Neutron Stars in Palatini R+αR2R+\alpha R^2 and R+αR2+βQR+\alpha R^2+\beta Q Theories

We study solutions of the stellar structure equations for spherically symmetric objects in Palatini $f(R)=R+\alpha R^2$ and $f(R,Q)=R+\alpha R^2+\beta Q$ in the mass-radius region associated to neutron stars. We illustrate the potential impact of the $R^2$ and $Q$ terms by studying a range of viable values of $\alpha$ and $\beta$. Similarly, we use different equations of state (SLy, FPS, HS(DD2) and HS(TMA)) as a simple way to account for the equation of state uncertainty. Our results show that for certain combinations of the $\alpha$ and $\beta$ parameters and equation of state, the effect of modifications of general relativity on the properties of stars is sizeable. Therefore, with increasing accuracy in the determination of the equation of state for neutron stars, astrophysical observations may serve as discriminators of modifications of General Relativity.Comment: 13 pages, 18 figures. References added. Discussion around Fig. 18 extended. Agrees with published versio

Beyond Rainbow-Ladder in a covariant three-body Bethe-Salpeter approach: Baryons

We report on recent results of a calculation of the nucleon and delta masses in a covariant bound-state approach, where to the simple rainbow-ladder gluon-exchange interaction kernel we add a pion-exchange contribution to account for pion cloud effects. We observe good agreement with lattice data at large pion masses. At the physical point our masses are too large by about five percent, signaling the need for more structure in the gluon part of the interaction.Comment: 4 pages, 3 figures, Proceedings of The 13th International Conference on Meson-Nucleon Physics and the Structure of the Nucleon (MENU 2013), Rom

Spectrum of scalar and pseudoscalar glueballs from functional methods

We provide results for the spectrum of scalar and pseudoscalar glueballs in pure Yang-Mills theory using a parameter-free fully self-contained truncation of Dyson-Schwinger and Bethe-Salpeter equations. The only input, the scale, is fixed by comparison with lattice calculations. We obtain ground state masses of $1.9\,\text{GeV}$ and $2.6\,\text{GeV}$ for the scalar and pseudoscalar glueballs, respectively, and $2.6\,\text{GeV}$ and $3.9\,\text{GeV}$ for the corresponding first excited states. This is in very good quantitative agreement with available lattice results. Furthermore, we predict masses for the second excited states at $3.7\,\text{GeV}$ and $4.3\,\text{GeV}$. The quality of the results hinges crucially on the self-consistency of the employed input. The masses are independent of a specific choice for the infrared behavior of the ghost propagator providing further evidence that this only reflects a nonperturbative gauge completion.Comment: 13 pages, 10 figs.; v2: extended version with a meson calculation to illustrate the extrapolation, unified scale setting in comparison, agrees with published versio

Dynamical Aspects of Generalized Palatini Theories of Gravity

We study the field equations of modified theories of gravity in which the lagrangian is a general function of the Ricci scalar and Ricci-squared terms in Palatini formalism. We show that the independent connection can be expressed as the Levi-Civita connection of an auxiliary metric which, in particular cases of interest, is related with the physical metric by means of a disformal transformation. This relation between physical and auxiliary metric boils down to a conformal transformation in the case of f(R) theories. We also show with explicit models that the inclusion of Ricci squared terms in the action can impose upper bounds on the accessible values of pressure and density, which might have important consequences for the early time cosmology and black hole formation scenarios. Our results indicate that the phenomenology of f(R_{ab}R^{ab}) theories is much richer than that of f(R) and f(R_{ab}R^{ab}) theories and that they also share some similarities with Bekenstein's relativistic theory of MOND.Comment: 8 pages, no figure

Glueballs from bound state equations

Glueballs are bound states in the spectrum of quantum chromodynamics which consist only of gluons. They belong to the group of exotic hadrons which are widely studied experimentally and theoretically. We summarize how to calculate glueballs in a functional framework and discuss results for pure Yang-Mills theory. Our setup is totally self-contained with the scale being the only external input. We enumerate a range of tests that provide evidence of the stability of the results. This illustrates the potential of functional equations as a continuum first-principles method complementary to lattice calculations

Bouncing Cosmologies in Palatini f(R)f(R) Gravity

We consider the early time cosmology of f(R) theories in Palatini formalism and study the conditions that guarantee the existence of homogeneous and isotropic models that avoid the Big Bang singularity. We show that for such models the Big Bang singularity can be replaced by a cosmic bounce without violating any energy condition. In fact, the bounce is possible even for pressureless dust. We give a characterization of such models and discuss their dynamics in the region near the bounce. We also find that power-law lagrangians with a finite number of terms may lead to non-singular universes, which contrasts with the infinite-series Palatini f(R) lagrangian that one needs to fully capture the effective dynamics of Loop Quantum Cosmology. We argue that these models could also avoid the formation of singularities during stellar gravitational collapse.Comment: 8 pages, 4 figures; added references and a short comment in sec.I

Approximating the Steady-State Temperature of 3D Electronic Systems with Convolutional Neural Networks

Thermal simulations are an important part of the design process in many engineering disciplines. In simulation-based design approaches, a considerable amount of time is spent by repeated simulations. An alternative, fast simulation tool would be a welcome addition to any automatized and simulation-based optimisation workflow. In this work, we present a proof-of-concept study of the application of convolutional neural networks to accelerate thermal simulations. We focus on the thermal aspect of electronic systems. The goal of such a tool is to provide accurate approximations of a full solution, in order to quickly select promising designs for more detailed investigations. Based on a training set of randomly generated circuits with corresponding finite element solutions, the full 3D steady-state temperature field is estimated using a fully convolutional neural network. A custom network architecture is proposed which captures the long-range correlations present in heat conduction problems. We test the network on a separate dataset and find that the mean relative error is around 2% and the typical evaluation time is 35 ms per sample (2 ms for evaluation, 33 ms for data transfer). The benefit of this neural-network-based approach is that, once training is completed, the network can be applied to any system within the design space spanned by the randomized training dataset (which includes different components, material properties, different positioning of components on a PCB, etc.)

Approximating the Steady-State Temperature of 3D Electronic Systems with Convolutional Neural Networks

Thermal simulations are an important part of the design process in many engineering disciplines. In simulation-based design approaches, a considerable amount of time is spent by repeated simulations. An alternative, fast simulation tool would be a welcome addition to any automatized and simulation-based optimisation workflow. In this work, we present a proof-of-concept study of the application of convolutional neural networks to accelerate thermal simulations. We focus on the thermal aspect of electronic systems. The goal of such a tool is to provide accurate approximations of a full solution, in order to quickly select promising designs for more detailed investigations. Based on a training set of randomly generated circuits with corresponding finite element solutions, the full 3D steady-state temperature field is estimated using a fully convolutional neural network. A custom network architecture is proposed which captures the long-range correlations present in heat conduction problems. We test the network on a separate dataset and find that the mean relative error is around 2% and the typical evaluation time is 35 ms per sample (2 ms for evaluation, 33 ms for data transfer). The benefit of this neural-network-based approach is that, once training is completed, the network can be applied to any system within the design space spanned by the randomized training dataset (which includes different components, material properties, different positioning of components on a PCB, etc.)