Publications

We present a fluctuating boundary integral method (FBIM) for overdamped Brownian Dynamics (BD) of two-dimensional periodic suspensions of rigid particles of complex shape immersed in a Stokes fluid. We develop a novel approach for generating Brownian displacements that arise in response to the thermal fluctuations in the fluid. Our approach relies on a first-kind boundary integral formulation of a mobility problem in which a random surface velocity is prescribed on the particle surface, with zero mean and covariance proportional to the Green's function for Stokes flow (Stokeslet). This approach yields an algorithm that scales linearly in the number of particles for both deterministic and stochastic dynamics, handles particles of complex shape, achieves high order of accuracy, and can be generalized to three dimensions and other boundary conditions. We show that Brownian displacements generated by our method obey the discrete fluctuation-dissipation balance relation (DFDB). Based on a recently-developed Positively Split Ewald method [A. M. Fiore, F. Balboa Usabiaga, A. Donev and J. W. Swan, J. Chem. Phys., 146, 124116, 2017], near-field contributions to the Brownian displacements are efficiently approximated by iterative methods in real space, while far-field contributions are rapidly generated by fast Fourier-space methods based on fluctuating hydrodynamics. FBIM provides the key ingredient for time integration of the overdamped Langevin equations for Brownian suspensions of rigid particles. We demonstrate that FBIM obeys DFDB by performing equilibrium BD simulations of suspensions of starfish-shaped bodies using a random finite difference temporal integrator.

The brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to years. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site, extracellular recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1,024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track large numbers of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

In suspensions of microorganisms, pattern formation can arise from the interplay of chemotaxis and the fluid flows collectively generated by the organisms themselves. Here we investigate the resulting pattern formation in square and elongated domains in the context of two distinct models of locomotion in which the chemoattractant dynamics is fully coupled to the fluid flows and swimmer motion. Analyses for both models reveal an aggregative instability due to chemotaxis, independent of swimmer shape and type, and a hydrodynamic instability for “pusher” swimmers. We discuss the similarities and differences between the models. Simulations reveal a critical length scale of the swimmer aggregates and this feature can be utilized to stabilize swimmer concentration patterns into quasi-one-dimensional bands by varying the domain size. These concentration bands transition to traveling pulses under an external chemoattractant gradient, as observed in experiments with chemotactic bacteria.

A key unmet challenge in interpreting omics experiments is inferring biological meaning in the context of public functional genomics data. We developed a computational framework, Your Evidence Tailored Integration (YETI; http://yeti.princeton.edu/ ), which creates specialized functional interaction maps from large public datasets relevant to an individual omics experiment. Using this tailored integration, we predicted and experimentally confirmed an unexpected divergence in viral replication after seasonal or pandemic human influenza virus infection.

For smooth bounded domains in ℝn, we prove upper and lower L2 bounds on the boundary data of Neumann eigenfunctions, and we prove quasiorthogonality of this boundary data in a spectral window. The bounds are tight in the sense that both are independent of the eigenvalues; this is achieved by working with an appropriate norm for boundary functions, which includes a spectral weight, that is, a function of the boundary Laplacian. This spectral weight is chosen to cancel concentration at the boundary that can happen for whispering gallery-type eigenfunctions. These bounds are closely related to wave equation estimates due to Tataru. Using this, we bound the distance from an arbitrary Helmholtz parameter E>0 to the nearest Neumann eigenvalue in terms of boundary normal derivative data of a trial function u solving the Helmholtz equation (Δ−E)u=0. This inclusion bound improves over previously known bounds by a factor of E5/6, analogously to a recently improved inclusion bound in the Dirichlet case due to the first two authors. Finally, we apply our theory to present an improved numerical implementation of the method of particular solutions for computation of Neumann eigenpairs on smooth planar domains. We show that the new inclusion bound improves the relative accuracy in a computed Neumann eigenvalue (around the 42000th) from nine to fourteen digits, with negligible extra numerical effort.

