Publications

We introduce a real-space slave rotor theory of the physics of topological Mott insulators, using the Kane-Mele-Hubbard model as an example, and use it to show that a topological gap in the Green function zeros corresponds to a gap in the bulk spinon spectrum and that a zero edge mode corresponds to a spinon edge mode. We then consider an interface between a topological Mott insulator and a conventional topological insulator showing how the spinon edge mode of the topological Mott insulator combines with the spin part of the conventional electron topological edge state leaving a non-Fermi liquid edge mode described by a gapless propagating holon and gapped spinon state. Our work demonstrates the physical meaning of Green function zeros and shows that interfaces between conventional and Mott topological insulators are a rich source of new physics.

The circularly polarized photogalvanic effect (CPGE) is studied in chiral Weyl semimetals with short-ranged quenched disorder. Without disorder, the topological properties of chiral Weyl semimetals lead to the quantization of the CPGE, which is a second-order optical response. Using a combination of diagrammatic perturbation theory in the continuum and exact numerical calculations via the kernel polynomial method on a lattice model we show that disorder perturbatively destabilizes the quantization of the CPGE.

We calculate target-material responses for dark matter--electron scattering at the

Recent experiments have realized steady-state electrical injection of interlayer excitons in electron-hole bilayers subject to a large bias voltage. In the ideal case in which interlayer tunneling is negligibly weak, the system is in quasi-equilibrium with a reduced effective band gap. Interlayer tunneling introduces a current and drives the system out of equilibrium. In this work we derive a nonequilibrium field theory description of interlayer excitons in biased electron-hole bilayers. In the large bias limit, we find that p-wave interlayer tunneling reduces the effective band gap and increases the effective temperature for intervalley excitons. We discuss possible experimental implications for InAs/GaSb quantum wells and transition metal dichalcogenide bilayers.

We consider when a sparse nonnegative matrix \(\mathbf{S}\) can be recovered, via an elementwise nonlinearity, from a real-valued matrix~ \(\mathbf{S}\) of significantly lower rank. Of particular interest is the setting where the positive elements of \( \mathbf{S}\) encode the similarities of nearby points on a low dimensional manifold. The recovery can then be posed as a problem in manifold learning---in this case, how to learn a norm-preserving and neighborhood-preserving mapping of high dimensional inputs into a lower dimensional space. We describe an algorithm for this problem based on a generalized low-rank decomposition of sparse matrices. This decomposition has the interesting property that it can be encoded by a neural network with one layer of rectified linear units; since the algorithm discovers this encoding, it can also be viewed as a layerwise primitive for deep learning. The algorithm regards the inputs \(\mathbf{x}_i|)\) and \(\mathbf{x}_j\)\) as similar whenever the cosine of the angle between them exceeds some threshold \(\tau\in(0,1)\). Given this threshold, the algorithm attempts to discover a mapping \(\mathbf{x}_i\mapsto\mathbf{y}_i\) by matching the elements of two sparse matrices; in particular, it seeks a mapping for which \(\mathbf{S}=\max(0,\mathbf{L})\), where \(S_{ij} = \max(0,\mathbf{x}_i\cdot\mathbf{x}_j - \tau\|\mathbf{x}_i\|\|\mathbf{x}_j\|)\) and \(L_{ij} = \mathbf{y}_i\cdot\mathbf{y}_j - \tau\|\mathbf{y}_i\|\|\mathbf{y}_j\|\). We apply the algorithm to data sets where vector magnitudes and small cosine distances have interpretable meanings (e.g., the brightness of an image, the similarity to other words). On these data sets, the algorithm is able to discover much lower dimensional representations that preserve these meanings

Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools. In this work, we present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full 6-dimensional Lorentz symmetry, and is fully permutation-equivariant throughout. We study the application of this network architecture to the standard task of top quark tagging and show that the resulting network outperforms all existing competitors despite much lower model complexity. In addition, we present a Lorentz-covariant variant of the same network applied to a 4-momentum regression task.

Observations show an almost ubiquitous presence of extra mixing in low-mass upper giant branch stars. The most commonly invoked explanation for this is thermohaline mixing. One-dimensional stellar evolution models include various prescriptions for thermohaline mixing, but the use of observational data directly to discriminate between thermohaline prescriptions has thus far been limited. Here, we propose a new framework to facilitate direct comparison: Using carbon-to-nitrogen measurements from the SDSS-IV APOGEE survey as a probe of mixing and a fluid parameter known as the reduced density ratio from one-dimensional stellar evolution programs, we compare the observed amount of extra mixing on the upper giant branch to predicted trends from three-dimensional fluid dynamics simulations. Using this method, we are able to empirically constrain how mixing efficiency should vary with the reduced density ratio. We find the observed amount of extra mixing is strongly correlated with the reduced density ratio and that trends between reduced density ratio and fundamental stellar parameters are robust across choices for modeling prescription. We show that stars with available mixing data tend to have relatively low density ratios, which should inform the regimes selected for future simulation efforts. Finally, we show that there is increased mixing at low reduced density ratios, which is consistent with current hydrodynamical models of thermohaline mixing. The introduction of this framework sets a new standard for theoretical modeling efforts, as validation for not only the amount of extra mixing, but trends between the degree of extra mixing and fundamental stellar parameters is now possible.

