Milky Way Satellites: Probes of Dark Matter Microphysics
Ethan Nadler 10/7/19 Small Halos as DM Probes
CDM WDM
Lovell et al. 2011 Faint Galaxies as DM Probes
CDM WDM Milky Way Satellites
Classical Boo III CVn II Com SDSS CVn I Boo I Boo II +60 Leo II PS1 Vir I Wil 1 Leo V Boo IV Leo IV Seg 1 DES UMa I Leo I UMi Dra II Crt II Sex DECam Her +30 UMa II Dra HSC Hyd II ATLAS Gaia Ant II
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Sgr Car III Car II Tri II Sgr II Car LMC Pic II Col I 30 Hyi I Pic I Seg 2 Peg III SMC Psc II Ret III Ret II Tuc II Tuc V Gru II Hor I Aqr II Tuc IV Gru I Cet III Tuc III Hor II 60 Phe II For Cet II Scl
Drlica-Wagner et al. in prep. 1. Resimulate Milky Way- like halos from large cosmological volume.
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3. Apply observational selection functions based on imaging data. Markov Chain Monte Carlo 4. Calculate likelihood of observed satellites given galaxy—halo connection parameters.
Nadler et al. in prep. Galaxy-Halo Connection Model
EN, Y.-Y. Mao, G. Green, R. Wechsler 2019 Observational Selection Functions
1.0 3 DES
Boo III Tuc II Tuc IV 2 UMa II Gru II 0.8 Tuc III Ret II Com Seg 2 (r / pc) Boo II Dra IISeg 1 Cet II 10 Wil 1 Tuc V Tri II
log 1 0.6 8 Boo I Col I Detection E ciency Aqr II 2 Ret III Leo IV Peg III Cet III Sgr II Hor I Hor II Vir I CVn II Psc II (r / pc) Phe II Leo V 10 0.2 Gru I Pic I log 1 64 0 0.0 10.0 7.5 5.0 2.5 0.0 10.0 7.5 5.0 2.5 0.0 10.0 7.5 5.0 2.5 0.0 MV MV MV Drlica-Wagner et al. in prep. Mock Satellite Observations simulated “LMC” Mock Satellite Observations simulated “LMC” Mock Satellite Observations simulated “LMC” Minimum Halo Mass Constraints +0.016 ↵ = 1.436 0.019 +0.057 M =0.201 0.000 0.5 0.4 M 0.3 +0.43 50 =7.51 0.00 M 8.5 50 M 8.0 +0.36 =0.70 0.22 B 1.6 B 0.8 Preliminary +0.00 gal =1.00 0.62 0.8 gal 0.4 +9.4 = 32.6 8.3 75 A 50 A 25 +0.15 R =0.26 0.16 0.6 R 0.3 +0.30 n =1.47 0.50 1.6 n 0.8 1.501.451.40 0.3 0.4 0.5 8.0 8.5 0.8 1.6 0.4 0.8 25 50 75 0.3 0.6 0.8 1.6 ↵ M 50 gal R n M B A Nadler et al. in prep. Minimum Halo Mass Constraints +0.016 ↵ = 1.436 0.019 Classical+SDSS+DES +0.057 Classical+SDSS M =0.201 0.000 0.5 0.4 M 0.3 +0.43 50 =7.51 0.00 M 8.5 1.8 50 M 8.0 +0.36 =0.70 0.22 M 1.2 B 1.6 B 0.6 0.8 Preliminary +0.00 gal =1.00 0.62 0.8 gal 8.5 0.4 +9.4 min = 32.6 8.3 75 A M 8.0 50 A 25 +0.15 R =0.26 0.16 1.8 0.6 R 0.3 1.2 +0.30 B n =1.47 0.50 1.6 0.6 n 0.8 1.501.451.40 0.3 0.4 0.5 8.0 8.5 0.8 1.6 0.4 0.8 25 50 75 0.3 0.6 0.8 1.6 1.44 1.36 1.28 0.6 1.2 1.8 8.0 8.5 0.6 1.2 1.8 ↵ M 50 gal R n ↵ M min M B A M B Theoretical Uncertainties Subhalo Number Counts Fit to Satellites 2 109 ] ⇥ M [ 109 50 M 5 108 ⇥ 2 108 ⇥ Upper Limit on 108 Minimal + MW Host + Subhalo + Satellites + Satellites CDM Mass Disruption (Confirmed) (Unconfirmed) Nadler et al. in prep. DM-Baryon Scattering Constraints • Early-time DM-baryon scattering M [M ] suppresses small-scale power 1016 1014 1012 1010 108 1.0 • Mass of the smallest halo allowed to form corresponds to the size of the horizon when 0.75 CDM /P 0.5 mWDM =0.3 keV mWDM =1.2 keV m =4.7 keV collisional WDM • Smallest detected subhalo yields P 27 2 0.25 0 = 10 cm 28 2 (analytic!) cross section limit: 0 = 10 cm 29 2 0 = 10 cm 0.05 0.1 1 5 10 30 50 1 k [h Mpc ] EN, V. Gluscevic, K. Boddy, R. Wechsler 2019 DM-Baryon Scattering Constraints 24 10 CMB Lyman-↵ XQC ] 26 XQC ] 26 2 10 2 10 MiniBoone CR [cm [cm 0 0 28 28 10 XENON1T CR 30 10 Direct Detection cross section cross section 32 10 34 10 34 10 5 4 3 2 1 0 1 2 10 10 10 10 10 10 10 10 dark matter mass m [GeV] EN, V. Gluscevic, K. Boddy, R. Wechsler 2019 DM-Baryon Scattering Constraints 24 10 CMB Lyman-↵ XQC ] 26 2 10 MiniBoone CR [cm 0 28 10 XENON1T CR 30 10 Direct Detection cross section 32 10 34 10 5 4 3 2 1 0 1 2 10 10 10 10 10 10 10 10 dark matter mass m [GeV] EN, V. Gluscevic, K. Boddy, R. Wechsler 2019 DM-Baryon Scattering Constraints 24 10 CMB Lyman-↵ XQC ] 26 2 10 MiniBoone CR [cm 0 28 Milky Way Satellites 10 XENON1T CR 30 10 Direct Detection cross section 32 10 Analytic Estimate Population Analysis 34 10 5 4 3 2 1 0 1 2 10 10 10 10 10 10 10 10 dark matter mass m [GeV] EN, V. Gluscevic, K. Boddy, R. Wechsler 2019 Warm DM Constraints • Minimum detected halo mass 24 10 m = 10 GeV constrains thermal relic WDM: 1 m = 10 GeV 25 3 10 m = 10 GeV ] 2 5 M 0.3 m = 10 GeV hm [cm 26 10 mWDM =2.3 keV 0 109 M 28 • Mapping between WDM and 10 DM-baryon scattering constrains cross section 10 29 any “thermal” DM model: 30 10 0 2 4 6 8 10 T ( ,m ) m mWDM [keV] dec 1 = WDM,1 T ( ,m ) m • Excess radiation decaying to DM after BBN (late-forming DM) suppresses small-scale power • Half-mode scale is related to size of horizon when DM forms: k 1.4 k • Order-of-magnitude improvement on formation redshift constraints: z > 7.8 106 (95% C.L.) • Fuzzy DM can be approximately Armengaud et al. 2017 mapped to WDM: 0.7 21 Mhm m =1.3 10 eV 109 M M 0.2 f =1.7 1017 ⌦1/2 hm GeV 109 M 80 w10 w200 CDM 10 w10 w200 w100 w500 w w 100 500 60 ) vir ] 1 40 g 1 < r/R 2 ( N [cm 20 /m T 0.1 0 Ultra-Faint Satellites 2 CDM /N 1 Classical Satellites 0.01 MW Host Halo SIDM 10 20 30 50 100 300 N 0 0 0.2 0.4 0.6 0.8 1.0 1 v [km s ] r/Rvir S. Adhikari, A. Banerjee, Y.