There are many well-known correlations between dark matter and baryons that exist on galactic scales. These correlations can essentially be encompassed by a simple scaling relation between observed and baryonic accelerations, historically known as the Mass Discrepancy Acceleration Relation (MDAR). The existence of such a relation has prompted many theories that attempt to explain the correlations by invoking additional fundamental forces on baryons. The standard lore has been that a theory that reduces to the MDAR on galaxy scales but behaves like cold dark matter (CDM) on larger scales provides an excellent fit to data, since CDM is desirable on scales of clusters and above. However, this statement should be revised in light of recent results showing that a fundamental force that reproduces the MDAR is challenged by Milky Way dynamics. In this study, we test this claim on the example of Superfluid Dark Matter. We find that a standard CDM model is strongly preferred over a static superfluid profile. This is due to the fact that the superfluid model over-predicts vertical accelerations, even while reproducing galactic rotation curves. Our results establish an important criterion that any dark matter model must satisfy within the Milky Way.

]]>I will discuss effective field theories for two classes of non-equilibrium systems, one far and one near equilibrium. In the first part I will present an effective response theory for topological driven (Floquet) systems, which are inherently far from equilibrium. As an example, I will discuss a topological chiral Floquet drive coupled to a background $U(1)$ field, which gives rise to a theta term in the effective action. In the second part, I will discuss an ongoing project using effective field theories for hydrodynamics. I will show that chiral diffusion for interacting systems in 1+1 dimensions, which may be relevant to edge transport in quantum Hall systems, has an infrared instability. I will then discuss the fate of this instability.

]]>Dark matter structures are expected to exist over a large range of scales, and their properties and distribution can strongly correlate with the underlying particle physics. In this talk, I will describe two separate methods to statistically infer the properties of dark matter substructure using (astrometric)-weak and strong lensing observations, respectively. In the first part of the talk, I will describe how the motion of subhalos in the Milky Way induces a correlated pattern of motions in background celestial objects---known as astrometric weak lensing---and how global signatures of these correlations can be measured using the vector spherical harmonic decomposition formalism. These measurement can be used to statistically infer the nature of substructure, and I will show how this can be practically achieved with future astrometric surveys and/or radio telescopes such as WFIRST and the Square Kilometer Array. Next, I will describe a novel method to disentangle the collective imprint of dark matter substructure on extended arcs in galaxy-galaxy strong lensing systems using likelihood-free (or simulation-based) inference techniques. This method uses neural networks to directly estimate the likelihood ratios associated with population-level parameters characterizing substructure within lensing systems. I will show how this method can provide an efficient and principled way to mine the large sample of strong lenses that will be imaged by future surveys like LSST and Euclid to look for signatures of dark matter substructure. I will emphasize how the statistical inference of substructure using these techniques can be used to stress-test the Cold Dark Matter paradigm and probe alternative scenarios such as scalar field dark matter and enhanced primordial fluctuations.

]]>Please note special time and location:

Wednesday 29th January, 2:30 - 3:30

3481 Randall Lab