Publications

We perform a statistical standard siren analysis of GW170817. Our analysis does not utilize knowledge of NGC 4993 as the unique host galaxy of the optical counterpart to GW170817. Instead, we consider each galaxy within the GW170817 localization region as a potential host; combining the redshift from each galaxy with the distance estimate from GW170817 provides an estimate of the Hubble constant, H0. We then combine the H0 values from all the galaxies to provide a final measurement of H0. We explore the dependence of our results on the thresholds by which galaxies are included in our sample, as well as the impact of weighting the galaxies by stellar mass and star-formation rate. Considering all galaxies brighter than 0.01L⋆B as equally likely to host a BNS merger, we find H0=76+48−23 km s−1 Mpc−1 (maximum a posteriori and 68.3% highest density posterior interval; assuming a flat H0 prior in the range [10,220] km s−1 Mpc−1). Restricting only to galaxies brighter than 0.626L⋆B tightens the measurement to H0=77+37−18 km s−1 Mpc−1. We show that weighting the host galaxies by stellar mass or star-formation rate provides entirely consistent results with potentially tighter constraints. While these statistical estimates are inferior to the value from the counterpart standard siren measurement utilizing NGC 4993 as the unique host, H0=76+19−13 km s−1 Mpc−1 (determined from the same publicly available data), our analysis is a proof-of-principle demonstration of the statistical approach first proposed by Bernard Schutz over 30 years ago.

Using polarized optical and magneto-optical spectroscopy, we have demonstrated universal aspects of electrodynamics associated with Dirac nodal-lines. We investigated anisotropic electrodynamics of NbAs2 where the spin-orbit interaction triggers energy gaps along the nodal-lines, which manifest as sharp steps in the optical conductivity spectra. We show experimentally and theoretically that shifted 2D Dirac nodal-lines feature linear scaling σ1(ω)∼ω, similar to 3D nodal-points. Massive Dirac nature of the nodal-lines are confirmed by magneto-optical data, which may also be indicative of theoretically predicted surface states. Optical data also offer a natural explanation for the giant magneto-resistance in NbAs2.

The observation of gravitational-waves from merging supermassive black holes will be transformative: the detection of a low-frequency gravitational-wave background can tell us if and how supermassive black holes merge, inform our knowledge of galaxy merger rates and supermassive black hole masses, and enable the possibility of detecting new physics at nanohertz frequencies. All we have to do is time pulsars.

We study the chaotic-like behavior of cosmological simulations by quantifying how minute perturbations grow over time and manifest as macroscopic differences in galaxy properties. When we run pairs of 'shadow' simulations that are identical except for random minute initial displacements to particle positions (e.g. of order 1e-7pc), the results diverge from each other at the individual galaxy level (while the statistical properties of the ensemble of galaxies are unchanged). After cosmological times, the global properties of pairs of 'shadow' galaxies that are matched between the simulations differ from each other generally at a level of ~2-25%, depending on the considered physical quantity. We perform these experiments using cosmological volumes of (25-50Mpc/h)^3 evolved either purely with dark matter, or with baryons and star-formation but no feedback, or using the full feedback model of the IllustrisTNG project. The runs cover four resolution levels spanning a factor of 512 in mass. We find that without feedback the differences between shadow galaxies generally become smaller as the resolution increases, but with the IllustrisTNG model the results are mostly converging towards a 'floor'. This hints at the role of feedback in setting the chaotic properties of galaxy formation. Importantly, we compare the macroscopic differences between shadow galaxies to the overall scatter in various galaxy scaling relations, and conclude that for the star formation-mass and the Tully-Fisher relations the butterfly effect in our simulations contributes significantly to the overall scatter. We find that our results are robust to whether random numbers are used in the sub-grid models or not. We discuss the implications for galaxy formation theory in general and for cosmological simulations in particular.

Motivated by recent experiments, we use the +U extension of the generalized gradient approximation to density functional theory to study superlattices composed of alternating layers of LaNiO3 and LaMnO3. For comparison we also study a rocksalt [(111) double perovskite] structure and bulk LaNiO3 and LaMnO3. A Wannier function analysis indicates that band parameters are transferable from bulk to superlattice situations with the exception of the transition-metal d-level energy, which has a contribution from the change in d-shell occupancy. The charge transfer from Mn to Ni is found to be moderate in the superlattice, indicating metallic behavior, in contrast to the insulating behavior found in recent experiments, while the rocksalt structure is found to be insulating with a large Mn-Ni charge transfer. We suggest a high density of cation antisite defects may account for the insulating behavior experimentally observed in short-period superlattices.

