Dark Matter, First Light
Katie Mack North Carolina State University Simons Emmy Noether Fellow, Perimeter
www.astrokatie.com : @AstroKatie entire Standard Model of Particle Physics Dark Matter
Image: dark matter: K. Mack; Andromeda Galaxy: GALEX, JPL-Caltech, NASA What We Don’t Know
Origin / particle type Particle mass Thermal history
Non-trivial evolution? Particle Zoo One component or many? Non-gravitational interactions (self or SM)? Small-scale behavior (mass of smallest halos) Candidates (incomplete list)
✦ Weakly Interacting Massive Particles (WIMPs)
‣ Something not included in the Standard Model of Particle Physics, generally with weak interactions
‣ May be thermally produced (or not) Annihilating (e.g., SUSY neutralino WIMP) Decaying (e.g., sterile neutrino) Warm (WDM) (e.g., axino) Self-interacting (SIDM) (particle + dark sector force) Axion (e.g., QCD axion / string axion) Fuzzy DM (tiny mass, large deBroglie wavelength) MACHO (e.g., primordial black holes) Candidates (incomplete list)
✦ Weakly Interacting Massive Particles (a.k.a., WIMPs)
‣ Something not included in the Standard Model of Particle Physics Annihilating (e.g., SUSY neutralino WIMP) Decaying (e.g., axino) Warm (WDM) (e.g., sterile neutrino) Self-interacting (SIDM) (particle + dark sector force) Axion (e.g., QCD axion / string axion) MACHO (e.g., primordial black holes) Annihilating WIMPS
Key detection signature: ? WIMP annihilation
Why annihilating dark matter? ‣ Good candidates in supersymmetry (e.g. neutralino), Kaluza-Klein theory (e.g. B1) ‣ Early thermal equilibrium and freeze-out gives natural production mechanism WIMP Miracle
Standard thermal WIMP dark matter • freezes out when no longer in thermal equilibrium with baryons • for weak-scale mass and cross-section, predict correct abundance of DM • discovery opportunities: annihilation, scattering, production Kolb & Turner 1990 Dark Matter: Indirect Detection
?
‣ signature: cosmic rays, gamma rays, neutrinos (annihilation products) ‣ results: inconclusive ‣ the future: giant cosmic ray array (CTA), high- resolution gamma-ray astronomy Galactic Center Gamma Rays
Gamma-ray excess in Galactic Center at 1-3 GeV 31-40 GeV WIMP annihilation?
In favor: spatial distribution looks plausible; fairly simple WIMP model, possible new hints seen in Andromeda
Against: Galactic Center is messy; Daylan et al. 2014 complicated analysis Galactic Center Gamma Rays week ending PRL 116, 051103 (2016) PHYSICAL REVIEW LETTERS 5 FEBRUARY 2016
Statistical distribution appears to be more consistent with point sources (probably pulsars) week ending PRL 116, 051103 (2016) PHYSICAL REVIEW LETTERS 5 FEBRUARY 2016
FIG. 2. (Left) Best-fit source-countLee et al. 2016 functions within 10° of the GC and b ≥ 2°, with the 3FGL sources unmasked. The median and 68% confidence intervals are shown for each of the following PS components:j j NFW (dashed, orange), thin disk (solid, blue), and isotropic (dotted, green). The number of observed 3FGL sources in each bin is indicated. The normalization for the diffuse emission in the fit is consistent with that at high latitudes, as desired. (Right) Posteriors for the flux fraction within 10° of the GC with b ≥ 2° arising from the separate PS components, with 3FGL sources unmasked. The inset shows the result of removing the NFW PS templatej j from the fit. Dashed vertical lines indicate the 16th, 50th, and 84th percentiles.
0.70 1.6 template is consistent with other estimates in the literature 2.5−þ0.62 %, while that attributed to NFW DM is 2.2−þ2.2 % [12,14]. The model including the NFW PS contribution is (the flux attributed to isotropic PSs is negligible). When no preferredFIG. 2. over (Left) that Best-fit without source-count by a Bayes functions factor within∼10 10°6 (for of the GCPS and templatesb ≥ 2°, are with included the 3FGL in the sources fit, the unmasked. NFW DM The template median and 68% confidence intervals are shown for each of the following PS components:j j 1.1 NFW (dashed, orange), thin disk (solid, blue), and reference, this corresponds to test statistic 2Δ ln L ≈ 36). absorbs 4.1−þ1.2 % of the total flux. As we will discuss later, isotropicWhen the (dotted, 3FGL green). sources The number are masked, of observed the NPTF 3FGL sources pro- inthis each behavior bin is indicated. agrees with The normalization expectations from for the simulated diffuse emission data. in the fit is consistent with that at high latitudes, as desired. (Right) Posteriors for the flux fraction within 10° of the GC with b 2° arising cedure yields a best-fit source-count function given by the In this statistics-limited regime, the fit does not distinguish≥ from the separate PS components, with 3FGL sources unmasked. The inset shows the result of removing the NFW PS templatej j from the orangefit. Dashed band vertical in the leftlines panel indicate of the Fig. 16th,3. Below 50th, and the 84th break, percentiles.different models for the PS distribution at high significance the source-count function agrees well with that found by (however, if we repeat the analysis using Pass 8 data up to the unmasked fit. In this case, the contributions from the June 3, 2015, corresponding to an average exposure 0.70 1.6 isotropictemplate and is consistent disk-correlated with other PS templates estimates are in negligible. the literature increase2.5−þ0.62 of%∼,30% whileand that a attributed slight improvement to NFW DM in is angular2.2−þ2.2 % The[12,14] flux. fraction The model attributed including to the the NFW NFW PS PS component contribution is is resolution,(the flux the attributed Bayes factor to isotropic in favor PSs of is NFW negligible). PSs increases When no 1.0 6 PS templates2 are4 included in the fit, the NFW DM template 5.preferred3−þ1.1 %, while over the that NFW without DM by template a Bayes absorbs factor ∼ no10 sig-(for from ∼10 to ∼10 in the 3FGL masked analysis; in the 1.1 nificantreference, flux. this corresponds to test statistic 2Δ ln L ≈ 36). 