Milky Way Satellites: Probes of Dark Matter Microphysics
Ethan Nadler 8/7/19 Small Halos as DM Probes
CDM WDM
Lovell et al. 2011 Faint Galaxies as DM Probes
CDM WDM Milky Way Satellites
Boo III CVn II Com Classical CVn I Boo I Boo II Leo II SDSS +60 Vir I Wil 1 Leo V PS1 Boo IV Leo IV Seg 1 UMa I Leo I DES UMi Dra II Crt II Sex DECam Her I +30 UMa II Dra HSC Hyd II ATLAS Gaia Ant II
0
CMa Car III Sgr Car II Tri II Sgr II Car LMC Pic II Col I 30 Hyi I Ind II Pic I Seg 2 Peg III SMC Psc II Ret III Tuc I I Tuc V Ret II Gru II Hor I Eri II Aqr II Tuc IV Gru I Hor II 60 Cet III Tuc I I I Phe II Eri III For Cet II Scl
Credit: Sidney Mau 1. Resimulate Milky Way- like halos from large cosmological volume.
↵
3. Apply observational selection functions based on imaging data. Markov Chain Monte Carlo 4. Calculate likelihood of observed satellites given galaxy—halo connection parameters. Satellite Galaxy-Subhalo Model
Nadler et al. 2019 Baryonic Subhalo Disruption
CDM CDM + BARYONS
Garrison-Kimmel et al. 2017 Observational Selection Function
• Run search algorithms on simulated satellites injected into DES and PS1 data
• Trained model predicts satellite detection probability (publicly available soon!)
Preliminary
DES MWWG in prep. Mock Satellite Observations Mock Satellite Observations Mock Satellite Observations Mock Satellite Observations
simulated “LMC” Minimum Halo Mass Constraints
Classical+SDSS+DES Classical+SDSS
Preliminary 1.8
M 1.2
0.6
8.5 min
M 8.0
1.8
1.2 B
0.6
1.44 1.36 1.28 0.6 1.2 1.8 8.0 8.5 0.6 1.2 1.8 ↵ M min M B Nadler et al. in prep Minimum Halo Mass Constraints 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
• Minimum inferred subhalo mass P 27 2 0.25 0 = 10 cm 28 2 yields cross section limit: 0 = 10 cm 29 2 0 = 10 cm 0.05 0.1 1 5 10 30 50 1 k [h Mpc ]
Nadler et al. 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] Nadler et al. 2019 DM-Baryon Scattering Constraints
24 10 CMB Lyman-↵ XQC ] ] 26 XQC 2 2 10 MiniBoone CR [cm [cm 0 0 28 Milky Way Satellites 10
XENON1T CR 30 10 Direct Detection cross section 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] Nadler et al. 2019 Warm DM Constraints
• Smallest detected halo directly
24 constrains WDM mass assuming 10 m = 10 GeV 1 thermal production: 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
DM-baryon scattering implies cross section 29 constraints on any “thermal” DM 10
model: 30 10 0 2 4 6 8 10 T ( ,m ) m mWDM [keV] dec 1 = WDM,1 T ( ,m ) m
• Excess radiation transitioning to DM after BBN (late-forming DM) suppresses small-scale power
• Universal relation between size of horizon at DM formation and half- mode scale: k 1.4 k
z > 7.8 106 (95% C.L.)
• Universal transfer function parameterization captures half- mode scale, large and small- scale slopes:
Murgia et al. 2018 • Half-mode constraints translate into generalized DM constraints:
MW Satellite Forecast Fuzzy DM Constraints
• Fuzzy DM constraints are limits on curvature and scale of V(Φ): Armengaud et al. 2017
0.7 21 Mhm m =1.3 10 eV 109 M
m > 3 10 21 eV (95% C.L.)
80 w10 w200 CDM 10 w10 w200 w100 w500 w100 w500 60 ) ]
1 ( N g 2 20 [cm T 0.1 1 Vpeak > 20 km s Ultra-Faint Satellites 0 2 CDM Classical Satellites /N 1 0.01 Host Halo SIDM 10 20 30 50 100 300 N 0 25 50 100 150 200 1 v [km s ] r [kpc] Nadler et al. in prep LSST Outlook 24 10 ) 2 CMB 26 Lyman-↵ 10 XQC MiniBoone CR 28 10 Classical + SDSS Satellites LSST Satellites 30 10 XENON1T CR Direct Detection 32 10 Dark Matter Scattering Cross Section (cm 34 10 5 4 3 2 1 0 1 10 10 10 10 10 10 10 Dark Matter Mass (GeV) Drlica-Wagner et al. 2019 Thanks! 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, Anirudh Prabhu, Risa Wechsler Simulating Milky Way Analogs Suite of 50 high-resolution CDM zoom-in simulations Narrow range of MW host mass; wide range of assembly histories Allows marginalization over assembly history, selection on secondary host halo properties (e.g. existence of LMC analog) Mao, Williamson, Wechsler 2015 high concentration low concentration Model Building: Luminosities • Abundance match to GAMA luminosity function (measured down to M 12 ) r ⇠ • Parameters: luminosity function slope + scatter, galaxy formation threshold log(Mpeak/M ) 8 9 10 11 10 ↵ : faint end slope ↵ = 1.3 M =0.2 dex 0 M : luminosity scatter [mag] V M -10 : galaxy formation threshold Mmin -20 7 10 20 50 100 1 Vpeak [km s ] Jethwa et al. 2018 Model Building: Sizes • Does the tight relationship between galaxy size and halo size hold for ultra- faint dwarf satellites? Jiang et al. 2018 • Parameters: accretion vs. present-day size, scatter Sizes Set at z =0 0.01 V Sizes Set at Accretion max MV < 1.5 mag : r10 /2 = r1/2 Vacc ) 2 ⇣ ⌘ / 1 r ( 0.005 P R : size scatter 0 101 102 103 r1/2 [pc] 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 Model Building: Orphan Satellites • 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 Vmax [km s ] r [kpc] 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 Half-Mode Mass Constraints +0.031 ↵ = 1.339 0.043 40 +0.29 M =0.20 0.00 0.9 20 M 0.6 ) 10 0.3 +0.690 V Mhm =7.102 0.095 8.8 hm 8.0 M 7.2 +0.052 =1.195 0.406 B 1.2 Classical + SDSS 1 Confidence Interval 0.8 B 2 Confidence Interval 1 0.4 0 -3 -6 -9 -12 -15 -18 MV [Mag] 1.50 1.35 0.3 0.6 0.9 7.2 8.0 8.8 0.4 0.8 1.2 ↵ M Mhm B 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 LSST Outlook: Warm/Self-Interacting DM Drlica-Wagner et al. 2019