5500 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 study utilizes the AMPT model to analyze meson production in Au+Au collisions at RHIC BES energies, revealing that while the model reproduces experimental ratios even without hadronic rescattering, the meson's directed flow and average transverse momentum serve as sensitive probes of the late-stage hadronic medium's lifetime and interactions.
This paper reviews the motivation and early experimental results from the July 2025 LHC light-ion run (pO, OO, and NeNe collisions), which provide strong evidence for quark-gluon plasma formation in small systems and bridge the gap between perturbative QCD, hot QCD, and low-energy nuclear structure physics.
This paper utilizes Fermi-LAT observations of dark matter mini-spikes around stellar-mass black holes to place stringent constraints on the high-mass parameter space of the Inert Doublet Model, demonstrating the enhanced sensitivity of indirect detection methods for probing dark matter beyond the reach of current collider and direct detection experiments.
This paper proposes a framework using smeared boundary conformal field theories to characterize gapped phases and massive renormalization group flows dual to massless ones, revealing that such phases often involve unphysical smeared Ishibashi states and spontaneously break non-invertible symmetries, thereby providing a quantum field-theoretic description of unusual order-disorder coexistence.
This paper utilizes the Bethe ansatz to determine the zero-temperature equation of state for the massive Thirring/sine-Gordon model, thereby validating recently derived model-independent bounds on systems with non-zero current density and demonstrating that these bounds constrain the energy density within a factor of two at high densities.
This paper argues that while flavor remains a conceptually incomplete aspect of the Standard Model, the DUNE experiment offers a powerful, systematic framework to test the limits of the current three-flavor description and search for small deviations through its unique combination of precision oscillation measurements, near-detector capabilities, and the DUNE-PRISM strategy.
This paper presents a comprehensive phenomenological analysis of new high-precision COHERENT germanium CENS data to extract updated Standard Model and nuclear physics parameters, including the weak mixing angle and germanium neutron radius, while simultaneously constraining neutrino non-standard interactions through a global combined analysis with other experimental datasets.
This paper computes perturbative QCD corrections to the form factors of the transition using symmetry-breaking relations and light-cone distribution amplitudes, finding that these effects induce only modest shifts in the branching ratio and lepton polarization asymmetries, thereby establishing a precise Standard Model baseline where significant experimental deviations would signal New Physics.
This paper proposes a novel mechanism where cosmic-ray protons scattering off dark matter in the Milky Way halo generate the observed GeV Galactic Center gamma-ray excess, offering a viable alternative to conventional dark matter annihilation or pulsar models.
This paper introduces a Graph Neural Network-based foundation model pretrained on 120 million simulated high-energy physics events that, when fine-tuned, significantly improves event classification accuracy and efficiency across diverse tasks and simulation frameworks while revealing that performance gains stem from developing new message-passing pathways while preserving general-purpose encoders.