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1. Rapid-turn inflation in supergravity is rare and tachyonic

   https://doi.org/10.1088/1475-7516/2022/03/002  

   Aragam, Vikas ; Chiovoloni, Roberta ; Paban, Sonia ; Rosati, Robert ; Zavala, Ivonne ( March 2022 , Journal of Cosmology and Astroparticle Physics)

   Abstract Strongly non-geodesic, or rapidly turning trajectories in multifield inflation have attracted much interest recently from both theoretical and phenomenological perspectives. Most models with large turning rates in the literature are formulated as effective field theories. In this paper we investigate rapid-turn inflation in supergravity as a first step towards understanding them in string theory. We find that large turning rates can be generated in a wide class of models, at the cost of high field space curvature. In these models, while the inflationary trajectories are stable, one Hessian eigenvalue is always tachyonic and large, in Hubble units. Thus, these models satisfy the de Sitter swampland conjecture along the inflationary trajectory. However, the high curvatures underscore the difficulty of obtaining rapid-turn inflation in realistic string-theoretical models.In passing, we revisit the η -problem in multifield slow-roll inflation and show that it does not arise, inasmuch as the inflatons, ϕ i , can all be heavier (in absolute value) that the Hubble scale: | m i | /H >1, ∀ i .
2. Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

   https://doi.org/10.3389/fphys.2021.667828  

   Toledo-Marín, J. Quetzalcóatl ; Fox, Geoffrey ; Sluka, James P. ; Glazier, James A. ( June 2021 , Frontiers in Physiology)

   In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019 ; Lee et al., 2020 ). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs couldmore »enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.« less
3. The scalar chemical potential in cosmological collider physics

   https://doi.org/10.1007/jhep02(2021)079  

   Bodas, Arushi ; Kumar, Soubhik ; Sundrum, Raman ( February 2021 , Journal of High Energy Physics)

   A bstract Non-analyticity in co-moving momenta within the non-Gaussian bispectrum is a distinctive sign of on-shell particle production during inflation, presenting a unique opportunity for the “direct detection” of particles with masses as large as the inflationary Hubble scale ( H ). However, the strength of such non-analyticity ordinarily drops exponentially by a Boltzmann-like factor as masses exceed H . In this paper, we study an exception provided by a dimension-5 derivative coupling of the inflaton to heavy-particle currents, applying it specifically to the case of two real scalars. The operator has a “chemical potential” form, which harnesses the large kinetic energy scale of the inflaton, $$ {\overset{\cdot }{\phi}}_0^{1/2}\approx 60H $$ ϕ ⋅ 0 1 / 2 ≈ 60 H , to act as an efficient source of scalar particle production. Derivative couplings of inflaton ensure radiative stability of the slow-roll potential, which in turn maintains (approximate) scale-invariance of the inflationary correlations. We show that a signal not suffering Boltzmann suppression can be obtained in the bispectrum with strength f NL ∼ $$ \mathcal{O} $$ O (0 . 01–10) for an extended range of scalar masses $$ \lesssim {\overset{\cdot }{\phi}}_0^{1/2} $$ ≲ ϕ ⋅ 0 1 / 2 , potentiallymore »as high as 10 15 GeV, within the sensitivity of upcoming LSS and more futuristic 21-cm experiments. The mechanism does not invoke any particular fine-tuning of parameters or breakdown of perturbation-theoretic control. The leading contribution appears at tree-level , which makes the calculation analytically tractable and removes the loop-suppression as compared to earlier chemical potential studies of non-zero spins. The steady particle production allows us to infer the effective mass of the heavy particles and the chemical potential from the variation in bispectrum oscillations as a function of co-moving momenta. Our analysis sets the stage for generalization to heavy bosons with non-zero spin.« less
4. Affine body dynamics: fast, stable and intersection-free simulation of stiff materials

   https://doi.org/10.1145/3528223.3530064  

   Lan, Lei ; Kaufman, Danny M. ; Li, Minchen ; Jiang, Chenfanfu ; Yang, Yin ( July 2022 , ACM Transactions on Graphics)

   Simulating stiff materials in applications where deformations are either not significant or else can safely be ignored is a fundamental task across fields. Rigid body modeling has thus long remained a critical tool and is, by far, the most popular simulation strategy currently employed for modeling stiff solids. At the same time, rigid body methods continue to pose a number of well known challenges and trade-offs including intersections, instabilities, inaccuracies, and/or slow performances that grow with contact-problem complexity. In this paper we revisit the stiff body problem and present ABD, a simple and highly effective affine body dynamics framework, which significantly improves state-of-the-art for simulating stiff-body dynamics. We trace the challenges in rigid-body methods to the necessity of linearizing piecewise-rigid trajectories and subsequent constraints. ABD instead relaxes the unnecessary (and unrealistic) constraint that each body's motion be exactly rigid with a stiff orthogonality potential, while preserving the rigid body model's key feature of a small coordinate representation. In doing so ABD replaces piecewise linearization with piecewise linear trajectories. This, in turn, combines the best of both worlds: compact coordinates ensure small, sparse system solves, while piecewise-linear trajectories enable efficient and accurate constraint (contact and joint) evaluations. Beginning with this simplemore »foundation, ABD preserves all guarantees of the underlying IPC model we build it upon, e.g., solution convergence, guaranteed non-intersection, and accurate frictional contact. Over a wide range and scale of simulation problems we demonstrate that ABD brings orders of magnitude performance gains (two- to three-orders on the CPU and an order more when utilizing the GPU, obtaining 10, 000× speedups) over prior IPC-based methods, while maintaining simulation quality and nonintersection of trajectories. At the same time ABD has comparable or faster timings when compared to state-of-the-art rigid body libraries optimized for performance without guarantees, and successfully and efficiently solves challenging simulation problems where both classes of prior rigid body simulation methods fail altogether.« less
5. Revealing the Field Sub-subgiant Population Using a Catalog of Active Giant Stars and Gaia EDR3

   https://doi.org/10.3847/1538-4357/ac53b1  

   Leiner, Emily M. ; Geller, Aaron M. ; Gully-Santiago, Michael A. ; Gosnell, Natalie M. ; Tofflemire, Benjamin M. ( March 2022 , The Astrophysical Journal)

   Sub-subgiant stars (SSGs) fall below the subgiant branch and/or red of the giant branch in open and globular clusters, an area of the color–magnitude diagram (CMD) not populated by standard stellar evolution tracks. One hypothesis is that SSGs result from rapid rotation in subgiants or giants due to tidal synchronization in a close binary. The strong magnetic fields generated inhibit convection, which in turn produces large starspots, radius inflation, and lower-than-expected average surface temperatures and luminosities. Here we cross-reference a catalog of active giant binaries (RS CVns) in the field with Gaia EDR3. Using the Gaia photometry and parallaxes, we precisely position the RS CVns in a CMD. We identify stars that fall below a 14 Gyr, metal-rich isochrone as candidate field SSGs. Out of a sample of 1723 RS CVn, we find 448 SSG candidates, a dramatic expansion from the 65 SSGs previously known. Most SSGs have rotation periods of 2–20 days, with the highest SSG fraction found among RS CVn with the shortest periods. The ubiquity of SSGs among this population indicates that SSGs are a normal phase in evolution for RS CVn-type systems, not rare by-products of dynamical encounters found only in dense star clusters asmore »some have suggested. We present our catalog of 1723 active giants, including Gaia photometry and astrometry, and rotation periods from the Transiting Exoplanet Survey Satellite and International Variable Star Index (VSX). This catalog can serve as an important sample to study the impacts of magnetic fields in evolved stars.

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