3153 papers
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.
This paper proposes that spacetime discreteness emerges naturally from the consistency of microscopic measurements, where treating infinitesimal intervals as scale-dependent outcomes leads to discrete, equidistant lengths and a geometric renormalization-group flow that preserves Lorentz invariance and general covariance without ad hoc cutoffs.
This paper investigates the form-factor dependence of Reggeized spin-2 Pomeron and spin-3 Odderon exchanges in high-energy elastic $pp$ and scattering, demonstrating that an exponential Odderon-proton form factor provides a superior fit to global data compared to other parametrizations and reveals a peripheral soft Odderon interaction with hadronic-scale transverse structure.
This paper investigates the intermittency and fractal behavior of charged particles in Pb-Pb collisions at 5.02 TeV using EPOS4 and PYTHIA8 simulations to analyze large density fluctuations as potential signatures of the QCD phase transition and critical point.
The paper introduces Linac, a high-performance, open-source, parallel implementation of Gaussian elimination over finite fields and floating-point arithmetic using CUDA, designed to accelerate the solution of linear systems for applications such as the analytic reconstruction of scattering amplitudes in quantum field theory.
This paper rigorously analyzes the spin-dependent interactions and fine structure of negative-parity singly heavy baryons using a newly proposed two-step Gaussian expansion method within the relativized quark model, successfully reproducing experimental data and providing a robust framework for high-precision calculations in few-body quantum systems.
This paper argues that CP is conserved in strong interactions because topological quantization arises from taking the infinite spacetime volume limit before summing over topological sectors, a reasoning the authors demonstrate is consistent with steepest-descent path integral constructions and robust against various objections regarding theta-parameters and instanton approximations.
This paper employs high-precision thermodynamic analysis to establish a universal lower bound on the transition timescale for strongly supercooled phase transitions, revealing that the resulting primordial black hole abundance is severely constrained and unlikely to constitute viable dark matter within classically conformal gauge-Higgs theories.
This paper utilizes the String Melting mode of the AMPT model to investigate the scaling behavior and intermittency of charged particle multiplicity fluctuations in Xe–Xe collisions at = 5.44 TeV, determining key parameters such as anomalous fractal dimensions and scaling exponents to characterize the system's self-similar dynamics and provide baseline predictions.
Using a two-flavor non-extensive NJL model with Tsallis statistics, this study demonstrates that non-equilibrium effects and chiral imbalance significantly modify the QCD phase diagram under strong magnetic fields by lowering the critical temperature for chiral symmetry restoration, inducing inverse magnetic catalysis, and altering thermodynamic observables like pressure and the speed of sound.
This paper proposes a machine learning strategy utilizing graph neural networks to detect Higgs-induced semi-visible jets from a confining dark sector at the Future Circular Collider, demonstrating the ability to constrain exotic Higgs branching ratios to the permille level across a wide parameter space.