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.

⚛️ high-energy theory

Deformed BTZ Radiance and Single Trace TTˉT\bar{T} Holography

This paper generalizes the "holar wind" mechanism to rotating λ\lambda-deformed BTZ black holes, demonstrating that their long string emission probabilities are universally governed by the change in Bekenstein-Hawking entropy and that the thermodynamically required background B-field precisely matches the spectrum of single-trace TTˉT\bar{T} deformed symmetric product CFTs.

Daniel Vainshtein2026-06-26
⚛️ phenomenology

Unitarity Cuts, t-channel Divergences and the KLN Theorem for Unstable Particles

This paper formulates practical prescriptions for handling t-channel divergences in phenomenological calculations involving massless or unstable particles by demonstrating intricate KLN theorem cancellations across regularization schemes and connecting these results to the complex-analytic structure of amplitudes to advance the construction of finite, fixed-order inclusive collider observables.

Marko Beocanin, Michael A. Schmidt2026-06-26
⚛️ high-energy theory

The Sharp Edges of Calabi-Yau Manifolds: Designing Symmetric Models for Ricci-flat Metrics

This paper provides a comprehensive overview of Calabi-Yau manifolds for machine learning researchers, characterizes the role of symmetries in approximating Ricci-flat metrics, and introduces a novel symmetry-aware graph neural network model alongside new formulas for volume ratios to address challenges in heterotic string theory predictions.

Viktor Mirjanić, Challenger Mishra2026-06-26