Publications

Explaining the currently observed magnetic fields in galaxies requires relatively strong seeding in the early Universe. One theory proposes that magnetic fields of the order of μG were expelled by supernova (SN) explosions after primordial, nG or weaker fields were amplified in stellar interiors. In this work, we calculate the maximum magnetic energy that can be injected in the interstellar medium by a stellar cluster of mass Mcl based on what is currently known about stellar magnetism. We consider early-type stars and adopt either a Salpeter or a top-heavy IMF. For their magnetic fields, we adopt either a Gaussian or a bimodal distribution. The Gaussian model assumes that all massive stars are magnetized with 103<⟨B∗⟩<104 G, while the bimodal, consistent with observations of Milky Way stars, assumes only 5-10 per cent of OB stars have 103<⟨B∗⟩<104 G, while the rest have 10<⟨B∗⟩<102 G. We find that the maximum magnetic energy that can be injected by a stellar population is between 10−10−10−7 times the total SN energy. The highest end of these estimates is about five orders of magnitude lower than what is usually employed in cosmological simulations, where about 10−2 of the SN energy is injected as magnetic. Pure advection of the stellar magnetic field by SN explosions is a good candidate for seeding a dynamo, but not enough to magnetize galaxies. Assuming SNe as main mechanism for galactic magnetization, the magnetic field cannot exceed an intensity of 10−7 G in the best-case scenario for a population of 105 solar masses in a superbubble of 300 pc radius, while more typical values are between 10−10−10−9~G. Therefore, other scenarios for galactic magnetization at high redshift need to be explored.

Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and computational accelerators like GPUs have facilitated forward modeling of the Universe with differentiable simulations. Current differentiable cosmological simulations are limited by memory, thus are subject to a trade-off between time and space/mass resolution. They typically integrate for only tens of time steps, unlike the standard non-differentiable simulations. We present a new approach free of such constraints, using the adjoint method and reverse time integration. It enables larger and more accurate forward modeling, and will improve gradient based optimization and inference. We implement it in a particle-mesh (PM) N-body library pmwd (particle-mesh with derivatives). Based on the powerful AD system JAX, pmwd is fully differentiable, and is highly performant on GPUs.

The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and dark matter on cosmological scales, requires numerical simulations. Differentiable simulations provide gradients of the cosmological parameters, that can accelerate the extraction of physical information from statistical analyses of observational data. The deep learning revolution has brought not only myriad powerful neural networks, but also breakthroughs including automatic differentiation (AD) tools and computational accelerators like GPUs, facilitating forward modeling of the Universe with differentiable simulations. Because AD needs to save the whole forward evolution history to backpropagate gradients, current differentiable cosmological simulations are limited by memory. Using the adjoint method, with reverse time integration to reconstruct the evolution history, we develop a differentiable cosmological particle-mesh (PM) simulation library pmwd (particle-mesh with derivatives) with a low memory cost. Based on the powerful AD library JAX, pmwd is fully differentiable, and is highly performant on GPUs.

Astrocytes have been shown to play a central role in Alzheimer’s Disease (AD). However, the genes and biological pathways underlying disease manifestation are unknown, and it is unclear whether regional molecular differences among astrocytes contribute to regional specificity of disease. Here, we began to address these challenges with integrated experimental and computational approaches. We constructed a human astrocyte-specific functional gene network using Bayesian integration of a large compendium of human functional genomics data, as well as regional astrocyte gene expression profiles we generated in the mouse. This network identifies likely region-specific astrocyte pathways that operate in healthy brains. We leveraged our findings to compile genome-wide astrocyte-associated disease-gene predictions, employing a novel network-guided differential expression analysis (NetDIFF). We also used this data to predict a list of astrocyte-expressed genes mediating region-specific human disease, using a network-guided shortest path method (NetPATH). Both the network and our results are publicly available using an interactive web interface at http://astrocyte.princeton.edu. Our experimental and computational studies propose a strategy for disease gene and pathway prediction that may be applied to a host of human neurological disorders.

We present a data-driven method for reconstructing the galactic acceleration field from phase-space measurements of stellar streams. Our approach is based on a flexible and differentiable fit to the stream in phase-space, enabling a direct estimate of the acceleration vector along the stream. Reconstruction of the local acceleration field can be applied independently to each of several streams, allowing us to sample the acceleration field due to the underlying galactic potential across a range of scales. Our approach is methodologically different from previous works, since a model for the gravitational potential does not need to be adopted beforehand. Instead, our flexible neural-network-based model treats the stream as a collection of orbits with a locally similar mixture of energies, rather than assuming that the stream delineates a single stellar orbit. Accordingly, our approach allows for distinct regions of the stream to have different mean energies, as is the case for real stellar streams. Once the acceleration vector is sampled along the stream, standard analytic models for the galactic potential can then be rapidly constrained. We find our method recovers the correct parameters for a ground-truth triaxial logarithmic halo potential when applied to simulated stellar streams. Alternatively, we demonstrate that a flexible potential can be constrained with a neural network, though standard multipole expansions can also be constrained. Our approach is applicable to simple and complicated gravitational potentials alike, and enables potential reconstruction from a fully data-driven standpoint using measurements of slowly phase-mixing tidal debris.