3FGLabsorbs unmasked4.1−þ1.2 % analysis,of the total the flux. Bayes As factor we will in discuss favor of later, InWhen the masked the 3FGL analysis, sources the Bayes are masked, factor for the a model NPTF that pro- NFWthis PSs behavior increases agrees from with∼10 expectations6 to ∼109), but from there simulated is still a data. containscedure yields a NFW a best-fitPS component, source-count relative function to one given that does by the strongIn this preference statistics-limited for unresolved regime, PSs. the The fit does Bayes not factor distinguish for not,orange is ∼10 band2, substantially in the left panel reduced of relative Fig. 3. to Below the result the break, for a modeldifferent with models disk and for isotropic the PS distribution PSs, relative at to high one significance with no thethe unmasked source-count case. function Masking agrees the 3FGL well with sources that removes found by PSs,(however, is ∼106, ifwhile we repeat the Bayes the factor analysis for using a model Pass with 8 data NFW, up to mostthe unmasked of the ROI fit. within In this∼5 case,° of the the GC, contributions reducing photon from the disk,June and 3, isotropic 2015, PSs, corresponding relative to one to with an no average PSs, is ∼ exposure108. statisticsisotropic markedly, and disk-correlated especially for PS any templates signal peaked are negligible. at the Theincrease Bayes factors, of ∼30% best-fitand source-count a slight improvement function parame- in angular GC.The Furthermore, flux fraction attributed in the masked to the NFW ROI, PS non-NFW component PS is ters,resolution, and DM the flux Bayes fractions factor for in favor the 3FGL of NFW masked PSs increases and templates1.0 can absorb a substantial fraction of the excess. unmasked analyses2 are4 summarized in Table I. 5.3−þ1.1 %, while the NFW DM template absorbs no sig- from ∼10 to ∼10 in the 3FGL masked analysis; in the Fornificant example, flux. if only disk and isotropic PS templates are To3FGL validate unmasked the analysis analysis, procedure, the Bayes we generate factor simulated in favor of added,In the the masked flux fraction analysis, attributed the Bayes to the factor disk for template a model is that dataNFW using PSs the increases best-fit fromparameters∼106 to from∼10 the9), butunmasked there is IG still a contains a NFW PS component, relative to one that does strong preference for unresolved PSs. The Bayes factor for 2 not, is ∼10 , substantially reduced relative to the result for a model with disk and isotropicFIG. 3. Same PSs, as relative Fig. 2, to except one with with no the unmasked case. Masking the 3FGL sources removes PSs, is ∼106, while the3FGL Bayes sources factor masked.for a model with NFW, most of the ROI within ∼5° of the GC, reducing photon disk, and isotropic PSs, relative to one with no PSs, is ∼108. statistics markedly, especially for any signal peaked at the The Bayes factors, best-fit source-count function parame- GC. Furthermore, in the masked ROI, non-NFW PS ters, and DM flux fractions for the 3FGL masked and templates can absorb a substantial fraction of the excess. unmasked analyses are summarized in Table I. For example, if only disk and isotropic PS templates are To validate the analysis procedure, we generate simulated added, the flux fraction attributed to the disk template is data using the best-fit parameters from the unmasked IG
FIG. 3. Same as Fig. 2, except with 3FGL sources masked.
051103-4
051103-4 Galactic Center Gamma Rays
5
BUT: Re-analysis by Leane & Slatyer (2019) show that method might miss DM; annihilation could account for whole signal
FIG. 2. Flux posteriors when an artificial DM signal with increasing normalization is injected into the Fermi data, and the data are analyzed with NFW PS, Disk PS, Isotropic PS, DM, Bubbles, Isotropic and Galactic Di↵use templates (note if any template has flux peaked below 0.1% (other than DM), it is omitted from the plots for simplicity). Vertical dashed lines indicate posterior medians and 68% containment bands. Di↵erent amounts of DM flux have been injected in each plot, the correct amount that should have been recovered is shown as the blue line labeled “Injected DM”. Top-Left: Zero DM injection. Top-Right: 1.8% DM flux injection. No DM is recovered, and DM is instead attributed to NFW PS. Bottom-Left: 6.7% DM flux injection. DM is still not recovered, and the NFW PS flux has been pushed up further. Bottom-Right: 15.2% DM flux injection. Some DM flux is finally identified, albeit clearly not all of it. sults of a fit on the real data, without any injected DM for the DM to be detected with non-zero flux, the in- signal, to serve as a baseline for comparison. By com- jected DM signal appears to require a total flux a factor parison with the no-injection case, we see two important & 5 larger than the GCE itself. e↵ects: firstly that (as noted above) the flux attributed Conclusions and Outlook. We have studied examples to the DM template is consistent with zero and inconsis- of how NPTF methods can be biased in both real and tent with the injected value, and secondly that the NFW simulated gamma-ray data, and how this could impact PS flux fraction increases, approximately absorbing the explanations of the GCE. We have showed a proof-of- injected DM signal. As the DM injection amount in- principle example in simulated data where a DM signal creases, we see that the NFW PS flux fraction continues can incorrectly be attributed to PSs by the NPTF, as to increase, until it reaches a saturation point and the a result of PSs with a spatial distribution that is not DM template begins to absorb some of the flux. In order described by the standard templates. Positron Excess Image credit: PAMELA Collaboration
PAMELA and the AMS instrument (and several Image credit: NASA others) saw an excess of positrons in their measurements -- could it be ?? dark matter annihilation?
