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 www.fysik.su.se/ pat
Outline Background 1 Background Introduction and models Dark matter detection 2 3
Outline Background Introduction and models Dark matter detection 1 Background Introduction and models Dark matter detection 2 3
Who needs dark matter? Introduction and models Dark matter detection Anyone wanting to make sense of: 1 rotation curves 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
What the χχχχ is it? Background Introduction and models Dark matter detection Must be: massive (gravitationally-interacting) unable to interact via the electromagnetic force (dark) non-baryonic cold (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 (esp. WIMPless dark matter)
What the χχχχ is it? Background Introduction and models Dark matter detection Must be: massive (gravitationally-interacting) unable to interact via the electromagnetic force (dark) non-baryonic cold (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 (esp. WIMPless dark matter)
What the χχχχ is it? Background Introduction and models Dark matter detection Must be: massive (gravitationally-interacting) unable to interact via the electromagnetic force (dark) non-baryonic cold (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 (esp. WIMPless dark matter)
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 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 off nuclei detection channel Many WIMPs are Majorana particles (own antiparticles) = self-annihilation cross-section
Outline Background Introduction and models Dark matter detection 1 Background Introduction and models Dark matter detection 2 3
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 JWST, VLT
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 JWST, VLT
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 JWST, VLT
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 JWST, VLT E 3 Φ [GeV 2 m -2 s -1 sr -1 ] 10 Positron fraction M DM = 3.65 TeV, Model N3, E F=2500 0.01 0.1 10 100 E e + [GeV] Bergström, Edsjö & Zaharijas 2009 Fermi HESS ( 0.85) HESS LE ( 0.85) Total Background ( 0.85) DM signal 100 1000 Positron energy, E e + [GeV]
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 JWST, VLT E 3 Φ [GeV 2 m -2 s -1 sr -1 ] 10 Positron fraction M DM = 3.65 TeV, Model N3, E F=2500 0.01 0.1 10 100 E e + [GeV] Bergström, Edsjö & Zaharijas 2009 Fermi HESS ( 0.85) HESS LE ( 0.85) Total Background ( 0.85) DM signal 100 1000 Positron energy, E e + [GeV]
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 JWST, VLT E 3 Φ [GeV 2 m -2 s -1 sr -1 ] 10 Positron fraction M DM = 3.65 TeV, Model N3, E F=2500 0.01 0.1 10 100 E e + [GeV] Bergström, Edsjö & Zaharijas 2009 Fermi HESS ( 0.85) HESS LE ( 0.85) Total Background ( 0.85) DM signal 100 1000 Positron energy, E e + [GeV]
Outline Background 1 Background Introduction and models Dark matter detection 2 3
The idea in a nutshell (cartoon version)
The idea in a nutshell (cartoon version)
The idea in a nutshell (cartoon version)
The idea in a nutshell (cartoon version)
The idea in a nutshell (cartoon version)
The idea in a nutshell (cartoon version)
Evolutionary tracks - HR diagram 1.8 M 1.8 M Luminosity, log 10 (L/L ) 1 0 1 ( 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 ) 3 1.4 M 1.0 M 0.8 M 0.6 M ZAMS Luminosity, log 10 (L/L ) 1 0 1 ( 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 ) 1 1.4 M 1.0 M 0.8 M 0.6 M ZAMS 4.0 3.9 3.8 3.7 3.6 ( Effective (surface) temperature, log Teff ) 10 K 4.0 3.9 3.8 3.7 3.6 ( Effective (surface) temperature, log Teff ) 10 K Luminosity, log 10 (L/L ) 1 0 1 ( LW,max log10 Lnuc(0) Z = 0.01 4.11 Gyr 2.0 Gyr 4.30 Gyr 4.05 Gyr 3.4 Gyr 0 yr 3.3 Myr ) 0 1.0 Myr 1.8 M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS Luminosity, log 10 (L/L ) 1.0 0.5 0.0 0.5 1.0 ( LW,max log10 Lnuc(0) Z = 0.01 0 yr ) 1 0.5 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 4.0 3.9 3.8 3.7 3.6 ( Effective (surface) temperature, log Teff ) 10 K 3.9 3.8 3.7 3.6 ( Effective (surface) temperature, log Teff ) 10 K (PS, Fairbairn & Edsjö, MNRAS 2009; Fairbairn, PS, & Edsjö, Phys. Rev. D 2008)