Pulsar timing array (PTA) collaborations in North America, Australia, and Europe, have been exploiting the exquisite timing precision of millisecond pulsars over decades of observations to search for correlated timing deviations induced by gravitational waves (GWs). The discovery space of this nanohertz band is potentially rich with populations of inspiraling supermassive black-holes binaries, decaying cosmic string networks, relic post-inflation GWs, and even non-GW imprints of axionic dark matter.
This article aims to provide an understanding of the exciting open science questions in cosmology, galaxy evolution, and fundamental physics that will be addressed by the detection and study of GWs at nanohertz frequencies. The focus of the article is on providing an understanding of the mechanisms by which PTAs can address specific questions in these fields, and to outline some of the subtleties and difficulties in each case. The material included is weighted most heavily towards the questions which we expect will be answered in the coming months to decades with PTAs; however, we have made efforts to include most currently anticipated applications of nanohertz GWs.

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over
all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web (1). To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe (2–9) and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D3M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D3M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D3M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.

We present a model for the interaction of the GD-1 stellar stream with a massive perturber that naturally explains many of the observed stream features, including a gap and an off-stream spur of stars. The model involves an impulse by a fast encounter, after which the stream grows a loop of stars at different orbital energies. At specific viewing angles, this loop appears offset from the stream track. The configuration-space observations are sensitive to the mass, age, impact parameter, and total velocity of the encounter, and future velocity observations will constrain the full velocity vector of the perturber. A quantitative comparison of the spur and gap features prefers models where the perturber is in the mass range of 106M⊙ to 108M⊙. Orbit integrations back in time show that the stream encounter could not have been caused by any known globular cluster or dwarf galaxy, and mass, size and impact-parameter arguments show that it could not have been caused by a molecular cloud in the Milky Way disk. The most plausible explanation for the gap-and-spur structure is an encounter with a dark matter substructure, like those predicted to populate galactic halos in ΛCDM cosmology. However, the expected densities of ΛCDM subhalos in this mass range and in this part of the Milky Way are 2−3σ lower than the inferred high density of the GD-1 perturber. This observation opens up the possibility that detailed observations of streams could measure the mass spectrum of dark-matter substructures and even identify individual substructures and their orbits in the Galactic halo.

Two fundamental difficulties are encountered in the numerical evaluation of time-dependent layer potentials. One is the quadratic cost of history dependence, which has been successfully addressed by splitting the potentials into two parts - a local part that contains the most recent contributions and a history part that contains the contributions from all earlier times. The history part is smooth, easily discretized using high-order quadratures, and straightforward to compute using a variety of fast algorithms. The local part, however, involves complicated singularities in the underlying Green's function. Existing methods, based on exchanging the order of integration in space and time, are able to achieve high order accuracy, but are limited to the case of stationary boundaries. Here, we present a new quadrature method that leaves the order of integration unchanged, making use of a change of variables that converts the singular integrals with respect to time into smooth ones. We have also derived asymptotic formulas for the local part that lead to fast and accurate hybrid schemes, extending earlier work for scalar heat potentials and applicable to moving boundaries. The performance of the overall scheme is demonstrated via numerical examples.

We produce light curves for all ~34,000 targets observed with K2 in Campaign 17 (C17), identifying 34 planet candidates, 184 eclipsing binaries, and 222 other periodic variables. The location of the C17 field means follow-up can begin immediately now that the campaign has concluded and interesting targets have been identified. The C17 field has a large overlap with C6, so this latest campaign also offers a rare opportunity to study a large number of targets already observed in a previous K2 campaign. The timing of the C17 data release, shortly before science operations begin with the Transiting Exoplanet Survey Satellite (TESS), also lets us exercise some of the tools and methods developed for identification and dissemination of planet candidates from TESS. We find excellent agreement between these results and those identified using only K2-based tools. Among our planet candidates are several planet candidates with sizes < 4 R_E and orbiting stars with KepMag < 10 (indicating good RV targets of the sort TESS hopes to find) and a Jupiter-sized single-transit event around a star already hosting a 6 d planet candidate.