Publications

Planets accompany most sun-like stars. The orbits of many are sufficiently close that they will be engulfed when their host stars ascend the giant branch. This Letter compares the power generated by orbital decay of an engulfed planet to the intrinsic stellar luminosity. Orbital decay power is generated by drag on the engulfed companion by the surrounding envelope. As stars ascend the giant branch their envelope density drops and so does the power injected through orbital decay, scaling approximately as Ldecay∝R−9/2∗. Their luminosity, however, increases along the giant branch. These opposed scalings indicate a crossing, where Ldecay=L∗. We consider the engulfment of planets along isochrones in the Hertzsprung-Russell (H-R) diagram. We find that the conditions for such a crossing occur around L∗≈102~L⊙ (or a≈0.1~au) for Jovian planetary companions. The consumption of closer-in giant planets, such as hot Jupiters, leads to Ldecay≫L∗, while more distant planets such as warm Jupiters, a≈0.5~au, lead to minor perturbations of their host stars with Ldecay≪L∗. Our results map out the parameter space along the giant branch in the H-R Diagram where interaction with planetary companions leads to significant energetic disturbance of host stars.

We have used a combination of ultrafast coherent phonon spectroscopy, ultrafast thermometry, and time-dependent Landau theory to study the inversion symmetry breaking phase transition at Tc=200 K in the strongly spin-orbit coupled correlated metal Cd2Re2O7. We establish that the structural distortion at Tc is a secondary effect through the absence of any softening of its associated phonon mode, which supports a purely electronically driven mechanism. However, the phonon lifetime exhibits an anomalously strong temperature dependence that decreases linearly to zero near Tc. We show that this behavior naturally explains the spurious appearance of phonon softening in previous Raman spectroscopy experiments and should be a prevalent feature of correlated electron systems with linearly coupled order parameters.

We study numerically the expansion dynamics of an initially confined quantum wave packet in the presence of a disordered potential and a uniform bias force. For white-noise disorder, we find that the wave packet develops asymmetric algebraic tails for any ratio of the force to the disorder strength. The exponent of the algebraic tails decays smoothly with that ratio and no evidence of a critical behavior on the wave density profile is found. Algebraic localization features a series of critical values of the force-to-disorder strength where the m-th position moment of the wave packet diverges. Below the critical value for the m-th moment, we find fair agreement between the asymptotic long-time value of the m-th moment and the predictions of diagrammatic calculations. Above it, we find that the m-th moment grows algebraically in time. For correlated disorder, we find evidence of systematic delocalization, irrespective to the model of disorder. More precisely, we find a two-step dynamics, where both the center-of-mass position and the width of the wave packet show transient localization, similar to the white-noise case, at short time and delocalization at sufficiently long time. This correlation-induced delocalization is interpreted as due to the decrease of the effective de Broglie wavelength, which lowers the effective strength of the disorder in the presence of finite-range correlations.

Perspective functions arise explicitly or implicitly in various forms in applied mathematics and in statistical data analysis. To date, no systematic strategy is available to solve the associated, typically nonsmooth, optimization problems. In this paper, we fill this gap by showing that proximal methods provide an efficient framework to model and solve problems involving perspective functions. We study the construction of the proximity operator of a perspective function under general assumptions and present important instances in which the proximity operator can be computed explicitly or via straightforward numerical operations. These results constitute central building blocks in the design of proximal optimization algorithms. We showcase the versatility of the framework by designing novel proximal algorithms for state-of-the-art regression and variable selection schemes in high-dimensional statistics.

\textbf{Background:} No microbe exists in isolation, and few live in environments with only members of their own kingdom or domain. As microbiome studies become increasingly more interested in the interactions between microbes than in cataloging which microbes are present, the variety of microbes in the community should be considered. However, the majority of ecological interaction networks for microbiomes built to date have included only bacteria. Joint association inference across multiple domains of life, e.g., fungal communities (the mycobiome) and bacterial communities, has remained largely elusive. \textbf{Results:} Here, we present a novel extension of the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework that allows statistical inference of cross-domain associations from targeted amplicon sequencing data. For human lung and skin micro- and mycobiomes, we show that cross-domain networks exhibit higher connectivity, increased network stability, and similar topological re-organization patterns compared to single-domain networks. We also validate in vitro a small number of cross-domain interactions predicted by the skin association network. \textbf{Conclusions:} For the human lung and skin micro- and mycobiomes, our findings suggest that fungi play a stabilizing role in ecological network organization. Our study suggests that computational efforts to infer association networks that include all forms of microbial life, paired with large-scale culture-based association validation experiments, will help formulate concrete hypotheses about the underlying biological mechanisms of species interactions and, ultimately, help understand microbial communities as a whole.