) ) ) ) Background Evolutionary tracks - central equation of state ( Central temperature, log Tc 10 K 7.0 7.1 7.2 7.3 7.4 7.5 2.0 Gyr 1.0 Gyr 0 yr 2.69 Gyr 2.77 Gyr ( LW,max log10 Lnuc(0) Z = 0.01 ) 3 2.82 Gyr 1.8 M 1.4 M 1.0 M 0.8 M 0.6 M ZAMS ( Central temperature, log Tc 10 K 7.0 7.1 7.2 7.3 7.4 7.5 2.1 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 ) 1 2.0 2.5 3.0 3.5 4.0 ( ) ρc Central density, log 10 g cm 3 2.0 2.5 3.0 3.5 4.0 ( ) ρc Central density, log 10 g cm 3 ( Central temperature, log Tc 10 K 6.9 7.0 7.1 7.2 7.3 7.4 7.5 7.6 Z = 0.01 ( ) LW,max log10 Lnuc(0) 0 1.0 Myr 3.3 Myr 3.4 Gyr 2.0 Gyr 0 yr 4.05 Gyr 4.11 Gyr 2.0 2.5 3.0 3.5 ( ) ρc Central density, log 10 g cm 3 4.30 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) 6.7 6.8 6.9 7.0 7.1 7.2 7.3 1.8 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 = 0.01 0 yr ) 1 0.0 0.5 1.0 1.5 2.0 ( ) ρc Central density, log 10 g cm 3
Stars on elliptical orbits at the Galactic centre ] [ LW,max WIMP luminosity, log 10 Lnuc(0) 6 4 2 0 2 0.6 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 = 0.02 3 2 1 0 Modified orbital ellipticity, log 10 (1 e) (PS, Fairbairn & Edsjö, MNRAS 2009; Fairbairn, PS, & Edsjö, Phys. Rev. D 2008)
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 http://www.fysik.su.se/ pat/darkstars
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 http://www.fysik.su.se/ pat/darkstars
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 http://www.fysik.su.se/ pat/darkstars
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 http://www.fysik.su.se/ pat/darkstars
Outline Background 1 Background Introduction and models Dark matter detection 2 3
Gamma-rays from neutralino dark matter Φ annihilation rate ρ 2 DM dn dedω = 1 dn f 8πmχ 2 de σ f v f l.o.s. ρ 2 (Ω, l)dl (1)
Gamma-rays from neutralino dark matter Φ annihilation rate ρ 2 DM dn dedω = 1 dn f 8πmχ 2 de σ f v f l.o.s. ρ 2 (Ω, l)dl (1) 3 main gamma-ray channels:
Gamma-rays from neutralino dark matter Φ annihilation rate ρ 2 DM dn dedω = 1 dn f 8πmχ 2 de σ f v f l.o.s. ρ 2 (Ω, l)dl (1) 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
Gamma-rays from neutralino dark matter 2 photons (or Z+photon): monochromatic lines Φ annihilation rate χ ρ 2 0 1 DM γ dn dedω = 1 dn f 8πmχ 2 de σ f v χ 0 1 f γ/z l.o.s. ρ 2 (Ω, l)dl (1) 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 Internal bremsstrahlung: Secondary decay: hard gamma-ray spectrum 3 main gamma-ray channels: soft(er) continuum spectrum χ 0 χ 0 1 SM 1 SM monchromatic lines SM γ SM internal bremsstrahlung (FSR + VIB) SM γ SM γ χ 0 χ 0 SM 1 SM 1 π
Gamma-rays from neutralino dark matter 2 photons (or Z+photon): monochromatic lines Φ annihilation rate χ ρ 2 0 1 DM γ dn dedω = 1 dn f 8πmχ 2 de σ f v χ 0 1 f γ/z l.o.s. χ 0 1 χ 0 1 Internal bremsstrahlung: hard gamma-ray spectrum γ/z ρ 2 (Ω, l)dl (1) χ 0 1 SM SM γ 2 photons (or Z+photon): monochromatic lines Internal bremsstrahlung: hard gamma-ray spectrum Secondary decay: soft(er) continuum spectrum χ 0 1 χ 0 1 γ γ/z χ 0 1 χ 0 1 γ γ Internal bremsstrahlung: Secondary decay: hard gamma-ray spectrum 3 main gamma-ray channels: soft(er) continuum spectrum χ 0 χ 0 1 SM 1 SM monchromatic lines SM SM internal bremsstrahlung (FSR + VIB) SM γ continuum from secondary decay SM γ χ 0 χ 0 SM 1 SM 1 π χ 0 SM 1 SM SM χ 0 SM 1 SM SM SM π SM γ γ γ
Targets Background 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??
