3484 papers
Hep-Th, or high-energy theoretical physics, explores the fundamental building blocks of our universe and the forces that govern them. Researchers in this field use complex mathematics to understand everything from subatomic particles to the behavior of black holes, often pushing the boundaries of what we know about space and time.
At Gist.Science, we monitor the arXiv repository to ensure you stay ahead of the curve in this rapidly evolving discipline. For every new preprint uploaded to arXiv under this category, our team generates both accessible plain-language overviews and detailed technical summaries, making cutting-edge research understandable regardless of your background.
Below are the latest papers in high-energy theoretical physics, curated to help you navigate the most significant recent discoveries.
This paper demonstrates that simple feed-forward neural networks can accurately reconstruct diverse conformal field theory correlators using minimal data—specifically crossing symmetry, a single scaling dimension, and one anchor point—by leveraging the spectral bias of gradient-based training to favor the smooth functions characteristic of physical correlators.
This paper analyzes the stability of tachyon-free non-supersymmetric heterotic string flux compactifications on , revealing that while perturbative tachyons can be avoided or projected out depending on flux separation, the system remains universally unstable to non-perturbative brane nucleation that drives the fluxes toward a tachyonic regime.
This paper introduces new analytic methods to construct quantum lattice models that exhibit high-temperature entropic orders, including continuous symmetry breaking in 1+1 dimensions and various topological states in 2+1 dimensions, which evade standard no-go theorems by coupling systems to bosonic degrees of freedom that favor ordered states with higher entropy.
This paper provides holographic consistency checks for a conjectured supersymmetric gauge theory that flows from a conformal UV to gapped IR vacua by demonstrating an area law for Wilson loops, constructing domain walls governed by Yang-Mills-Chern-Simons theory that reproduce the correct anomaly inflow, and showing the instability of axionic strings which confirms the absence of a massless axion.
This paper utilizes exact diagonalization and matrix product state techniques on a fuzzy sphere to extract conformal data for the -dimensional Wilson-Fisher CFT, identifying 32 primary operators and verifying their scaling dimensions against conformal bootstrap predictions and large charge expansion results.
This paper employs topological field theories and generalized symmetries to demonstrate that ordinary superconductors are inherently bosonic systems with a topological BF description and a gravito-magnetic anomaly stemming from their fermionic origin, a feature that broadly characterizes gauged electronic matter and forbids trivial massive phases in various dimensions.
This paper investigates the BCS-BEC crossover in the large- attractive Fermi-Hubbard model on a one-dimensional lattice using mean-field theory with an imaginary chemical potential, revealing that the crossover is governed by three key parameters and identifying a specific thermal window at unitarity where the superconducting gap vanishes while the fermion number exhibits extrema, signaling a transition between particle-like and hole-like dominant regimes.
This paper presents a systematic Bayesian MCMC analysis of sixteen ghost-free, string-inspired inflation models using Planck 2018 and ACT data, demonstrating that while all models successfully reproduce the observed scalar spectral tilt, the preference for specific datasets is driven by the Hubble parametrization rather than the coupling function, with the parameter emerging as a stable fundamental constant.
This paper presents a systematic study of invariant operators in the three-Higgs-doublet model (3HDM) by computing the associated Hilbert series and constructing explicit expressions for flavor invariants up to cubic order in the couplings.
This paper presents the first fine-tuning study of small 7B-parameter reasoning models for Quantum Field Theory, utilizing a novel data generation pipeline to create synthetic and human-adapted training data, and evaluates the effectiveness of Reinforcement Learning and Supervised Fine-Tuning in enhancing domain-specific reasoning while analyzing the evolution of reasoning errors.