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Register a new userTowards constraining warm dark matter with stellar streams through neural simulation-based inference
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A statistical analysis of the observed perturbations in the density of stellar streams can in principle set stringent contraints on the mass function of dark matter subhaloes, which in turn can be used to constrain the mass of the dark matter particle. However, the likelihood of a stellar density with respect to the stream and subhaloes parameters involves solving an intractable inverse problem which rests on the integration of all possible forward realisations implicitly defined by the simulation model. In order to infer the subhalo abundance, previous analyses have relied on Approximate Bayesian Computation (ABC) together with domain-motivated but handcrafted summary statistics. Here, we introduce a likelihood-free Bayesian inference pipeline based on Amortised Approximate Likelihood Ratios (AALR), which automatically learns a mapping between the data and the simulator parameters and obviates the need to handcraft a possibly insufficient summary statistic. We apply the method to the simplified case where stellar streams are only perturbed by dark matter subhaloes, thus neglecting baryonic substructures, and describe several diagnostics that demonstrate the effectiveness of the new method and the statistical quality of the learned estimator.
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Narrow stellar streams in the Milky Way halo are uniquely sensitive to dark-matter subhalos, but many of these subhalos may be tidally disrupted. I calculate the interaction between stellar and dark-matter streams using analytical and $N$-body calculations, showing that disrupting objects can be detected as low-concentration subhalos. Through this effect, we can constrain the lumpiness of the halo as well as the orbit and present position of individual dark-matter streams. This will have profound implications for the formation of halos and for direct and indirect-detection dark-matter searches.
We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities. By formulating the mass estimation problem within this simulation-based inference framework, we are able to quantify the uncertainties on the inferred masses in a straightforward and robust way. We generate a realistic mock catalogue emulating the Sloan Digital Sky Survey (SDSS) Legacy spectroscopic observations (the main galaxy sample) for redshifts $z lesssim 0.09$ and explicitly illustrate the challenges posed by interloper (non-member) galaxies for cluster mass estimation from actual observations. Our approach constitutes the first optimal machine learning-based exploitation of the information content of the full 3D projected phase-space distribution, including both the virialized and infall cluster regions, for the inference of dynamical cluster masses. We also present, for the first time, the application of a simulation-based inference machinery to obtain dynamical masses of around $800$ galaxy clusters found in the SDSS Legacy Survey, and show that the resulting mass estimates are consistent with mass measurements from the literature.
We estimate the 3D density profile of the Galactic dark matter (DM) halo within $r lesssim 30$ kpc from the Galactic centre by using the astrometric data for halo RR Lyrae stars from Gaia DR2. We model both the stellar halo distribution function and the Galactic potential, fully taking into account the survey selection function, the observational errors, and the missing line-of-sight velocity data for RR Lyrae stars. With a Bayesian MCMC method, we infer the model parameters, including the density flattening of the DM halo $q$, which is assumed to be constant as a function of radius. We find that 99% of the posterior distribution of $q$ is located at $q>0.963$, which strongly disfavours a flattened DM halo. We cannot draw any conclusions as to whether the Galactic DM halo at $r lesssim 30$ kpc is prolate, because we restrict ourselves to axisymmetric oblate halo models with $qleq1$. Our result is inconsistent with predictions from cosmological hydrodynamical simulations that advocate more oblate ($langle{q}rangle sim0.8 pm 0.15$) DM halos within $sim 15%$ of the virial radius for Milky-Way-sized galaxies. An alternative possibility, based on our validation tests with a cosmological simulation, is that the true value $q$ of the Galactic halo could be consistent with cosmological simulations but that disequilibrium in the Milky Way potential is inflating our measurement of $q$ by 0.1-0.2. As a by-product of our analysis, our model constrains the DM density in the Solar neighbourhood to be $rho_{mathrm{DM},odot} = (9.01^{+0.18}_{-0.20})times10^{-3}M_odot mathrm{pc}^{-3} = 0.342^{+0.007}_{-0.007}$ $;mathrm{GeV} mathrm{cm}^{-3}$.
We perform a search for stellar streams around the Milky Way using the first three years of multi-band optical imaging data from the Dark Energy Survey (DES). We use DES data covering $sim 5000$ sq. deg. to a depth of $g > 23.5$ with a relative photometric calibration uncertainty of $< 1 %$. This data set yields unprecedented sensitivity to the stellar density field in the southern celestial hemisphere, enabling the detection of faint stellar streams to a heliocentric distance of $sim 50$ kpc. We search for stellar streams using a matched-filter in color-magnitude space derived from a synthetic isochrone of an old, metal-poor stellar population. Our detection technique recovers four previously known thin stellar streams: Phoenix, ATLAS, Tucana III, and a possible extension of Molonglo. In addition, we report the discovery of eleven new stellar streams. In general, the new streams detected by DES are fainter, more distant, and lower surface brightness than streams detected by similar techniques in previous photometric surveys. As a by-product of our stellar stream search, we find evidence for extra-tidal stellar structure associated with four globular clusters: NGC 288, NGC 1261, NGC 1851, and NGC 1904. The ever-growing sample of stellar streams will provide insight into the formation of the Galactic stellar halo, the Milky Way gravitational potential, as well as the large- and small-scale distribution of dark matter around the Milky Way.
The dark matter halos that surround Milky Way-like galaxies in cosmological simulations are, to first order, triaxial. Nearly 30 years ago it was predicted that such triaxial dark matter halos should exhibit steady figure rotation or tumbling motions for durations of several gigayears. The angular frequency of figure rotation predicted by cosmological simulations is described by a log-normal distribution of pattern speed with a median value 0.15hkm/s/kpc (~ 0.15h rad/Gyr ~ 9h deg/Gyr) and a width of 0.83km/s/kpc. These pattern speeds are so small that they have generally been considered both unimportant and undetectable. In this work we show that even this extremely slow figure rotation can significantly alter the structure of extended stellar streams produced by the tidal disruption of satellites in the Milky Way halo. We simulate the behavior of a Sagittarius-like polar tidal stream in triaxial dark matter halos with different shapes, when the halos are rotated about the three principal axes. For pattern speeds typical of cosmological halos we demonstrate, for the first time, that a Sagittarius-like tidal stream would be altered to a degree that is detectable even with current observations. This discovery will potentially allow for a future measurement of figure rotation of the Milky Ways dark halo, and perhaps enabling the first evidence of this relatively unexplored prediction of LambdaCDM.
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