Publications

Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar observations with stellar physics and to understand the distribution of stellar populations within the context of galaxy formation. Numerous ongoing and upcoming missions are charting brightness fluctuations of stars over time, which encode information about physical processes such as the rotation period, evolutionary state (such as effective temperature and surface gravity), and mass (via asteroseismic parameters). Here, we explore how well we can predict these stellar properties, across different evolutionary states, using only photometric time-series data. To do this, we implement a convolutional neural network, and with data-driven modeling we predict stellar properties from light curves of various baselines and cadences. Based on a single quarter of Kepler data, we recover the stellar properties, including the surface gravity for red giant stars (with an uncertainty of ≲0.06 dex) and rotation period for main-sequence stars (with an uncertainty of ≲5.2 days, and unbiased from ≈5 to 40 days). Shortening the Kepler data to a 27 days Transiting Exoplanet Survey Satellite–like baseline, we recover the stellar properties with a small decrease in precision, ∼0.07 for log g and ∼5.5 days for Prot, unbiased from ≈5 to 35 days. Our flexible data-driven approach leverages the full information content of the data, requires minimal or no feature engineering, and can be generalized to other surveys and data sets. This has the potential to provide stellar property estimates for many millions of stars in current and future surveys.

Pulsar radio emission may be generated in pair discharges that fill the pulsar magnetosphere with plasma as an accelerating electric field is screened by freshly created pairs. In this Letter, we develop a simplified analytic theory for the screening of the electric field in these pair discharges and use it to estimate total radio luminosity and spectrum. The discharge has three stages. First, the electric field is screened for the first time and starts to oscillate. Next, a nonlinear phase occurs. In this phase, the amplitude of the electric field experiences strong damping because the field dramatically changes the momenta of newly created pairs. This strong damping ceases, and the system enters a final linear phase, when the electric field can no longer dramatically change pair momenta. Applied to pulsars, this theory may explain several aspects of radio emission, including the observed luminosity, Lrad ∼ 1028 erg s−1, and the observed spectrum, Sω ∼ ω−1.4±1.0.

Peptoid macrocycles are versatile and chemically diverse peptidomimetic oligomers. However, the conformations and dynamics of these macrocycles have not been evaluated comprehensively and require extensive further investigation. Recent studies indicate that two degrees of freedom, and four distinct conformations, adequately describe the behavior of each monomer backbone unit in most peptoid oligomers. On the basis of this insight, we conducted molecular dynamics simulations of model macrocycles using an exhaustive set of idealized possible starting conformations. Simulations of various sizes of peptoid macrocycles yielded a limited set of populated conformations. In addition to reproducing all relevant experimentally determined conformations, the simulations accurately predicted a cyclo-octamer conformation for which we now present the first experimental observation. Sets of three adjacent dihedral angles (ϕi, ψi, ωi+1) exhibited correlated crankshaft motions over the course of simulation for peptoid macrocycles of six residues and larger. These correlated motions may occur in the form of an inversion of one amide bond and the concerted rotation of the preceding ϕ and ψ angles to their mirror-image conformation, a variation on “crankshaft flip” motions studied in polymers and peptides. The energy landscape of these peptoid macrocycles can be described as a network of conformations interconnected by transformations of individual crankshaft flips. For macrocycles of up to eight residues, our mapping of the landscape is essentially complete.

Mixtures of filaments and molecular motors form active materials with diverse dynamical behaviors that vary based on their constituents’ molecular properties. To develop a multiscale of these materials, we map the nonequilibrium phase diagram of microtubules and tip-accumulating kinesin-4 molecular motors. We find that kinesin-4 can drive either global contractions or turbulent like extensile dynamics, depending on the concentrations of both microtubules and a bundling agent. We also observe a range of spatially heterogeneous nonequilibrium phases, including finite-sized radial asters, 1D wormlike chains, extended 2D bilayers, and system-spanning 3D active foams. Finally, we describe intricate kinetic pathways that yield microphase-separated structures and arise from the inherent frustration between the orientational order of filamentous microtubules and the positional order of tip-accumulating molecular motors. Our work reveals a range of novel active states. It also shows that the form of active stresses is not solely dictated by the properties of individual motors and filaments, but is also contingent on the constituent concentrations and spatial arrangement of motors on the filaments.

Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. We address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. We provide Sei as a resource to elucidate the regulatory basis of human health and disease.

We consider the Saintillan--Shelley kinetic model of active rodlike particles in Stokes flow (Saintillan & Shelley 2008a,b), for which the uniform, isotropic suspension of pusher particles is known to be unstable in certain settings. Through weakly nonlinear analysis accompanied by numerical simulations, we determine exactly how the isotropic steady state loses stability in different parameter regimes. We study each of the various types of bifurcations admitted by the system, including both subcritical and supercritical Hopf and pitchfork bifurcations. Elucidating this system's behavior near these bifurcations provides a theoretical means of comparing this model with other physical systems which transition to turbulence, and makes predictions about the nature of bifurcations in active suspensions that can be explored experimentally.

The long-term goal of this work is the development of high-fidelity simulation tools for dispersive tsunami propagation. A dispersive model is especially important for short wavelength phenomena such as an asteroid impact into the ocean, and is also important in modeling other events where the simpler shallow water equations are insufficient. Adaptive simulations are crucial to bridge the scales from deep ocean to inundation, but have difficulties with the implicit system of equations that results from dispersive models. We propose a fractional step scheme that advances the solution on separate patches with different spatial resolutions and time steps. We show a simulation with 7 levels of adaptive meshes and onshore inundation resulting from a simulated asteroid impact off the coast of Washington. Finally, we discuss a number of open research questions that need to be resolved for high quality simulations.

In this work, we performed extensive first-principles simulations of high-harmonic generation in the topological Diract semimetal Na3Bi using a time-dependent density functional theory framework, focusing on the effect of spin-orbit coupling (SOC) on the harmonic response. We also derived a general analytical model describing the microscopic mechanism of strong-field dynamics in presence of spin-orbit coupling, starting from a locally U(1)xSU(2) gauge-invariant Hamiltonian. Our results reveal that SOC: (i) affects the strong-field ionization by modifying the bandstructure of Na3Bi, (ii) modifies the electron velocity, making each spin channel to react differently to the pump laser field, (iii) changes the emission timing of the emitted harmonics. Moreover, we show that the SOC affects the harmonic emission by directly coupling the charge current to the spin currents, paving the way to the high-harmonic spectroscopy of spin currents in solids.

Predictions of the mean and covariance matrix of summary statistics are critical for confronting cosmological theories with observations, not least for likelihood approximations and parameter inference. Accurate estimates require running costly N-body and hydrodynamics simulations. Approximate solvers, or surrogates, greatly reduce the computational cost but introduce biases, especially in the non-linear regime of structure growth. We propose ‘CARPool Bayes’ to solve the inference problem for both the means and covariances using a combination of simulations and surrogates. Our approach allows incorporating prior information for the mean and covariance. We derive closed-form solutions for maximum a posteriori covariance estimates that are efficient Bayesian shrinkage estimators, guarantee positive semidefiniteness, and can optionally leverage analytical covariance approximations. We discuss choices of the prior and propose a procedure for obtaining optimal prior hyperparameter values with a small set of test simulations. We test our method by estimating the covariances of clustering statistics of GADGET-IIIN-body simulations at redshift z = 0.5 using surrogates from a 100–1000× faster particle-mesh code. Taking the sample covariance from 15 000 simulations as the truth, and using an empirical Bayes prior with diagonal blocks, our estimator produces nearly identical Fisher matrix contours for ΛCDM parameters using only 15 simulations of the non-linear dark matter power spectrum. In this case, the number of simulations is so small that the sample covariance is degenerate. We show cases where even with a naïve prior our method improves the estimate. Our framework is applicable to a wide range of cosmological problems where fast surrogates are available.

We use angle-resolved photoemission to map the Fermi surface and quasiparticle dispersion of bulk-like thin films of SrMoO