Targets Background 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??
Targets Background 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??
Targets Background 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??
Targets Background 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??
Targets Background 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??
Targets Background 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??
Dark clumps: Ultracompact minihalos Small-scale, large amplitude density perturbations in the early Universe can create ultracompact minihalos (Ricotti & Gould, arxiv:0908.0735) 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, arxiv:0908.4082) Integrated flux above 100 MeV (cm 2 s 1 ) 10 20 10 15 10 10 10 5 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 2009 10 100 1000 WIMP mass (GeV) b b µ + µ b b µ + µ b b µ + µ
Ultracompact minihalos Integrated flux above threshold (cm 2 s 1 ) 10 20 10 15 10 10 10 5 Fermi-LAT CTA/AGIS e + e annihilation epoch QCD phase transition electroweak phase transition Scott & Sivertsson 2009 b b, mχ = 100 GeV b b, mχ = 5 TeV µ + µ, mχ = 100 GeV µ + µ, mχ = 5 TeV b b: no boost µ + µ : boost = 100 VERITAS + MAGIC + HESS Minihalos produced in the e + e annihilation epoch should be visible today by Fermi or HESS, or even in EGRET data Constrains the spectrum of perturbations formed during the e + e annihilation phase Minihalos from the QCD phase transition could soon be detectable also 0.1 1 10 100 10 3 10 4 Energy threshold (GeV) (PS & Sivertsson, Phys. Rev. Lett. 2009, arxiv:0908.4082)
Dwarf galaxies Background Why dwarfs? Very high mass-to-light ratios = lots of DM, little BG High latitude = low BG = arguably the best targets for WIMP gammas Segue 1 100 MeV 300 GeV (Farnier & Fermi-LAT Collaboration, 2009 Fermi Symposium & ApJ submitted)
Outline Background 1 Background Introduction and models Dark matter detection 2 3
Scanning supersymmetric parameter spaces Goal: given a particular version of SUSY, 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 2 1 + 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
Scanning supersymmetric parameter spaces 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
Including Segue 1 in supersymmetric scans Why Segue 1? Close(ish) 23 kpc M/L 1300 (large) Best S/N dwarf for WIMP gammas Leading the pack in Fermi dwarf upper limit analysis Segue 1 100 MeV 300 GeV Purpose: see how Segue observations impact real models, considering soft bounds (i.e. full likelihood function).
Annihilation cross-section (Segue 1 only) No Fermi data 9 months of real data 5 year projection log 10 [ <σ v> (cm 3 s 1 ) ] 24 25 26 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Phys + Nuis only 0.5 1 1.5 m 0 χ1 (TeV) log log 10 [ <σ v> (cm 3 s 1 10 [ <σ v> (cm 3 s 1 ) ] ) ] 24 25 26 24 25 26 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 9 mth, BF=1 0.5 1 1.5 m 0 χ1 (TeV) Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 9 mth, BF=50 0.5 1 1.5 m 0 χ1 (TeV) log log 10 [ <σ v> (cm 3 s 1 10 [ <σ v> (cm 3 s 1 ) ] ) ] 24 25 26 24 25 26 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr, BF=1 0.5 1 1.5 m 0 χ1 (TeV) Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr, BF=50 0.5 1 1.5 m 0 χ1 (TeV) (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP submitted, arxiv:0909.3300)
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 ) ] 24 26 28 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 All other constr. 30 0 0.5 1 m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 26 28 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 9 mth + All, BF=50 30 0 0.5 1 m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 26 28 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF=50 30 0 0.5 1 m 0 (TeV) χ1 (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP submitted, arxiv:0909.3300)
CMSSM parameters after 5 years (Segue 1 + All) 4 Scott et al. 2009 Scott et al. 2009 3 5 m 0 (TeV) 2 1 Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF=50 0 0 2 4 m 1/2 (TeV) A 0 (TeV) 0 5 Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF=50 20 40 60 tan β (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP submitted, arxiv:0909.3300)
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 www.fysik.su.se/ pat/flatlib
