Hep-Ph explores the fundamental forces that govern how particles interact and behave at the smallest scales imaginable. This field bridges the gap between theoretical predictions and experimental reality, helping scientists understand the building blocks of our universe without getting lost in complex mathematics. Whether investigating the Higgs boson or searching for new physics beyond current models, these studies push the boundaries of human knowledge about matter and energy.

At Gist.Science, we process every new preprint in this category as soon as it appears on arXiv. We strip away the dense jargon to offer both accessible plain-language explanations and detailed technical summaries, ensuring that groundbreaking research is understandable to everyone from students to seasoned experts. Below are the latest papers in this dynamic field, ready for you to explore with clarity and depth.

⚛️ high-energy experiments

Can Elastic Neutrino Scattering Account for the LZ230616 Event?

This paper investigates whether the isolated nuclear-recoil event reported by the LUX-ZEPLIN (LZ) collaboration could originate from elastic neutrino-nucleus scattering, concluding that kinematic constraints, the recoil spectrum, and existing limits rule out this explanation across all considered Standard Model and beyond scenarios, including exotic neutrino fluxes from dark matter and primordial black holes.

Ayan Chattaraj, Anirban Majumdar, Dimitrios K. Papoulias, Rahul Srivastava2026-09-10
⚛️ high-energy theory

Multiparticle scalar dark matter with ZN\mathbb{Z}_N symmetry

This paper systematically investigates a ZN\mathbb{Z}_N symmetric model featuring two scalar dark matter components that undergo co-annihilation, analyzing their viability as WIMP or pFIMP candidates within the Z3\mathbb{Z}_3 framework while confronting the allowed parameter space against current direct and indirect detection constraints.

Subhaditya Bhattacharya, Lipika Kolay, Dipankar Pradhan2026-09-09
⚛️ general relativity

SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation

SageNet is a deep learning framework that utilizes Long Short-Term Memory networks to emulate stiff-amplified inflationary gravitational wave spectra with high accuracy and a 10410^4-fold speedup over traditional numerical solvers, thereby enabling efficient Bayesian inference for extended cosmological models.

Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro2026-09-09