Publications

Motile cilia are slender, hair-like cellular appendages that spontaneously oscillate under the action of internal molecular motors and are typically found in dense arrays. These active filaments coordinate their beating to generate metachronal waves that drive long-range fluid transport and locomotion. Until now, our understanding of their collective behavior largely comes from the study of minimal models that coarse grain the relevant biophysics and the hydrodynamics of slender structures. Here we build on a detailed biophysical model to elucidate the emergence of metachronal waves on millimeter scales from nanometer-scale motor activity inside individual cilia. Our study of a one-dimensional lattice of cilia in the presence of hydrodynamic and steric interactions reveals how metachronal waves are formed and maintained. We find that, in homogeneous beds of cilia, these interactions lead to multiple attracting states, all of which are characterized by an integer charge that is conserved. This even allows us to design initial conditions that lead to predictable emergent states. Finally, and very importantly, we show that, in nonuniform ciliary tissues, boundaries and inhomogeneities provide a robust route to metachronal waves.

Microtubule-based active matter provides insight into the self-organization of motile interacting constituents. We describe several formulations of microtubule-based 3D active isotropic fluids. Dynamics of these fluids is powered by three types of kinesin motors: a processive motor, a non-processive motor, and a motor which is permanently linked to a microtubule backbone. Another modification uses a specific microtubule crosslinker to induce bundle formation instead of a non-specific polymer depletant. In comparison to the already established system, each formulation exhibits distinct properties. These developments reveal the temporal stability of microtubule-based active fluids while extending their reach and the applicability.

We present a fast direct solver for boundary integral equations on complex surfaces in three dimensions using an extension of the recently introduced recursive strong skeletonization scheme. For problems that are not highly oscillatory, our algorithm computes an LU-like hierarchical factorization of the dense system matrix, permitting application of the inverse in (n) time, where n is the number of unknowns on the surface. The factorization itself also scales linearly with the system size, albeit with a somewhat larger constant. The scheme is built on a level-restricted adaptive octree data structure, and therefore it is compatible with highly nonuniform discretizations. Furthermore, the scheme is coupled with high-order accurate locally-corrected Nyström quadrature methods to integrate the singular and weakly-singular Green's functions used in the integral representations. Our method has immediate applications to a variety of problems in computational physics. We concentrate here on studying its performance in acoustic scattering (governed by the Helmholtz equation) at low to moderate frequencies, and provide rigorous justification for compression of submatrices via proxy surfaces.

Magnetic reconnection can power bright, rapid flares originating from the inner magnetosphere of accreting black holes. We conduct extremely high-resolution (5376 × 2304 × 2304 cells) general-relativistic magnetohydrodynamics simulations, capturing plasmoid-mediated reconnection in a 3D magnetically arrested disk for the first time. We show that an equatorial, plasmoid-unstable current sheet forms in a transient, nonaxisymmetric, low-density magnetosphere within the inner few Schwarzschild radii. Magnetic flux bundles escape from the event horizon through reconnection at the universal plasmoid-mediated rate in this current sheet. The reconnection feeds on the highly magnetized plasma in the jets and heats the plasma that ends up trapped in flux bundles to temperatures proportional to the jet’s magnetization. The escaped flux bundles can complete a full orbit as low-density hot spots, consistent with Sgr A* observations by the GRAVITY interferometer. Reconnection near the horizon produces sufficiently energetic plasma to explain flares from accreting black holes, such as the TeV emission observed from M87. The drop in the mass accretion rate during the flare and the resulting low-density magnetosphere make it easier for very-high-energy photons produced by reconnection-accelerated particles to escape. The extreme-resolution results in a converged plasmoid-mediated reconnection rate that directly determines the timescales and properties of the flare.

Magnetic reconnection can power bright, rapid flares originating from the inner magnetosphere of accreting black holes. We conduct extremely high resolution (5376×2304×2304 cells) general-relativistic magnetohydrodynamics simulations, capturing plasmoid-mediated reconnection in a 3D magnetically arrested disk for the first time. We show that an equatorial, plasmoid-unstable current sheet forms in a transient, non-axisymmetric, low-density magnetosphere within the inner few Schwarzschild radii. Magnetic flux bundles escape from the event horizon through reconnection at the universal plasmoid-mediated rate in this current sheet. The reconnection feeds on the highly-magnetized plasma in the jets and heats the plasma that ends up trapped in flux bundles to temperatures proportional to the jet's magnetization. The escaped flux bundles can complete a full orbit as low-density hot spots, consistent with Sgr A∗ observations by the GRAVITY interferometer. Reconnection near the horizon produces sufficiently energetic plasma to explain flares from accreting black holes, such as the TeV emission observed from M87. The drop in mass accretion rate during the flare, and the resulting low-density magnetosphere make it easier for very high energy photons produced by reconnection-accelerated particles to escape. The extreme resolution results in a converged plasmoid-mediated reconnection rate that directly determines the timescales and properties of the flare.

