3255 papers
Quantum gravity represents the frontier where the very large meets the very small, attempting to unify Einstein's theory of gravity with the strange rules of quantum mechanics. This field explores the fundamental fabric of spacetime, seeking to understand how the universe behaves at its most extreme scales, from the heart of black holes to the moment of the Big Bang. Because these concepts often involve complex mathematics, they can feel distant to non-specialists, yet they hold the key to a complete picture of physical reality.
At Gist.Science, we bridge this gap by processing every new preprint in this category directly from arXiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, ensuring that groundbreaking research is accessible to everyone, from curious students to seasoned researchers. Below are the latest papers in quantum gravity, offering fresh insights into the nature of our cosmos.
This paper constructs four-dimensional de Sitter space as an excited Glauber-Sudarshan state within M-theory and its dual heterotic string theories to evade vacuum-based no-go theorems, deriving the necessary effective field theory conditions equivalent to the null energy condition and analyzing constraints from axionic cosmology.
This paper proposes that in curved spacetime, probability acquires a fundamentally quasilocal character where gravitational boundaries and horizons convert global conservation into a flux balance law, inducing effective non-Hermiticity for restricted observers while preserving global unitarity and yielding observable imprints in black hole ringdowns.
This paper investigates the thermodynamics of Schwarzschild-AdS black holes in -deformed non-commutative geometry, demonstrating that the deformation induces Van der Waals-like phase transitions with a universal critical ratio and a unique double-loop structure in the Gibbs free energy-temperature curve.
This paper proposes a new mechanism to solve the cosmological constant problem by utilizing a global constraint derived from a lapse function in a 5-dimensional anisotropic gravitational theory, which successfully cancels radiative contributions to vacuum energy at all orders while preserving 4D Lorentz invariance.
This paper investigates the loss of hyperbolicity in scalar Gauss-Bonnet gravity, demonstrating that while black hole solutions can exist with arbitrarily small masses by tuning coupling functions, their physical validity is constrained by a minimum mass threshold and their observable scalar charges remain bounded, limiting deviations from general relativity.
This paper proposes a fluid-based analogue gravity setup using surface-gravity waves to experimentally emulate the instabilities of regular black holes and horizonless mimickers, concluding that while theoretically feasible, alternative media like Bose-Einstein condensates may be more practical for capturing these specific physical features.
This paper provides a systematic theoretical treatment of the prompt response component in gravitational waves from sources plunging into a Schwarzschild black hole, demonstrating that its inclusion alongside quasinormal modes and tail contributions enables a highly accurate (99%) reconstruction of the full inspiral-merger-ringdown waveform.
This paper presents a comprehensive benchmark of classical and deep learning models for multiclass classification of LIGO gravitational-wave glitches using tabular metadata, revealing that while tree-based methods remain strong baselines, certain deep learning architectures offer competitive performance with greater parameter efficiency and distinct interpretability characteristics.
This paper demonstrates that while unrestricted generalized Vaidya exteriors in metric gravity leave the Oppenheimer-Snyder collapse problem formally open, imposing physically realistic matter constraints forces the exterior solution into highly restricted branches that either lack global asymptotic validity or reduce the interior to a constant-curvature sector, thereby failing to resolve the collapse of dust.
The paper introduces **labrador**, a domain-optimized neural inference tool that leverages physical insights like heterodyning, tailored coordinates, and parameter folding to achieve fast, efficient, and interpretable gravitational-wave parameter estimation for long-duration signals across a broad mass range.