The interplay of black hole and cosmological horizons introduces distinctive thermodynamic behavior for deSitter black holes, including well-known upper bounds for the mass and entropy. We point to a new such feature, a Schottky peak in the heat capacity of Schwarzschild-deSitter (SdS) black holes. With this behavior in mind, we explore statistical models for the underlying quantum degrees of freedom of SdS holes. While a simple two-state spin model gives Schottky behavior, in order to capture the non-equilibrium nature of the SdS system we consider a system with a large number of non-interacting spins.We examine to what extent constrained states of this system reproduce the thermodynamic properties of the black hole. We also review results of a recent study of particle production in SdS spacetimes in light of the Schottky anomaly and our spin models. arXiv:1907.00248v2 [hep-th]

In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running.

A new population of millisecond pulsars is a long-standing proposed explanation for the excess of GeV-scale gamma rays emanating from the region surrounding the center of the Milky Way (the “Galactic Center excess”). We examine several simple parameterizations of possible luminosity functions for this population, as well as several benchmark luminosity functions proposed in the literature, and compare the predicted populations of resolved point sources to the Fermi 4FGL-DR2 point source catalog and a sub-population recently identified using wavelet-based methods. We provide general results that can be used to translate upper limits on the number of resolved point sources associated with the excess, and the fraction of the flux in the excess that can be attributed to resolved sources, into limits on the luminosity function parameter space. We discuss a number of important systematic uncertainties, including in the detection threshold model and the total flux attributed to the excess. We delineate regions of parameter space (containing existing benchmark models) where there is no apparent tension with current data, and the number of total pulsars needed to explain the excess is in the range of 𝒪(104-5). In the future, lowered point source detection thresholds could be achieved either by new analysis methods or new data. An order-of-magnitude reduction in the sensitivity threshold (which may already be achieved by novel analyses probing sub-threshold source populations) could hope to resolve more than 30% of the flux of the excess even in pessimistic scenarios.

Knowledge of the interior density distribution of an asteroid can reveal its composition and constrain its evolutionary history. However, most asteroid observational techniques are not sensitive to interior properties. We investigate the interior constraints accessible through monitoring variations in angular velocity during a close encounter. We derive the equations of motion for a rigid asteroid’s orientation and angular velocity to arbitrary order and use them to generate synthetic angular velocity data for a representative asteroid on a close Earth encounter. We develop a toolkit AIME (Asteroid Interior Mapping from Encounters) which reconstructs asteroid density distribution from these data, and we perform injection-retrieval tests on these synthetic data to assess AIME’s accuracy and precision. We also perform a sensitivity analysis to asteroid parameters (e.g. asteroid shape and orbital elements), observational set-up (e.g. measurement precision and cadence), and the mapping models used. We find that high precision in rotational period estimates (≲ 0.27 seconds) are necessary for each cadence, and that low perigees (≲ 18 Earth radii) are necessary to resolve large-scale density non-uniformities with uncertainties of $\sim 0.1{{\%}}$ of the local density under some models.