3 TeV DM with high cross- section proposed as explanation
AMS Collaboration, 2013 Positron Excess
But: pulsars also make electron- positron pairs
Limited directional information
A couple of nearby pulsars could produce entire signal
Grasso et al. 2009 LESSONS FROM HAWC PULSAR WIND NEBULAE … PHYS. REV. D 97, 123008 (2018)
FIG. 4. The integrated flux per square meter per second per steradian for different energy ranges and different diffusion scenarios. The 26 2 −1 28 2 −1 dashed lines represent the cases in which the medium undergoes a transition. D1 15.4 × 10 cm s , D2 15.4 × 10 cm s and t 342 000 yr. ¼ ¼ ¼
An additional notable feature of Fig. 3 is that for the when they reach the high-diffusion region, they do not have same total energy injected in electrons and positrons, enough time to effectively diffuse. The greater the distance at generally it is not true that a region of inefficient diffusion which the transition happens, the more the spectrum around the pulsar suppresses the flux at Earth. Rather, we resembles that of the low-diffusion constant. find that for several choices for the radial dependence of the Figure 5 provides evidence that a pulsar interpretation of diffusion coefficient, the positron flux from Geminga is the anomalous positron fraction is perfectly consistent with actually enhanced compared to a uniform, large diffusion inefficient diffusion around PWNe. The figure utilizes a coefficient. transition region with a steep transition profile and of We explore and explain this feature by studying, in Fig. 4, transition radius 35 pc, and an assumed fraction of the the integrated flux for different energy ranges as a function pulsar spin-down luminosity converted to electron-positron of distance. We can appreciate from these plots how the pairs consistent with the power inferred by the TeV positron number density between 100 and 1000 GeV is observations of HAWC, i.e., around 40% [26]. We also higher than the constant-diffusion case, as we find in the show a standard secondary positron flux (light blue), and Positron Excessspectral plot. Our interpretation of these results is that the the sum of the positrons from secondary processes plus positrons in a high-diffusion-constant region reach further those from Geminga. The figure clearly shows that with a distances than the case where a transition is present: radially dependent diffusion coefficient positrons from positrons that were injected in a low-diffusion-constant Geminga can dominate the positron flux measure at region are effectively “trapped” until later times and thus, Earth and give an acceptable fit to the data. (Notice that for simplicity and for the sake of clarity we do not include But: pulsars also additional pulsars, which certainly contribute to the high- make electron- energy positron flux as well; see e.g., Ref. [27].) positron pairs IV. MACROSCOPIC EFFECTS OF INEFFICIENT DIFFUSION IN PWNe Limited directional In this section we quantify the degree to which the implied inefficient diffusion coefficient inside PWNe, information −2 DPWN ∼ 10 DISM affects cosmic-ray diffusion in the Galaxy at macroscopic scales. While we postpone detailed A couple of nearby simulations and observational tests for future work, here we employ observationally motivated relations between the pulsars could produce PWN radius, RPWN,andthepulsarcharacteristicage,τc from the Australia Telescope National Facility (ATNF) entire signal 3 catalogue of Galactic pulsars, to infer the total volume in the Galactic diffusive halo where inefficient diffusion is FIG. 5. The positron flux with the contributions of Geminga expected, V . Inefficient diffusionand secondary production, comparedProfumo to AMS-02 et al. data. 2018 The PWN transition for this case is set to 35 pc with ∼40%eÆ energy likely importantinjection efficiency. 3http://www.atnf.csiro.au/people/pulsar/psrcat/.
123008-7 Antiproton Excess 3
III. ANTIPROTON AND POSITRON FITTINGS Excess in antiprotons also seen by AMS-02
Can’t be produced by pulsars
Supernova remnants have been suggested (Kachelrieß et al. 2018) but see Cholis, Linden & FIG. 2: (a) Positron fraction (solid line), which includes the electrons and positrons coming from the DC and back- Hooper 2019 ground electrons (dotted line, for example see Refs. [29, 30]). Filled circles correspond to the AMS-02 data [1, 34, 35] and FIG. 1: Antiproton fraction fitted to the data. The data PAMELA data [5] (b) Total electron and positron flux (solid points are taken by [1] for AMS-02, and by [15] forKohri PAMELA. et al. 2016 line). The flux of the electrons and positrons created only in The dotted line is plotted only by using the background the DC (background) is plotted by the dashed (dotted) line. flux [33]. The shadow region represents the uncertainties of Observational data by AMS-02, Fermi, HESS, BETS, PPB- the background flux among the propagation models shown BETS, and ATIC2 [6–8, 36] are also plotted. The shadow in [1]. region represents the uncertainty of the HESS data.