-Y. Mao, EN, R. Wechsler in prep. Future Work • Joint models and constraints of DM properties (plus baryons!), marginalizing over uncertainties in DM models • Example: warm + self-interacting DM constrained by satellite abundances and dynamics LSST Dark Matter White Paper Drlica-Wagner et al. 2019 Future Work Gilman et al. 2019 • Combine DM models and small- scale probes, marginalizing over uncertainties in DM models and baryonic physics • Example: joint constraints from satellites and strong lensing M Susmita Adhikari, Arka Banerjee, Keith Bechtol, Kimberly Boddy, Francis-Yan Cyr-Racine, Subinoy Das, Alex Drlica-Wagner, Vera Gluscevic, Greg Green, Yao-Yuan Mao, Sidney Mau Mitch McNanna, Risa Wechsler Baryonic Subhalo Disruption CDM CDM + BARYONS Garrison-Kimmel et al. 2017 Baryonic Subhalo Disruption • Five subhalo features encode ~90% of disruption • Predicted subhalo populations consistent with FIRE Baryonic Subhalo Disruption Nadler et al. 2018 Artificial Subhalo Disruption van den Bosch & Ogiya 2018 Artificial Subhalo Disruption • Track and reinsert disrupted subhalos by modeling host potential, tidal stripping 500 r<300 kpc 100 ) ) max N 10 Halo 937 16K + Orphans Halo 937 16K 1 Vacc > 10 km s Halo 937 + Orphans 1 Halo 937 Vmax > 10 km s 1 10 20 30 40 20 30 50 100 300 2 16K Resolution orphan Fiducial Resolution 1 N/N 0.5 10 20 30 40 20 30 50 100 300 1 r [kpc] Vmax [km s ] Nadler et al. 2018 Satellite Population Likelihood • Assume that mock and observed satellites are Poisson distributed • Marginalize over unknown rate i i =1 i =5 i = 10 1.0 ) Nˆ = 10 Nˆ = 10 Nˆ = 10 } i ˆ n |{ 0.5 i n ( P 0.0 1.0 ) Nˆ = 1000 Nˆ = 1000 Nˆ = 1000 } i ˆ n |{ 0.5 i Poisson n ( This Work P Jethwa 2018 0.0 0 1 2 3 4 5 0 3 6 9 12 15 0 4 8 12 16 20 ni ni ni Predicted Luminosity Functions Observed Satellites Observed Satellites No LMC No LMC 20 Fiducial (with LMC) 20 Fiducial (with LMC) 10 10 ) ) V V ( 5 ( 5 N N DES PS1 1 1 0 -3 -6 -9 0 -3 -6 -9 MV [Mag] MV [Mag] Nadler et al. in prep DM-Baryon Scattering Constraints Evolution of DM density fluctuations: Evolution of DM temperature: DM-Baryon Scattering Constraints Evolution of DM density fluctuations: Evolution of DM temperature: Momentum transfer rate: f DM-Baryon Scattering Constraints Evolution of DM density fluctuations: Evolution of DM temperature: Heat transfer rate: (SI) m (SI) R0 = R m + m p Evolution of DM density fluctuations: Evolution of DM temperature: • Find thermal decoupling redshift • Evolve DM temp. adiabatically to find kinetic decoupling redshift DM-Baryon Scattering Constraints Evolution of DM density fluctuations: Evolution of DM temperature: • Find thermal decoupling redshift • Evolve DM temp. adiabatically to find kinetic decoupling redshift Modeling Subhalo Suppression Half-mode mass: scale at which subhalo abundance is suppressed Calibrate subhalo mass function on WDM zoom-in simulations 9 10 M • Parameterize transfer function in terms of half-mode scale and large/small-scale slopes: Murgia et al. 2018 • Half-mode constraint translates into generalized DM constraints: MW Satellite Forecast