The core collapse of rapidly rotating massive ∼ 10M⊙ stars ("collapsars"), and the resulting formation of hyperaccreting black holes, comprise a leading model for the central engines of long-duration gamma-ray bursts (GRBs) and promising sources of r-process nucleosynthesis. Here, we explore the signatures of collapsars from progenitors with helium cores ≳ 130M⊙ above the pair-instability mass gap. While the rapid collapse to a black hole likely precludes prompt explosions in these systems, we demonstrate that disk outflows can generate a large quantity (up to ≳ 50M⊙) of ejecta, comprised of ≳ 5–10M⊙ in r-process elements and ∼ 0.1–1M⊙ of 56Ni, expanding at velocities ∼0.1 c. Radioactive heating of the disk wind ejecta powers an optical/IR transient, with a characteristic luminosity ∼ 1042 erg s−1 and a spectral peak in the near-IR (due to the high optical/UV opacities of lanthanide elements), similar to kilonovae from neutron star mergers, but with longer durations ≳1 month. These "super-kilonovae" (superKNe) herald the birth of massive black holes ≳ 60M⊙, which—as a result of disk wind mass loss—can populate the pair-instability mass gap "from above," and could potentially create the binary components of GW190521. SuperKNe could be discovered via wide-field surveys, such as those planned with the Roman Space Telescope, or via late-time IR follow-up observations of extremely energetic GRBs. Multiband gravitational waves of ∼ 0.1–50 Hz from nonaxisymmetric instabilities in self-gravitating massive collapsar disks are potentially detectable by proposed observatories out to hundreds of Mpc; in contrast to the "chirp" from binary mergers, the collapsar gravitational-wave signal decreases in frequency as the disk radius grows ("sad trombone").

Here, we report the discovery of a kilonova associated with the nearby (350 Mpc) minute-duration GRB 211211A. In tandem with deep optical limits that rule out the presence of an accompanying supernova to MI>−13 mag at 17.7 days post-burst, the identification of a kilonova confirms that this burst's progenitor was a compact object merger. While the spectrally softer tail in GRB 211211A's gamma-ray light curve is reminiscent of previous extended emission short GRBs (EE-SGRBs), its prompt, bright spikes last ≳12 s, separating it from past EE-SGRBs. GRB 211211A's kilonova has a similar luminosity, duration and color to AT2017gfo, the kilonova found in association with the gravitational wave (GW)-detected binary neutron star (BNS) merger GW170817. We find that the merger ejected ≈0.04M⊙ of r-process-rich material, and is consistent with the merger of two neutron stars (NSs) with masses close to the canonical 1.4M⊙. This discovery implies that GRBs with long, complex light curves can be spawned from compact object merger events and that a population of kilonovae following GRBs with durations ≫2 s should be accounted for in calculations of the NS merger r-process contribution and rate. At 350 Mpc, the current network of GW interferometers at design sensitivity would have detected the merger precipitating GRB 211211A, had it been operating at the time of the event. Further searches for GW signals coincident with long GRBs are therefore a promising route for future multi-messenger astronomy.

For decades, gamma-ray bursts (GRBs) have been broadly divided into `long'- and `short'-duration bursts, lasting more or less than 2s, respectively. However, this dichotomy does not map perfectly to the two progenitor channels that are known to produce GRBs -- the merger of compact objects (merger-GRBs) or the collapse of massive stars (collapsar-GRBs). In particular, the merger-GRBs population may also include bursts with a short, hard ≲2s spike and subsequent longer, softer extended emission (EE). The recent discovery of a kilonova -- the radioactive glow of heavy elements made in neutron star mergers -- in the 50s-duration GRB 211211A further demonstrates that mergers can drive long, complex GRBs that mimic the collapsar population. Here we present a detailed temporal and spectral analysis of the high-energy emission of GRB 211211A. We demonstrate that the emission has a purely synchrotron origin, with both the peak and cooling frequencies moving through the γ-ray band down to the X-rays, and that the rapidly-evolving spectrum drives the EE signature at late times. The identification of such spectral evolution in a merger-GRB opens avenues for diagnostics of the progenitor type.