In Fig. 1, we plot the antiproton fraction at the Earth fitted only by adding astrophysical local contributions in our model (See the model B shown in Ref. [4]). For produced from the same pp collision sources. the background flux, we adopted the 15% smaller value of the mean value shown in [33]. Here, the radius of a spherical DC, RDC = 40 pc is adopted. The targetproton −3 IV. CONCLUSION density is set to be n0 =50cm . The spectral index s =1.75 and the maximum energy Emax =100TeVare assumed. We take the duration of the pp collision to be We have discussed the anomaly of the antiproton frac- 5 tpp =2× 10 yr. The total energy of the accelerated tion recently-reported by the AMS-02 experiment. By 50 protons is assumed to be Etot,p =3× 10 erg. The considering the same origin of the pp collisions between distance to the front of the DC is set to be d =200pc. cosmic-ray protons accelerated by SNRs and a dense − + 5 About the diffusion time of e and e , tdiff =2× 10 yr cloud which surrounds the SNRs, we can fit the data is adopted. We take the magnetic field outside the DC of the observed antiproton and positron simultaneously to be Bdiff =3µG (See [4] for the further details). without a fine tuning in the model parameters. The ob- In Fig. 2 we also plot the positron fraction and the total served fluxes of both antiprotons and positrons are con- e−+e+ flux. It is remarkable that we can automatically sistent with our predictions shown in Ref. [4]. fit the observational data of both the positron fraction Regardless of the model details, the ratio of antipro- and the total e− + e+ flux by using the same set of the tons and positrons is essentially determined by the fun- parameters [4]. damental branching ratio of the pp collisions. Thus the The positron fraction rises at higher energies than that observed antiproton excess should entail the positron ex- of the antiproton fraction (Fig. 2), because the spectral cess, and vice versa. This does not depend on the propa- index of the background antiproton is harder than that of gation model since both antiparticles propagate in a sim- the background positron. This comes from a difference ilar way below the cooling cutoff energy ∼ TeV. between their cooling processes. Only for background The cutoff energy of e− cooling marks the supernova positrons and electrons the cooling is effective in the cur- age of ∼ 105 years [18, 37], while we also expect a e+ rent situation. cutoff. The trans-TeV energy will be probed by the fu- In Fig. 3, we plot the positron to antiproton ratio as a ture CALET, DAMPE and CTA experiments [38, 40]. function of the rigidity. Here the local components repre- An anisotropy of the arrival direction is also a unique sent the contribution of the nearby SNRs produced only signature, e.g., [39]. by the pp collisions. From this figure, we find that both The boron to carbon ratio as well as the Li to car- of the positron and the antiproton can be consistently bon ratio have no clear excesses [1]. This suggests that Antiproton Excess
9
10 CMB Limits
5 ) 1 - s Some suggestions that 3 Dwarf Limits cm 26
antiproton excess - 10
(× GC GeV Excess p Excess and Galactic Center v GeV excess may be 1
consistent 0.5 40 60 80 100
m (GeV) FIG. 8. Left frame: The regions of dark matter parameter space favored (within 2 )bytheAMS-02 antiproton spectrum (green closed) and the Galactic Center gamma-ray excess (red closed) [18], for the case of annihilations to b¯b. Right frame: The upper limitCholis, on the dark Linden matter’s & annihilation Hooper section 2019 derived from the cosmic-ray antiproton spectrum. Also shown in each frame are the regions excluded by measurements of the cosmic microwave background (purple) [79] and by gamma-ray observations of dwarf galaxies (red) [25]. The dashed green curve denotes the annihilation cross section predicted for dark matter in the form of a simple (s wave) thermal relic.
with, or more stringent than, other bounds. In the right that are very similar to our own. frame of Fig. 8, we show our overall constraint on the dark matter annihilation cross section, which we take to be the weakest of the constraints shown in Figs. 2, 4 and 6, evaluated at each value of m . Compared to ACKNOWLEDGMENTS the constraints derived from gamma-ray observations of dwarf spheroidal galaxies [25], we find that the limit pre- sented in this study is stronger for dark matter particles We would like to thank Simeon Bird and Marc with a mass below 40 GeV or between 130 and 540 GeV Kamionkowski for valuable discussions. DH is supported (for annihilations to b¯b). by the US Department of Energy under contract DE- As this article was being finalized, Ref. [80] appeared FG02-13ER41958. Fermilab is operated by Fermi Re- on the arXiv which addresses many of the same questions search Alliance, LLC, under Contract No. DE- AC02- discussed here. The authors of Ref. [80] reach conclusions 07CH11359 with the US Department of Energy.
