterior pdf Flat priors CMSSM, µ> Segue 9 mth + All, BF= m 0 (TeV) χ1 1 Relative Probability Density 0 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al Posterior pdf Flat priors CMSSM, µ> Segue 5 yr + All, BF= m 0 (TeV) χ1 1 Relative Probability Density 0 (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP 2010)
73 Extras 3: Comparison with posterior PDFs m 0 (TeV) m 0 (TeV) Scott et al Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF= m 1/2 (TeV) Scott et al Posterior pdf Flat priors CMSSM, µ>0 Segue 5 yr + All, BF= m 1/2 (TeV) Scott et al Relative Probability Density 0 A 0 (TeV) A 0 (TeV) Scott et al Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF= tan β Posterior pdf Flat priors CMSSM, µ>0 Segue 5 yr + All, BF= tan β 1 Relative Probability Density 0 (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP 2010)
74 Extras 4: SUSY framework Model: We focus on the Constrained MSSM (CMSSM) m 0 m 1 2 tan β A 0 sgn µ GUT boundary conditions on soft SUSY breaking parameters such that only 4 free parameters and 1 sign remain includes the simplest implementation of msugra scalar mass parameter gaugino mass parameter ratio of Higgs VEVs trilinear coupling Higgs mass parameter (+ve in our scans) Just a testbed framework techniques are applicable to any MSSM parameterisation
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