1967 Publications

“Super-Kilonovae” from Massive Collapsars as Signatures of Black-Hole Birth in the Pair-instability Mass Gap

D. M. Siegel, A. Agarwal, J. Barnes, B. Metzger, M. Renzo, A. Villar

The core collapse of rapidly rotating massive ~10 Msun stars ("collapsars"), and resulting formation of hyper-accreting black holes, are a leading model for the central engines of long-duration gamma-ray bursts (GRB) and promising sources of r-process nucleosynthesis. Here, we explore the signatures of collapsars from progenitors with extremely massive helium cores >130 Msun above the pair-instability mass gap. While rapid collapse to a black hole likely precludes a prompt explosion in these systems, we demonstrate that disk outflows can generate a large quantity (up to >50 Msun) of ejecta, comprised of >5-10 Msun in r-process elements and ~0.1-1 Msun of 56Ni, expanding at velocities ~0.1c. Radioactive heating of the disk-wind ejecta powers an optical/infrared transient, with a characteristic luminosity ∼1042 erg s−1 and spectral peak in the near-infrared (due to the high optical/UV opacities of lanthanide elements) similar to kilonovae from neutron star mergers, but with longer durations ≳ 1 month. These "super-kilonovae" (superKNe) herald the birth of massive black holes >60 Msun, which, as a result of disk wind mass-loss, can populate the pair-instability mass gap 'from above' and could potentially create the binary components of GW190521. SuperKNe could be discovered via wide-field surveys such as those planned with the Roman Space Telescope or via late-time infrared follow-up observations of extremely energetic GRBs. Gravitational waves of frequency ~0.1-50 Hz from non-axisymmetric instabilities in self-gravitating massive collapsar disks are potentially detectable by proposed third-generation intermediate and high-frequency observatories at distances up to hundreds of Mpc; in contrast to the "chirp" from binary mergers, the collapsar gravitational-wave signal decreases in frequency as the disk radius grows ("sad trombone").

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November 4, 2021

Radiative Turbulent Flares in Magnetically Dominated Plasmas

J. Nättilä, A. Beloborodov

We perform 2D and 3D kinetic simulations of reconnection-mediated turbulent flares in a magnetized electron-positron plasma, with weak and strong radiative cooling. Such flares can be generated around neutron stars and accreting black holes. We focus on the magnetically dominated regime where tension of the background magnetic field lines exceeds the plasma rest-mass density by a factor σ0 > 1. In the simulations, turbulence is excited on a macroscopic scale l0, and we observe that it develops by forming thin, dynamic current sheets on various scales. The deposited macroscopic energy dissipates by energizing thermal and nonthermal particles. The particle energy distribution is shaped by impulsive acceleration in reconnecting current sheets, gradual stochastic acceleration, and radiative losses. We parameterize radiative cooling by the ratio ${ \mathcal A }$ of light-crossing time l0/c to a cooling timescale, and study the effect of increasing ${ \mathcal A }$ on the flare. When radiative losses are sufficiently weak, ${ \mathcal A }\lt {\sigma }_{0}^{-1}$, the produced emission is dominated by stochastically accelerated particles, and the radiative power depends logarithmically on ${ \mathcal A }$. The resulting radiation spectrum of the flare is broad and anisotropic. In the strong-cooling regime, ${ \mathcal A }\gt {\sigma }_{0}^{-1}$, stochastic acceleration is suppressed, while impulsive acceleration in the current sheets continues to operate. As ${ \mathcal A }$ increases further, the emission becomes dominated by thermal particles. Our simulations offer a new tool to study particle acceleration by turbulence, especially at high energies, where cooling competes with acceleration. We find that the particle distribution is influenced by strong intermittency of dissipation, and stochastic acceleration cannot be described by a universal diffusion coefficient.

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Encoding Priors with Group Sparsity for Model Learning From Limited and Noisy Biological Data

Suryanarayana Maddu, Dominik Sturm, Bevan L Cheeseman, C. Müller, Ivo F Sbalzarini

Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e.g., in applications like turbulence, combustion, and shock propagation. Numerical approximation also requires knowing the PDE in order to construct problem-specific discretizations. Systematically deriving such solution-adaptive discrete operators, however, is a current challenge. Here we present an artificial neural network architecture for data-driven learning of problemand resolution-specific local discretizations of nonlinear PDEs. Our proposed method achieves numerically stable discretization of the operators in an unknown nonlinear PDE by spatially and temporally adaptive parametric pooling on regular Cartesian grids, and by incorporating knowledge about discrete time integration. Knowing the actual PDE is not necessary, as solution data is sufficient to train the network to learn the discrete operators. A once-trained neural architecture model can be used to predict solutions of the PDE on larger spatial domains and for longer times than it was trained for, hence addressing the problem of PDE-constrained extrapolation from data. We present demonstrative examples on long-term forecasting of hard numerical problems including equation-free forecasting of non-linear dynamics of forced Burgers problem on coarse spatio-temporal grids.

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Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition

Wendy Hui Kyong Chun, A. Barnett

mathematical illustrations by Alex H. Barnett: In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data's predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible.