Predictions for ultracompact minihalo fluxes in the CMSSM Scott & Sivertsson 2009 1 log 10 [ Φ (cm 2 s 1 ) ] 6 e + e epoch Posterior pdf Relative Probability Density Flat priors 7 CMSSM, µ>0 100 1000 m 0 χ1 (GeV) 0 (PS & Sivertsson, Phys. Rev. Lett. 2009, arxiv:0908.4082)
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)
CMSSM/mSUGRA parameters with GAs GAs find better fits than traditional (Bayesian) methods like nested sampling (χ 2 = 9.35 vs. χ 2 = 13.51). Good fits to all data in horizontal branch / focus point and stau coannihilation regions. Best-fit point is in the focus point region. m 0 (GeV) 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + GA levels 0 500 1000 1500 2000 m 1/ 2 (GeV) m 0 (GeV) 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels 0 500 1000 1500 2000 m 1/ 2 (GeV) (Akrami, PS, Edsjö, Conrad & Bergström, JCAP submitted, arxiv:0909.3300)
CMSSM/mSUGRA parameters with GAs GAs find better fits than traditional (Bayesian) methods like nested sampling (χ 2 = 9.35 vs. χ 2 = 13.51). Good fits to all data in horizontal branch / focus point and stau coannihilation regions. Best-fit point is in the focus point region. m 0 (GeV) m 0 (GeV) 4000 3000 2000 1000 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + GA levels 0 500 1000 1500 2000 m 1/ 2 (GeV) Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels m 0 (GeV) 0 500 1000 1500 2000 0 500 1000 1500 2000 m 1/ 2 (GeV) m 1/ 2 (GeV) 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels (Akrami, PS, Edsjö, Conrad & Bergström, JCAP submitted, arxiv:0909.3300)
CMSSM/mSUGRA parameters with GAs GAs find better fits than traditional (Bayesian) methods like nested sampling (χ 2 = 9.35 vs. χ 2 = 13.51). Good fits to all data in horizontal branch / focus point and stau coannihilation regions. Best-fit point is in the focus point region. m 0 (GeV) m 0 (GeV) 4000 3000 2000 1000 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + GA levels 0 500 1000 1500 2000 m 1/ 2 (GeV) Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels 0 500 1000 1500 2000 m 1/ 2 (GeV) m 0 (GeV) m 0 (GeV) 4000 3000 2000 1000 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + GA levels 0 500 1000 1500 2000 m 1/ 2 (GeV) Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels 0 500 1000 1500 2000 m 1/ 2 (GeV) (Akrami, PS, Edsjö, Conrad & Bergström, JCAP submitted, arxiv:0909.3300)
CMSSM/mSUGRA parameters with GAs GAs find better fits than traditional (Bayesian) methods like nested sampling (χ 2 = 9.35 vs. χ 2 = 13.51). Good fits to all data in horizontal branch / focus point and stau coannihilation regions. Best-fit point is in the focus point region. m 0 (GeV) m 0 (GeV) 4000 3000 2000 1000 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + GA levels 0 500 1000 1500 2000 m 1/ 2 (GeV) Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels 0 500 1000 1500 2000 m 1/ 2 (GeV) m 0 (GeV) m 0 (GeV) 4000 3000 2000 1000 4000 3000 2000 1000 Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + GA levels 0 500 1000 1500 2000 m 1/ 2 (GeV) Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels 0 500 1000 1500 2000 m 1/ 2 (GeV) (Akrami, PS, Edsjö, Conrad & Bergström, JCAP submitted, arxiv:0909.3300)
[ [ 1 1 [ [ 1 1 Background Direct detection with GAs -5 Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + GA levels -5 Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + GA levels -6 XENON10-6 XENON10 (pb)] σ SI 10 p log (pb)] σ SI 10 p log -7-8 -9-10 CDMS-II -11 0 200 400 600 800 1000-5 -6-7 -8-9 m 0 ~ (GeV) χ Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels XENON10 CDMS-II (pb)] σ SI 10 p log (pb)] σ SI 10 p log -7-8 -9-10 CDMS-II -11 0 200 400 600 800 1000-5 -6-7 -8-9 m 0 ~ (GeV) χ Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels XENON10 CDMS-II Best-fit point is actually ruled out by direct detection (under standard halo assumptions). Best-fit coannihlation point still OK. -10-10 -11 0 200 400 600 800 1000 m 0 ~ (GeV) χ -11 0 200 400 600 800 1000 m 0 ~ (GeV) χ (Akrami, PS, Edsjö, Conrad & Bergström, JCAP submitted, arxiv:0909.3300)