There is currently great interest in the strong coupling between the quantized photon field of a cavity and electronic or other degrees of freedom in materials. A major goal is the creation of novel collective states entangling photons with those degrees of freedom. Here we show that the cooperative effect between strong electron correlations in quantum materials and the long-range interactions induced by the photon field leads to the stabilization of coherent phases of light and matter. By studying a two-band model of interacting electrons coupled to a cavity field, we show that a phase characterized by the simultaneous condensation of excitons and photon superradiance can be realized, hence stabilizing and intertwining two collective phenomena which are rather elusive in the absence of this cooperative effect.

We describe an approach for many-body calculations with a finite-temperature, grand canonical ensemble formalism using auxiliary-field quantum Monte Carlo (AFQMC) with a self-consistent constraint to control the sign problem. The usual AFQMC formalism of Blankenbecler, Scalapino, and Sugar suffers from the sign problem with most physical Hamiltonians, as is well known. Building on earlier ideas to constrain the paths in auxiliary-field space [Phys. Rev. Lett. \textbf{83}, 2777 (1999)] and incorporating recent developments in zero-temperature, canonical-ensemble methods, we discuss how a self-consistent constraint can be introduced in the finite-temperature, grand-canonical-ensemble framework. This together with several other algorithmic improvements discussed here leads to a more accurate, more efficient, and numerically more stable approach for finite-temperature calculations. We carry out a systematic benchmark study in the two-dimensional repulsive Hubbard model at 1/8 doping. Temperatures as low as T=1/80 (in units of hopping) are reached. The finite-temperature method is exact at very high temperatures, and approaches the result of the zero-temperature constrained-path AFQMC as temperature is lowered. The benchmark shows that systematically accurate results are obtained for thermodynamic properties.

Extreme-precision radial velocity (EPRV) exoplanet surveys face considerable data analysis challenges in extracting maximally precise RVs from spectra. Chief among these, particularly for the upcoming generation of red-optimized spectrographs targeting M dwarfs, is the presence of telluric absorption features which are not perfectly known. Another major limitation on the achievable RV precision is the need to adopt an imperfect stellar template against which to cross-correlate or otherwise match the observations. In both cases, precision-limiting reliance on external information can be sidestepped using the data directly. Here we propose a data-driven method to simultaneously extract precise RVs and infer the underlying stellar and telluric spectra using a linear model (in the log of flux). The model employs a convex objective and convex regularization to keep the optimization of the spectral components fast. We implement this method in wobble, an open-source python package which uses TensorFlow in one of its first non-neural-network applications to astronomical data. In this work, we demonstrate the performance of wobble on archival HARPS spectra. We recover the canonical exoplanet 51 Pegasi b, detect the secular RV evolution of the M dwarf Barnard's Star, and retrieve the Rossiter-McLaughlin effect for the Hot Jupiter HD 189733b. The method additionally produces extremely high-S/N composite stellar spectra and detailed time-variable telluric spectra, which we also present here.

The dynamics and deformations of immersed flexible fibers are at the heart of important industrial and biological processes, induce peculiar mechanical and transport properties in the fluids that contain them, and are the basis for novel methods of flow control. Here we focus on the low–Reynolds number regime where advances in studying these fiber–fluid systems have been especially rapid. On the experimental side, this is due to new methods of fiber synthesis, microfluidic flow control, and microscope-based tracking measurement techniques. Likewise, there have been continuous improvements in the specialized mathematical modeling and numerical methods needed to capture the interactions of slender flexible fibers with flows, boundaries, and each other.

Current mechanisms for knowledge transfer in deep networks tend to either share the lower layers between tasks, or build upon representations trained on other tasks. However, existing work in non-deep multi-task and lifelong learning has shown success with using factorized representations of the model parameter space for transfer, permitting more flexible construction of task models. Inspired by this idea, we introduce a novel architecture for sharing latent factorized representations in convolutional neural networks (CNNs). The proposed approach, called a deconvolutional factorized CNN, uses a combination of deconvolutional factorization and tensor contraction to perform flexible transfer between tasks. Experiments on two computer vision data sets show that the DF-CNN achieves superior performance in challenging lifelong learning settings, resists catastrophic forgetting, and exhibits reverse transfer to improve previously learned tasks from subsequent experience without retraining.