Quantum entities manifest themselves as either particles or waves. In a physical system containing a very large number of identical particles, such as electrons in a material, individualistic (particle-like) behavior prevails at high temperatures. At low temperatures, collective behavior emerges, and excitations of the system in this regime are best described as waves—long-lived phenomena that are periodic in both space and time and often dubbed “coherent excitations” by physicists. On page 186 of this issue, Goremychkin et al. (1) used experiment and theory to describe the emergence of coherent excitations in a complex quantum system with strong interactions. They studied a ceriumpalladium compound, CePd3, in which the very localized electrons of 4f orbitals of Ce interact with the much more itinerant conduction electrons of the extended d orbitals of Pd at low temperatures to create a wavelike state.

We introduce a simple scheme to efficiently compute photon exchange-correlation contributions due to the coupling to transversal photons as formulated in the newly developed quantum-electrodynamical density functional theory (QEDFT). Our construction employs the optimized-effective potential (OEP) approach by means of the Sternheimer equation to avoid the explicit calculation of unoccupied states. We demonstrate the efficiency of the scheme by applying it to an exactly solvable GaAs quantum ring model system, a single azulene molecule, and chains of sodium dimers, all located in optical cavities and described in full real space. While the first example is a two-dimensional system and allows to benchmark the employed approximations, the latter two examples demonstrate that the correlated electron-photon interaction appreciably distorts the ground-state electronic structure of a real molecule. By using this scheme, we not only construct typical electronic observables, such as the electronic ground-state density, but also illustrate how photon observables, such as the photon number, and mixed electron-photon observables, e.g. electron-photon correlation functions, become accessible in a DFT framework. This work constitutes the first three-dimensional ab-initio calculation within the new QEDFT formalism and thus opens up a new computational route for the ab-initio study of correlated electron-photon systems in quantum cavities.

We present the measurement of the kinematic Sunyaev-Zel'dovich (kSZ) effect in Fourier space, rather than in real space. We measure the density-weighted pairwise kSZ power spectrum, the first use of this promising approach, by cross-correlating a cleaned Cosmic Microwave Background (CMB) temperature map, which jointly uses both Planck Release 2 and Wilkinson Microwave Anisotropy Probe nine-year data, with the two galaxy samples, CMASS and LOWZ, derived fr om the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12. With the current data, we constrain the average optical depth τ multiplied by the ratio of the Hubble parameter at redshift z and the present day, E=H/H0; we find τE=(3.95±1.62)×10−5 for LOWZ and τE=(1.25±1.06)×10−5 for CMASS, with the optimal angular radius of an aperture photometry filter to estimate the CMB temperature distortion associ ated with each galaxy. By repeating the pairwise kSZ power analysis for various aperture radii, we measure the optical depth as a function of aperture ra dii. While this analysis results in the kSZ signals with only evidence for a detection, S/N=2.54 for LOWZ and 1.24 for CMASS, the combination of future CMB and spectroscopic galaxy surveys should enable precision measurements. We estimate that the combination of CMB-S4 and data from DESI shoul d yield detections of the kSZ signal with S/N=70−100, depending on the resolution of CMB-S4.

We search for an isotropic stochastic gravitational-wave background (GWB) in the newly released 11-year dataset from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). While we find no significant evidence for a GWB, we place constraints on a GWB from a population of supermassive black-hole binaries, from cosmic strings, and from a primordial GWB. For the first time, we find that the GWB upper limits and detection statistics are sensitive to the Solar System ephemeris (SSE) model used, and that SSE errors can mimic a GWB signal. To mitigate this effect, we developed and implemented a novel approach that bridges systematic SSE differences, producing the first PTA constraints that are robust against SSE uncertainties. We place a 95% upper limit on the GW strain amplitude of AGWB<1.45×10−15 at a frequency of f=1 yr−1 for a fiducial f−2/3 power-law spectrum, and with inter-pulsar correlations modeled. This is a factor of ∼2 improvement over the NANOGrav 9-year limit, calculated using the same procedure. Previous PTA upper limits on the GWB will need revision in light of SSE systematic uncertainties. We also characterize the combined influence of the mass-density of stars in galactic cores, the eccentricity of binaries at formation, and the relation between the mass of the central supermassive black hole and the galactic bulge (the MBH−Mbulge relation). We constrain cosmic-string tension on the basis of recent simulations, yielding an SSE-marginalized 95\% upper limit on the cosmic string tension of Gμ<5.3×10−11---a factor of ∼2 better than the NANOGrav 9-year constraints. We then use our new Bayesian SSE model to limit the energy density of primordial GWBs, corresponding to ΩGWB(f)h2<3.4×10−10 for a radiation-dominated inflationary era.

Inspired by coarse-graining approaches used in physics, we show how similar algorithms can be adapted for data. The resulting algorithms are based on layered tree tensor networks and scale linearly with both the dimension of the input and the training set size. Computing most of the layers with an unsupervised algorithm, then optimizing just the top layer for supervised classification of the MNIST and fashion-MNIST data sets gives very good results. We also discuss mixing a prior guess for supervised weights together with an unsupervised representation of the data, yielding a smaller number of features nevertheless able to give good performance.