3 v [ 3 v [ 1 1 3 v [ 3 v [ 1 1 Background Indirect detection with GAs -24 Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + GA levels -24 Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + GA levels )] -1 (cm s 10 σ log )] -1 (cm s 10 σ log -25-26 -27-28 -29-30 -31 0 200 400 600 800 1000-24 -25-26 -27-28 -29-30 m 0 ~ (GeV) χ Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels -31 0 200 400 600 800 1000 m 0 ~ (GeV) χ )] -1 (cm s 10 σ log )] -1 (cm s 10 σ log -25-26 -27-28 -29-30 -31 0 200 400 600 800 1000-24 -25-26 -27-28 -29-30 m 0 ~ (GeV) χ Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels -31 0 200 400 600 800 1000 m 0 ~ (GeV) χ 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. (Akrami, PS, Edsjö, Conrad & Bergström, JCAP submitted, arxiv:0909.3300)
3 v [ 3 v [ 1 1 3 v [ 3 v [ 1 1 Background Indirect detection with GAs -24 Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + GA levels -24 Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + GA levels )] -1 (cm s 10 σ log )] -1 (cm s 10 σ log -25-26 -27-28 -29-30 -31 0 200 400 600 800 1000-24 -25-26 -27-28 -29-30 m 0 ~ (GeV) χ Akrami, Scott, Edsjö, Conrad & Bergström (2009) GA points + MN levels -31 0 200 400 600 800 1000 m 0 ~ (GeV) χ )] -1 (cm s 10 σ log )] -1 (cm s 10 σ log -25-26 -27-28 -29-30 -31 0 200 400 600 800 1000-24 -25-26 -27-28 -29-30 m 0 ~ (GeV) χ Akrami, Scott, Edsjö, Conrad & Bergström (2009) MN points + MN levels -31 0 200 400 600 800 1000 m 0 ~ (GeV) χ 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. (Akrami, PS, Edsjö, Conrad & Bergström, JCAP submitted, arxiv:0909.3300)
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, JCAP submitted, arxiv:0909.3300)
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! in SUSY present unique technical challenges - genetic algorithms can help tackle them.
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! in SUSY present unique technical challenges - genetic algorithms can help tackle them.
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! in SUSY present unique technical challenges - genetic algorithms can help tackle them.
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! in SUSY present unique technical challenges - genetic algorithms can help tackle them.
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! in SUSY present unique technical challenges - genetic algorithms can help tackle them.
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! in SUSY present unique technical challenges - genetic algorithms can help tackle them.
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 http://www.fysik.su.se/ pat/darkstars
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 64 64 region of interest Segue 1 100 MeV 300 GeV Dark matter halo profile from best-fit Einasto profile from stellar kinematic data (Martinez et al., JCAP 2009)
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 10 10 ) 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)
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. 2009 26 28 Profile likelihood Flat priors CMSSM, µ>0 30 0 All other constr. 0.5 1 m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al. 2009 26 28 Profile likelihood Flat priors CMSSM, µ>0 30 0 Segue 9 mth + All, BF=50 0.5 1 m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al. 2009 26 28 Profile likelihood Flat priors CMSSM, µ>0 30 0 Segue 5 yr + All, BF=50 0.5 1 m 0 (TeV) χ1 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al. 2009 26 28 Posterior pdf Flat priors CMSSM, µ>0 30 0 All other constr. 0.5 1 m 0 (TeV) χ1 1 Relative Probability Density 0 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al. 2009 26 28 Posterior pdf Flat priors CMSSM, µ>0 30 0 Segue 9 mth + All, BF=50 0.5 1 m 0 (TeV) χ1 1 Relative Probability Density 0 log 10 [ <σ v> (cm 3 s 1 ) ] 24 Scott et al. 2009 26 28 Posterior pdf Flat priors CMSSM, µ>0 30 0 Segue 5 yr + All, BF=50 0.5 1 m 0 (TeV) χ1 1 Relative Probability Density 0 (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP submitted, arxiv:0909.3300)
Extras 3: Comparison with posterior PDFs m 0 (TeV) m 0 (TeV) 4 3 2 1 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF=50 0 0 2 4 m 1/2 (TeV) 4 3 2 1 Scott et al. 2009 Posterior pdf Flat priors CMSSM, µ>0 Segue 5 yr + All, BF=50 0 0 2 4 m 1/2 (TeV) Scott et al. 2009 1 Relative Probability Density 0 A 0 (TeV) A 0 (TeV) 5 0 5 5 0 5 Scott et al. 2009 Profile likelihood Flat priors CMSSM, µ>0 Segue 5 yr + All, BF=50 20 40 60 tan β Posterior pdf Flat priors CMSSM, µ>0 Segue 5 yr + All, BF=50 20 40 60 tan β 1 Relative Probability Density 0 (PS, Conrad, Edsjö, Bergström, Farnier & Akrami, JCAP submitted, arxiv:0909.3300)