[1] Lars Bergstrom, Joakim Edsjo, and Piero Ullio, “Cos- ticle Fluxes in Primary Cosmic Rays Measured with the mic anti-protons as a probe for supersymmetric dark Alpha Magnetic Spectrometer on the International Space matter?” Astrophys. J. 526,215–235(1999),arXiv:astro- Station,” Phys. Rev. Lett. 117,091103(2016). ph/9902012 [astro-ph]. [7] AMS-02 Collaboration, “AMS Days at La Palma, La [2] Dan Hooper, James E. Taylor, and Joseph Silk, “Can Palma, Canary Islands, Spain,” (2018). supersymmetry naturally explain the positron excess?” [8] Dan Hooper, Tim Linden, and Philipp Mertsch, “What Phys. Rev. D69,103509(2004),arXiv:hep-ph/0312076 Does The PAMELA Antiproton Spectrum Tell Us About [hep-ph]. Dark Matter?” JCAP 1503,021(2015),arXiv:1410.1527 [3] Stefano Profumo and Piero Ullio, “The Role of antimatter [astro-ph.HE]. searches in the hunt for supersymmetric dark matter,” [9] Marco Cirelli, Daniele Gaggero, Gaëlle Giesen, Marco JCAP 0407,006(2004),arXiv:hep-ph/0406018[hep-ph]. Taoso, and Alfredo Urbano, “Antiproton constraints on [4] Torsten Bringmann and Pierre Salati, “The galactic an- the GeV gamma-ray excess: a comprehensive analysis,” tiproton spectrum at high energies: Background expec- JCAP 1412,045(2014),arXiv:1407.2173[hep-ph]. tation vs. exotic contributions,” Phys. Rev. D75,083006 [10] Torsten Bringmann, Martin Vollmann, and Christoph (2007), arXiv:astro-ph/0612514 [astro-ph]. Weniger, “Updated cosmic-ray and radio constraints on [5] Miguel Pato, Dan Hooper, and Melanie Simet, “Pin- light dark matter: Implications for the GeV gamma-ray pointing Cosmic Ray Propagation With The AMS-02 excess at the Galactic center,” Phys. Rev. D90,123001 Experiment,” JCAP 1006,022(2010),arXiv:1002.3341 (2014), arXiv:1406.6027 [astro-ph.HE]. [astro-ph.HE]. [11] Alessandro Cuoco, Michael Krämer, and Michael Ko- [6] M. Aguilar et al. (AMS), “Antiproton Flux, Antiproton- rsmeier, “Novel Dark Matter Constraints from Antipro- to-Proton Flux Ratio, and Properties of Elementary Par- tons in Light of AMS-02,” Phys. Rev. Lett. 118,191102 Antiproton Excess
But others point out that uncertainties in cosmic ray production and propagation can explain the excess away
Boudad et al. 2020 Giesen et al. 2015 X-Ray Excess
X-ray excess in galaxy cluster cores at around 3.5 keV 7.1 keV sterile neutrino decay?
In favor: excess in many clusters; backgrounds well studied
Against: scaling of signal strength to mass not clearly consistent; nearby emission lines could Bulbul et al. 2014 contaminate X-Ray Excess 2 T. Jeltema and S. Profumo
30 Ms XMM Newton Blank-Sky 3 0.3 0.08 MOS data
analyzed1 with a Poisson likelihood, where the data is 1
− 0.090− the number of counts in each energy channel. The as- keV keV 1 1
sociated− model is a combination of the DM-induced flux − 0.06 0.085 represented by an X-ray line broadened by the detec- 0.2 tor response and two independent power laws for the 0.080 PN data background0.04 astrophysical emission and the instrumental 0.110 back. model back. + signal data QPB, where the normalization and indices of each power 0.105
law are free parameters. This same QPB power-law con- Flux [counts/s/keV] 0.1
tributionnormalized counts s 0.02 is also fit to the estimated QPB data using a normalized counts s counts/s/keV 0.100 Gaussian likelihood. For both of these datasets, we re- Draco Dwarf Galaxy 3.3 3.4 3.5 3.6 3.7 3.8 strict our attention to the energy range ms/2 0.25 keV. ± Eline [keV] 1 0 1 0 This− narrow energy range is chosen to be bigger than − Figure 2. The summed spectra, and uncertainties, for the the energy−3 resolution of the detector, which is 0.1 keV, Dessert et al. 2018 5 keV ×10 keV 0.02
1 ⇠ MOS1 and PN exposures used in the fiducial analysis. We also but small− enough such that our power-law background − show the summed best-fit background models and an exam- models are good descriptions of the data over the whole 2 ple signal contribution with ms =7.105 keV and sin (2✓)= 0 10 energy range. 10 , which0 is clearly inconsistent with the data. Note that The two likelihoods for the X-ray counts and QPB es- inBut our statistical non-detections analysis we use the joint may likelihood be notdue the summed spectra; this figure is only shown for illustrative pur- timate−5×10 are−3 then combined, providing a likelihood that, poses. −0.02 for a given ms, is a function of five parameters: the DM- to differences in signal inducednormalized counts s line flux S, as well as the normalization and in- normalized counts s 2.5 3 3.5 4 4.5 3 3.5 4 4.5 dices of the astrophysical and QPB power laws. The last search (Boyarsky et al. 2019) four of these are treated as nuisanceEnergy (keV) parameters and are Results. In Fig. 2 we showEnergy the summed(keV) spectra over profiled over at the level of this individual exposure. Each all exposures included in the analysis for the MOS and datasetJeltema has then& Profumo been reduced 2015 to a profile likelihood as PN data separately. We emphasize that we do not use Figurea 1. functionLeft:CombinedMOSspectrumandresidualsinthe2.5-5.0keVener of the DM decay flux S. As described above, gythe range summed fit to an spectra unfolded, for single our power data law. analysis,Right: Combined instead we PN use spectrum α and residualsthis flux in the can 2.5-5.0 be readily keV energy converted range fit to to ana lifetime unfoldedsinglepowerlawplusaninstrumentallineduetoTiK and hence the joint likelihood procedure describedemission at above, 4.51 keV. but A the weaker Ti Kβ line can be seen at 4.93 keV but has no effect on the 3.5 keV line constraints. sin2(2✓)[1], once the D-factor for this region of the sky is summed spectra are still useful for illustrative purposes. known. In our fiducial analysis we compute the D-factors In particular, we also show the summed best-fit back- by describing the DM density profile of the Milky Way ground models in solid red. While these curves appear ius project (Springel et al. 2008)toestimatethefluxratiofora pipeline-processed with the emchain and epchain tasks. Flare by an NFW profile with a 20 kpc scale radius. We nor- to be single power-laws, they are actually constructed standard dark matter decay process across different