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The Astrophysical Variance in Gaia-Radial Velocity Spectrometer Spectra

R. Rampalli, M. Ness, S. Wylie

Large surveys are providing a diversity of spectroscopic observations with Gaia alone set to deliver millions of Ca-triplet-region spectra across the Galaxy. We aim to understand the dimensionality of the chemical abundance information in the Gaia–Radial Velocity Spectrometer (RVS) data to inform galactic archeology pursuits. We fit a quadratic model of four primary sources of variability, described by labels of Teff, \mathrm{log}g, [Fe/H], and [α/Fe], to the normalized flux of 10,802 red-clump stars from the Gaia-RVS-like Abundances and Radial velocity Galactic Origins Survey (ARGOS). We examine the residuals between ARGOS spectra and the models and find that the models capture the flux variability across 85 percent of the wavelength region. The remaining residual variance is concentrated to the Ca-triplet features, at an amplitude up to 12 percent of the normalized flux. We use principal component analysis on the residuals and find orthogonal correlations in the Ca-triplet core and wings. This variability, not captured by our model, presumably marks departures from the completeness of the 1D LTE label description. To test the indication of low-dimensionality, we turn to abundance-space to infer how well we can predict measured [Si/H], [O/H], [Ca/H], [Ni/H], and [Al/H] abundances from the Gaia-RVS-like Radial Velocity Experiment survey with models of Teff, \mathrm{log}g, [Fe/H], and [Mg/Fe]. We find that we can nearly entirely predict these abundances. Using high-precision Apache Point Observatory Galactic Evolution Experiment abundances, we determine that a measurement uncertainty of <0.03 dex is required to capture additional information from these elements. This indicates that a four-label model sufficiently describes chemical abundance variance for an approximate signal-to-noise ratio <200 per pixel, in Gaia-RVS spectra.

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Topological Materials Discovery from Crystal Symmetry

Benjamin J. Wieder, Barry Bradlyn, J. Cano, Zhijun Wang, Maia G. Vergniory, Luis Elcoro, Alexey A. Soluyanov, Claudia Felser, Titus Neupert, Nicolas Regnault, B. A. Bernevig
Topological materials discovery has evolved at a rapid pace over the past 15 years following the identification of the first nonmagnetic topological insulators (TIs), topological crystalline insulators (TCIs), and 3D topological semimetals (TSMs). Most recently, through complete analyses of symmetry-allowed band structures - including the theory of Topological Quantum Chemistry (TQC) - researchers have determined crystal-symmetry-enhanced Wilson-loop and complete symmetry-based indicators for nonmagnetic topological phases, leading to the discovery of higher-order TCIs and TSMs. The recent application of TQC and related methods to high-throughput materials discovery has revealed that over half of all of the known stoichiometric, solid-state, nonmagnetic materials are topological at the Fermi level, over 85 percent of the known stoichiometric materials host energetically isolated topological bands, and that just under 2/3 of the energetically isolated bands in known materials carry the stable topology of a TI or TCI. In this Review, we survey topological electronic materials discovery in nonmagnetic crystalline solids from the prediction of the first 2D and 3D TIs to the recently introduced methods that have facilitated large-scale searches for topological materials. We also discuss future venues for the identification and manipulation of solid-state topological phases, including charge-density-wave compounds, magnetic materials, and 2D few-layer devices.
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Band manipulation and spin texture in interacting moiré helical edges

Yang-Zhi Chou, J. Cano, J. H. Pixley
We develop a theory for manipulating the effective band structure of interacting helical edge states realized on the boundary of two-dimensional time-reversal symmetric topological insulators. For sufficiently strong interaction, an interacting edge band gap develops, spontaneously breaking time-reversal symmetry on the edge. The resulting spin texture, as well as the energy of the the time-reversal breaking gaps, can be tuned by an external moiré potential (i.e., a superlattice potential). Remarkably, we establish that by tuning the strength and period of the potential, the interacting gaps can be fully suppressed and interacting Dirac points re-emerge. In addition, nearly flat bands can be created by the moiré potential with a sufficiently long period. Our theory provides an unprecedented way to enhance the coherence length of interacting helical edges by suppressing the interacting gap. The implications of this finding for ongoing experiments on helical edge states is discussed.
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Universality in the onset of quantum chaos in many-body systems

Tyler LeBlond, D. Sels, A. Polkovnikov, Marcos Rigol
We show that the onset of quantum chaos at infinite temperature in two many-body one-dimensional lattice models, the perturbed spin-1/2 XXZ and Anderson models, is characterized by universal behavior. Specifically, we show that the onset of quantum chaos is marked by maxima of the typical fidelity susceptibilities that scale with the square of the inverse average level spacing, saturating their upper bound, and that the strength of the integrability- or localization-breaking perturbation at these maxima decreases with increasing system size. We also show that the spectral function below the
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Dynamical obstruction to localization in a disordered spin chain

We analyze a one-dimensional XXZ spin chain in a disordered magnetic field. As the main probes of the system's behavior we use the sensitivity of eigenstates to adiabatic transformations, as expressed through the fidelity susceptibility, in conjunction with the low frequency asymptotes of the spectral function. We identify a region of maximal chaos -- with exponentially enhanced susceptibility -- which separates the many-body localized phase from the diffusive ergodic phase. This regime is characterized by slow transport and we argue that the presence of such slow dynamics is incompatible with the localization transition in the thermodynamic limit. Instead of localizing, the system appears to enter a universal subdiffusive relaxation regime at moderate values of disorder, where the spectral function of the local longitudinal magnetization is inversely proportional to the frequency, corresponding to logarithmic in time relaxation of its auto-correlation function.
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