We compare an analytic model for the evolution of supernova-driven superbubbles with observations of local and high-redshift galaxies, and the properties of intact HI shells in local star-forming galaxies. Our model correctly predicts the presence of superwinds in local star-forming galaxies (e.g., NGC 253) and the ubiquity of outflows near z∼2. We find that high-redshift galaxies may `capture' 20-50\% of their feedback momentum in the dense ISM (with the remainder escaping into the nearby CGM), whereas local galaxies may contain ≲10\% of their feedback momentum from the central starburst. Using azimuthally averaged galaxy properties, we predict that most superbubbles stall and fragment \emph{within} the ISM, and that this occurs at, or near, the gas scale height. We find a consistent interpretation in the observed HI bubble radii and velocities, and predict that most will fragment within the ISM, and that those able to break-out originate from short dynamical time regions (where the dynamical time is shorter than feedback timescales). Additionally, we demonstrate that models with constant star cluster formation efficiency per Toomre mass are inconsistent with the occurrence of outflows from high-z starbursts and local circumnuclear regions.

The nanohertz gravitational wave background (GWB) is believed to be dominated by GW emission from supermassive black hole binaries (SMBHBs). Observations of several dual active galactic nuclei (AGN) strongly suggest a link between AGN and SMBHBs, given that these dual AGN systems will eventually form bound binary pairs. Here we develop an exploratory SMBHB population model based on empirically constrained quasar populations, allowing us to decompose the GWB amplitude into an underlying distribution of SMBH masses, SMBHB number density, and volume enclosing the GWB. Our approach also allows us to self-consistently predict the number of local SMBHB systems from the GWB amplitude. Interestingly, we find the local number density of SMBHBs implied by the common-process signal in the NANOGrav 12.5-yr dataset to be roughly five times larger than previously predicted by other models. We also find that at most ∼25% of SMBHBs can be associated with quasars. Furthermore, our quasar-based approach predicts ≳95% of the GWB signal comes from z≲2.5, and that SMBHBs contributing to the GWB have masses ≳108M⊙. We also explore how different empirical galaxy-black hole scaling relations affect the local number density of GW sources, and find that relations predicting more massive black holes decrease the local number density of SMBHBs. Overall, our results point to the important role that a measurement of the GWB will play in directly constraining the cosmic population of SMBHBs, as well as their connections to quasars and galaxy mergers.

Upcoming measurements of the small-scale primary cosmic microwave background (CMB) temperature and polarization power spectra (TT/TE/EE) are anticipated to yield transformative constraints on new physics, including the effective number of relativistic species in the early universe (Neff). However, at multipoles ℓ≳3000, the primary CMB power spectra receive significant contributions from gravitational lensing. While these modes still carry primordial information, their theoretical modeling requires knowledge of the CMB lensing convergence power spectrum, CκκL, including on small scales where it is affected by nonlinear gravitational evolution and baryonic feedback processes. Thus, the high-ℓ primary CMB is sensitive to these late-time, nonlinear effects. Here, we show that inaccuracies in the modeling of CκκL can yield surprisingly large biases on cosmological parameters inferred from the primary CMB power spectra measured by the upcoming Simons Observatory and CMB-S4 experiments. For CMB-S4, the biases can be as large as 1.6σ on the Hubble constant H0 in a fit to ΛCDM and 1.2σ on Neff in a fit to ΛCDM+Neff.
We show that these biases can be mitigated by explicitly discarding all TT data at ℓ>3000 or by marginalizing over parameters describing baryonic feedback processes, both at the cost of slightly larger error bars. We also discuss an alternative, data-driven mitigation strategy based on delensing the CMB T and E-mode maps. Finally, we show that analyses of upcoming data will require Einstein-Boltzmann codes to be run with much higher numerical precision settings than is currently standard, so as to avoid similar -- or larger -- parameter biases due to inaccurate theoretical predictions.

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on data sets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.

To understand the formation and evolution of the Milky Way disk, we must connect its current properties to its past. We explore hydrodynamical cosmological simulations to investigate how the chemical abundances of stars might be linked to their origins. Using hierarchical clustering of abundance measurements in two Milky Way–like simulations with distributed and steady star formation histories, we find that groups of chemically similar stars comprise different groups in birth place (Rbirth) and time (age). Simulating observational abundance errors (0.05 dex), we find that to trace distinct groups of (Rbirth, age) requires a large vector of abundances. Using 15 element abundances (Fe, O, Mg, S, Si, C, P, Mn, Ne, Al, N, V, Ba, Cr, Co), up to ≈10 groups can be defined with ≈25 percent overlap in (Rbirth, age). We build a simple model to show that in the context of these simulations, it is possible to infer a star's age and Rbirth from abundances with precisions of ±0.06 Gyr and ±1.17 kpc, respectively. We find that abundance clustering is ineffective for a third simulation, where low-α stars form distributed in the disk and early high-α stars form more rapidly in clumps that sink toward the Galactic center as their constituent stars evolve to enrich the interstellar medium. However, this formation path leads to large age dispersions across the [α/Fe]–[Fe/H] plane, which is inconsistent with the Milky Way's observed properties. We conclude that abundance clustering is a promising approach toward charting the history of our Galaxy.