targets, includ- filtering was carried out with the ESAS tasks mos-filter and malize the density profile assuming a local DM density from sums over 2794 independent power-laws, two for ing for the Draco dwarf spheroidal galaxy (dSph) and the Galactic pn-filter;thesetimeperiodsofincreasedparticlebackground of 0.4 GeV/cm3 [28], and we take the distance between each exposure describing the astrophysical flux and in- center (GC). This ratio has a certain statistical distribution, which due to soft protons can lead to background levels elevated by two the Sun and the Galactic Center to be 8.127 kpc [29]. strumental QPB. The summed data is seen to closely depends on the choice of the placement of the observer. The central orders of magnitude and are thus removed from the data. Unfortu- Joining the resulting likelihoods associated with each match the summed background models. Furthermore, finding of that study is that a 1.3 Ms long XMM-Newton observa- nately, in the case of Draco particle background flaring was signif- exposure yields the final joint likelihood that is a function we illustrate in dotted red what a signal would look like tion of the Draco dSph would enable the discovery or exclusionat icant in many of the observations.2 For the two10 MOS detectors, two of only sin2(2✓) for a given m .Thislikelihoodisthen for ms =7.105 keV and sin (2✓) = 10 .Referring the 3σ level of a dark matter decay interpretations of the 3.5 keV observations (ObsID 0603190401 and 0770190601) were almost used to calculate the one-sided 95% limit on the mixing to Fig. 1, this is a relevant benchmark point as it sits signal. entirely contaminated by flaring, and we removed these from our angle and to search for evidence for the UXL using the right in the middle of the parameter space of interest for Here, we utilize recent, deep archival XMM-Newton observa- final data set; the other observations had reduced usable exposure discovery TS, which is defined as twice the log-likelihood explaining the observed UXL. However, as illustrated in tions of the Draco dSph to test a dark matter decay origin for the times. The net exposure time after filtering was a little over one di↵erence between the maximum likelihood and the like- Fig. 2, this model is clearly inconsistent with the data. 3.5 keVlihood line. We at the find null no evidence hypothesis of a (assuming line in either the the likelihood MOS or is MsecIn Fig. for each1 we MOS show detector our fiducial with limit a total along time withfor both mean, detectors PN data,maximized and we are at able a positive to rule out value a dark of sin matter2(2✓ decay)). For origin consis- at 1of and 2.1 Msec. 2 expectations The PN detector under is typically the null more hypothesis. effected by The particle greatertency, than the we 99% also confidence include negative level. values of sin2(2✓)inthe limitflaring is than consistent the MOS with detectors, expectations and we found and strongly that only dis- 20 of 31 Theprofile remainder likelihood. of this manuscript has the following structure: favorsobservations the decaying had flares DM satisfactorily explanation removed of the by pn-filter UXL. The ;for we describeTo the calibrate XMM our observations expectation and data for the reduction sensitivity in the under fol- best-fitthese observations parameter the space net for usable decaying exposure DM time to explain for PN the was 0.58 lowingthe section null2;wethendescribeourfluxcalculationandcompare hypothesis, we construct the 68% and 95% ex- previously-observedMsec. UXL is in tension with our results with the flux limits from the XMM MOS and PN data in section 3, 2 pectations for the limit using the Asimov procedure [30]. by wellPoint over sources an order were of detected magnitude and removed in sin (2 separately✓). In Fig. from3 each and we present our conclusions in the final section 4. The Asimov procedure requires a model for the data weobservation show the using TS in the favor ESAS of task decayingcheese DM;pointsourcedetection as a function under the null hypothesis; we compute this model by ofwas DM run mass, on broad-band with the images1 and 2 (0.4-7.2 expectations keV) with under a flux the limit of performing the likelihood fits described above under the null10−14 hypothesiserg cm−2 showns−1 and in a green minimum and separation yellow, respectively. of 10 arcsec. Low 2 2XMMDATAANALYSISnull hypothesis (sin (2✓) = 0). We set one-sided power- Thisexposure figure regions shows are explicitly likewise that masked we find by cheese no evidence.Spectrawere for constrained limits following [31]. In this procedure, the decaying DM. Draco was observed by XMM-Newton in 31 separate observations, extracted from the full field-of-view from each detector in each actual limit is not allowed to go below the 68% contain- flare-filteredThe TS shown observation; in Fig. 3 however,is for the for joint the MOS1likelihood detector anal- CCDs 5in2009(PIDhuga)and26in2015(PIBoyarsky),withindividment region for the expected limit in order to prevent- ysis over the ensemble of exposures. However, we can ual exposure times ranging from 17 to 87 ksec and a total time in 3and6wereexcludedduetomicrometeroiddamage.Spectraand setting stronger limits than expected due to downward alsocorresponding calculate aredistribution TS in favor matrix of decaying files (RMF) DM andfrom ancill eachary re- all observationsstatistical of fluctuations. 1.66 Msec. We reprocessed all 31 observations individual exposure. Under the null hypothesis, the dis- using standard procedures and utilizing the XMM SAS1 and ESAS sponse files (ARF) for the 0.4-7.2 keV range were created using (Snowden et al. 2008; Kuntz & Snowden 2008)softwarepackages. mos-spectra and pn-spectra in the ESAS package. The in- Starting from the Observation Data Files, the raw EPIC data was dividual spectra and response files were co-added using the rou- tines mathpha, addrmf,andaddarf in the FTOOLS package (Blackburn 1995). Combined RMF and ARF files were weighed by 1 http://xmm.esac.esa.int/sas/ the relative contribution of each observation to the total exposure
MNRAS 000,000–000(0000) Dark Matter: Indirect Detection
?
