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 paper demonstrates that the DUNE-PRISM technique, which utilizes measurements at multiple off-axis angles to mitigate flux and cross-section systematic uncertainties, significantly restores the sensitivity of the DUNE experiment to new physics scenarios like non-unitarity and sterile neutrinos in the electron and muon sectors, while providing high-statistics flux data for future analyses.
The paper introduces SubTropica, a Mathematica package that utilizes tropical geometry to symbolically integrate multi-polylogarithmic integrals like Feynman diagrams, alongside its independent HyperIntica engine and an AI-driven online library for cataloging and retrieving physics results.
This paper addresses limitations in existing resonant anomaly detection methods by introducing new simulated signal benchmarks and a comprehensive "kitchen sink" observable set combining Energy Flow Polynomials and subjettiness variables, demonstrating that this approach offers superior sensitivity across diverse signal types while an attribute bagging variant significantly reduces training costs with comparable performance.
This paper introduces the first application of masked-token prediction, a technique adapted from Large Language Models, to anomaly detection at the Large Hadron Collider, demonstrating that a lightweight transformer trained solely on Standard Model background events can effectively identify rare new-physics signals through deviations in learned data structures.
This paper proposes a dynamical framework linking the dark matter-baryon coincidence to a GeV-scale asymmetric dark matter scenario, where a strongly supercooled dark confinement phase transition—potentially responsible for observed nano-Hz gravitational waves—naturally correlates the dark matter mass with the ultraviolet completion of the neutron portal operator.
This paper presents a unified relativistic perturbation framework to calculate frequency shifts in gravitational waves emitted by axion clouds around rotating black holes, accounting for self-interaction effects that populate multiple superradiant modes and revising the treatment of self-gravity to improve predictions for next-generation observations.
This paper investigates how quantum entanglement and Bell-inequality violations in top-antitop pair production at future lepton colliders serve as sensitive probes for distinguishing the Standard Model from new physics scenarios involving scalar mediators, bosons, and Kaluza-Klein gravitons.
This paper demonstrates that a nontrivial sound speed during inflation significantly alters the quantum-information diagnostics of cosmological perturbations by suppressing state purity, amplifying entropic and entanglement measures, and postponing the onset of classicality through modified decoherence dynamics.
This paper proposes a novel approach using quantum tomography to reconstruct the spin density matrix of diboson systems, enabling the simultaneous and superior constraint of both CP-even and CP-violating SMEFT operators by exploiting full Beyond-Standard-Model signatures, including pure quadratic new physics terms that are typically degenerate in traditional polarisation-blind observables.
This paper demonstrates that leveraging multidimensional correlations in baseline, recoil energy, and timing through both conventional likelihood analyses and machine learning techniques enables robust discrimination between sterile neutrino and non-standard interaction models in CENS experiments, even when total event rates are obscured by systematic uncertainties.