We use tensor network techniques to obtain high-order perturbative diagrammatic expansions for the quantum many-body problem at very high precision. The approach is based on a tensor train parsimonious representation of the sum of all Feynman diagrams, obtained in a controlled and accurate way with the tensor cross interpolation algorithm. It yields the full time evolution of physical quantities in the presence of any arbitrary time-dependent interaction. Our benchmarks on the Anderson quantum impurity problem, within the real-time nonequilibrium Schwinger-Keldysh formalism, demonstrate that this technique supersedes diagrammatic quantum Monte Carlo by orders of magnitude in precision and speed, with convergence rates \(1/N2\) or faster, where N is the number of function evaluations. The method also works in parameter regimes characterized by strongly oscillatory integrals in high dimension, which suffer from a catastrophic sign problem in quantum Monte Carlo calculations. Finally, we also present two exploratory studies showing that the technique generalizes to more complex situations: a double quantum dot and a single impurity embedded in a two-dimensional lattice.

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

The contemporaneous detection of gravitational waves and gamma rays from the GW170817/GRB 170817A, followed by kilonova emission a day after, confirmed compact binary neutron-star mergers as progenitors of short-duration gamma-ray bursts (GRBs), and cosmic sources of heavy r-process nuclei. However, the nature (and lifespan) of the merger remnant and the energy reservoir powering these bright gamma-ray flashes remains debated, while the first minutes after the merger are unexplored at optical wavelengths. Here, we report the earliest discovery of bright thermal optical emission associated with the short GRB 180618A with extended gamma-ray emission, with ultraviolet and optical multicolour observations starting as soon as 1.4 minutes post-burst. The spectrum is consistent with a fast-fading afterglow and emerging thermal optical emission at 15 minutes post-burst, which fades abruptly and chromatically (flux density Fν∝t−α, α=4.6±0.3) just 35 minutes after the GRB. Our observations from gamma rays to optical wavelengths are consistent with a hot nebula expanding at relativistic speeds, powered by the plasma winds from a newborn, rapidly-spinning and highly magnetized neutron star (i.e. a millisecond magnetar), whose rotational energy is released at a rate Lth∝t−(2.22±0.14) to reheat the unbound merger-remnant material. These results suggest such neutron stars can survive the collapse to a black hole on timescales much larger than a few hundred milliseconds after the merger, and power the GRB itself through accretion. Bright thermal optical counterparts to binary merger gravitational wave sources may be common in future wide-field fast-cadence sky surveys.

We examine the quenching characteristics of 328 isolated dwarf galaxies (108<Mstar/M⊙<1010) within the \Rom{} cosmological hydrodynamic simulation. Using mock observation methods, we identify isolated dwarf galaxies with quenched star formation and make direct comparisons to the quenched fraction in the NASA Sloan Atlas (NSA). Similar to other cosmological simulations, we find a population of quenched, isolated dwarf galaxies below Mstar<109M⊙ not detected within the NSA. We find that the presence of massive black holes (MBHs) in \Rom{} is largely responsible for the quenched, isolated dwarfs, while isolated dwarfs without an MBH are consistent with quiescent fractions observed in the field. Quenching occurs between z=0.5−1, during which the available supply of star-forming gas is heated or evacuated by MBH feedback. Mergers or interactions seem to play little to no role in triggering the MBH feedback. At low stellar masses, Mstar≲109.3M⊙, quenching proceeds across several Gyr as the MBH slowly heats up gas in the central regions. At higher stellar masses, Mstar≳109.3M⊙, quenching occurs rapidly within 1 Gyr, with the MBH evacuating gas from the central few kpc of the galaxy and driving it to the outskirts of the halo. Our results indicate the possibility of substantial star formation suppression via MBH feedback within dwarf galaxies in the field. On the other hand, the apparent over-quenching of dwarf galaxies due to MBH suggests higher resolution and/or better modeling is required for MBHs in dwarfs, and quenched fractions offer the opportunity to constrain current models.

Inspired by the recent realization of a two-dimensional (2-D) chiral fluid as an active monolayer droplet moving atop a 3-D Stokesian fluid, we formulate mathematically its free-boundary dynamics. The surface droplet is described as a general 2-D linear, incompressible and isotropic fluid, having a viscous shear stress, an active chiral driving stress and a Hall stress allowed by the lack of time-reversal symmetry. The droplet interacts with itself through its driven internal mechanics and by driving flows in the underlying 3-D Stokes phase. We pose the dynamics as the solution to a singular integral–differential equation, over the droplet surface, using the mapping from surface stress to surface velocity for the 3-D Stokes equations. Specializing to the case of axisymmetric droplets, exact representations for the chiral surface flow are given in terms of solutions to a singular integral equation, solved using both analytical and numerical techniques. For a disc-shaped monolayer, we additionally employ a semi-analytical solution that hinges on an orthogonal basis of Bessel functions and allows for efficient computation of the monolayer velocity field, which ranges from a nearly solid-body rotation to a unidirectional edge current, depending on the subphase depth and the Saffman–Delbrück length. Except in the near-wall limit, these solutions have divergent surface shear stresses at droplet boundaries, a signature of systems with codimension-one domains embedded in a 3-D medium. We further investigate the effect of a Hall viscosity, which couples radial and transverse surface velocity components, on the dynamics of a closing cavity. Hall stresses are seen to drive inward radial motion, even in the absence of edge tension.