‣ signature: cosmic rays, gamma rays, neutrinos (annihilation products) ‣ results: inconclusive ‣ the future: giant cosmic ray array (CTA), high- resolution gamma-ray astronomy Dark Matter: Direct Detection
?
‣ signature: nuclear recoil ‣ results: inconclusive ‣ the future: directional Image credit: UC Berkeley detection (see: CYGNUS project) Direct Detection
CDMS Collaboration, 2013 Direct Detection
SuperCDMS
LUX
SuperCDMS Collaboration, 2014 Neutrino Wall
10−38 CRESST-II ] DAMA
2 (2015) 5) −40 CDMSLite (201 10 (2017) -2L ICO S P u p e r −42 C 15) D (20 10 M 60 CO- S CoGeNT (2014) PI ) E 015 DE (2 LW -50 (20 E ide ) 16 IS kS 016 ) S Dar (2 −44 ndaX 10 Pa 16) (20 ) 7Be LUX 2017 pp 1T ( ON XEN −46 8 10 B LZ year ton- 200 hep WIN DAR 10−48 Solar-ν
−50 Atm-ν
SI WIMP-nucleon10 cross section [cm SN-ν
10−1 100 101 102 103 104 WIMP mass [GeV/c2] O’Hare, from paper in prep Annual Modulation
DAMA/LIBRA experiment results
11.9 σ summer
winter Himawari 8 satellite image Dark Matter: Direct Detection
SABRE ?
me
‣ signature: nuclear recoil ‣ results: inconclusive ‣ the future: SABRE, directional detection (see: CYGNUS project) Dark Matter: Production
ATLAS
?
‣ signature: missing energy ‣ results: no signal (yet) ‣ the future: more LHC data, future colliders Dark Matter: Cosmology
Paul Angel, Tiamat Simulation Impact of Dark Matter Annihilation
Major unanswered question: If dark matter annihilates across all of cosmic time, how does it affect the first stars and galaxies?
ionization heating
photons gamma rays Lyman(alpha/Werner) Annihilation Over Cosmic Time
2 First question to ask: power v ⇢ When is annihilation = h i power strongest? volume m
v velocity-averaged self- Balance: h i annihilation rate density of universe (decreasing with time) ⇢ dark matter density vs
growth of structure m dark matter particle mass (increasing with time) Annihilation Over Cosmic Time
10-12 power ∝ (density)2 - 10 14 “smooth” component power
10-16 total signal 4 h
H -18 ê
s 10 ê
eV “structure”
10-20 component
10-22
10-24 0 20 40 60 80 100 120 140 present first stars redshift early times Complications
Dark matter halo density profile (NFW? Einasto? Other?) Mass-concentration relation c(M,z) Lower mass cutoff in power spectrum
NOAO/AURA/NSF/WIYN Alterations in density profile due to:
R Jay Gabany (Blackbird Observatory) Baryonic effects (feedback) Halo formation histories (low masses / high redshift) Annihilation Over Cosmic Time
10-12 Varying:
-14 * density profile 10 * mass-concentration relation power * mass function 10-16 * lower-mass cut-off 4 h
H -18 ê
s 10 ê eV
10-20
10-22
10-24 0 20 40 60 80 100 120 140 present first stars redshift early times Annihilation Over Cosmic Time
10-12
10See-14 Mack 2014, “Known Unknowns of Dark power Matter Annihilation over Cosmic Time” 10-16 in MNRAS [arxiv: 1309.7783] for details 4 h
H -18 ê
s 10 ê eV
10-20
10-22
10-24 0 20 40 60 80 100 120 140 present first stars redshift early times Annihilation in the Intergalactic Medium
halo halo
halo halo Annihilation in the Intergalactic Medium
halo halo
halo halo Usual treatment: • monolithic halos • immediate uniform energy deposition Annihilation in the Intergalactic Medium
inverse Compton scattering
Better: • structured halos • delayed energy deposition Annihilation Feedback on Halo Gas
If dark matter is annihilating within baryonic halos, does this constitute an effective “feedback” process?
Sarah Schon, Queens U, Canada Annihilation Feedback on Halo Gas
If dark matter is annihilating within baryonic halos, does this constitute an effective “feedback” process?
PYTHIA code: dark matter annihilation events MEDEA2 code: energy transfer to baryons Halo models: density profile, mass-concentration Annihilation Feedback on Halo Gas
50 Comparing: 40 dark matter
log annihilation
1 10 energy
30 ( (over Hubble time) F
eff to:
0 )
redshift (z) redshift 20 gas binding -1 -3 energy 10 -2 103 105 107 109 mass (MSun)
Schon, Mack+ 2015, MNRAS [arxiv: 1411.3783] Annihilation Feedback on Halo Gas
50 Comparing: 40 dark matter
stars log annihilation
1 form 10 energy
30 ( (over Hubble time) here F eff to:
0 )
redshift (z) redshift 20 gas binding -1 -3 energy 10 -2 103 105 107 109 mass (MSun)
Schon, Mack+ 2015, MNRAS [arxiv: 1411.3783] Annihilation Feedback on Halo Gas
50 Comparing: dark matter 40 2
stars log annihilation
form 10 energy
30 ( 1 (over Hubble time) here F eff to: )
redshift (z) redshift 20 gas binding 0 energy 10 -1
103 105 107 109 mass (MSun)
Schon, Mack+ 2015, MNRAS [arxiv: 1411.3783] Halo Structure and Environment
Improved code: tracks full particle cascades & deposition within halos
Main questions:
‣ Where is the energy deposited?
‣ What is the effect on the halo environment?
Schon, Mack & Wyithe 2018, MNRAS [arxiv:1706.04327] Halo Structure and Environment 1 direct injection
-2 Annihilation 10 products escaping photons
filtered 10-4 through halo baryons 10-6 escaping fraction of injected energy of injected fraction electrons
10-8 10 103 105 107 109 Schon, Mack & Wyithe energy (eV) 2018, MNRAS [arxiv:1706.04327] 10 Schon¨ et. al.
Halo Structure and Environment
Figure 7. Code output showing the energy distribution of particles escaping a 106M halo at redshift 20. The upper most plot shows the distribution of escapedSchon, electrons (left), Mack photons (middle) & and positronsWyithe (right) for 5 GeV 2018, electrons injected MNRAS into the halo. The [arxiv:1706.04327] plots directly below show the same but for a 5 GeV photon. The lower two plots show the total spectrum where the third row shows the original, un-modified injected annihilating spectrum for a 5 GeV DM particle annihilating via muons. The lowest most plot shows the spectra after the injected particles have passed through the halo. In both plots the pink columns show photons, blue electron and green positrons and results are given as a fraction of the halo’s total annihilation power.
Given a b =100, a number of dark matter candidates would would have to increase in mass by a factor of 2 3 at redshift 20 reduce the infall of gas onto the halo to a degree that potential and 4 5 at redshift 40 to recover a b of 100. It is again important 6 baryonic structure formation could be effected. For the dark matter to note that the large b produced for halos with mass > 10 M model with the most pronounced impact on b, 130 a MeV particle arise due to the non-inclusion of shock heating surrounding the ha- with a concentrated Einasto profile (black curve), a 105M halo
c 0000 RAS, MNRAS 000, 000–000 Probing Cosmic Dawn
Djorgovski et al., Caltech current instruments next decade
JWST
SKA High-redshift 21cm Astronomy
• The spin temperature determines the 6 relative occupancy of pected that many of our approximations will break down as smallthe scale hyperfine information about levels the sources becomes im- portant (see for example [63] for the importance of higher
Temperature order correlations on small scales during reionization). For• theThe mean brightness histories shown in Figure 1, we calculate the evolutiontemperature of the 21 cm measured angle-averaged power spec- 10 30 100 200 trum, which is plotted in Figures 2, 3, and 4, for models A, B,by and observations C, respectively. is dark ages ¯ The evolution of ∆Tb clearly shows three regimes: the star formation post-reionizationdetermined regime by at lowthe redshifts (z Pritchard & Loeb 2008 low thespin matter temperature’s power spectrum, an intermediate red- shiftcoupling regime (xreion to EDGES Experiment Bowman et al. 2018 Pritchard & Loeb 2010 EDGES EDGES Experiment Bowman et al. 2018 EDGES EDGES Experiment Bowman et al. 2018 Cold dark matter coupling? Barkana 2018; Barkana+ 2018; Berlin+ 2018; Muñoz & Loeb 2018 EDGES EDGES Experiment Bowman et al. 2018 EDGES EDGES Experiment Higher radiation background? Bowman et al. 2018 Ewall-Wice et al. 2018 Pospelov et al. 2018 Dark Matter & 21cm EDGES Experiment Foreground problems? Hills et al. 2018 Work in Progress Alternative DM Thermal Histories Standard thermal WIMP dark matter • WIMP-interaction- strength cross section mostly ruled out ➡ consider alternative histories Kolb & Turner 1990 Alternative DM Thermal Histories A. Erickcek, H. Ganjoo et al., in prep Early matter-dominated epoch • dark matter coupled to Standard Model only via mediator • mediator dominates during radiation domination, initiating temporary matter-dominated era • mediator decays, heating dark matter Alternative DM Thermal Histories Potential impacts on: • annihilation signal • small-scale matter power spectrum A. Erickcek, H. Ganjoo et al., in prep dark matter annihilates dark matter annihilates internal heating radiation of dark matter background halos low-mass cut-off structure of first stars heating/ ionization of intergalactic gas H2 abundance black hole formation dark matter annihilates internal heating radiation of dark matter background halos low-mass cut-off structure of first stars heating/ ionization of intergalactic gas H2 abundance black hole formation dark matter annihilates internal heating radiation of dark matter background halos low-mass cut-off structure of first stars heating/ ionization of intergalactic gas H2 abundance black hole formation unified simulation of galaxy formation & evolution dark matter annihilates internal heating radiation of dark matter background halos low-mass cut-off structure of first stars heating/ ionization of intergalactic gas H2 abundance black hole formation unified simulation of galaxy formation & evolution high-redshift high-redshift 21cm signal galaxies dark matter annihilates internal heating radiation of dark matter background halos low-mass cut-off structure of first stars heating/ ionization of intergalactic gas H2 abundance black hole formation unified simulation of galaxy formation & evolution high-redshift high-redshift 21cm signal galaxies Image credit: Swinburne/ credit: Image ICRAR/Cambridge/ASTRON SKA dark matter annihilates internal heating radiation of dark matter background halos low-mass cut-off structure of first stars heating/ ionization of intergalactic gas H2 abundance black hole formation JWST unified simulation of galaxy formation & evolution high-redshift high-redshift 21cm signal galaxies Image credit: Swinburne/ credit: Image ICRAR/Cambridge/ASTRON SKA Image credit: NASA Take-Home Messages Future surveys can probe the particle physics of dark matter and produce a more consistent picture of cosmology To determine dark matter’s impact on high-redshift astrophysics, we need to understand small halos and their evolution