5257 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 presents a detailed analysis of the fermion mass spectrum in the Minimal Supersymmetric model under both -parity conserving and violating scenarios, demonstrating that numerical predictions for all masses and mixing angles align with current experimental data.
This paper presents a comprehensive reanalysis of DESI DR1 data combined with other cosmological probes, achieving percent-level constraints on key parameters and providing robust evidence favoring the normal neutrino mass hierarchy while setting the tightest bounds to date on the sum of neutrino masses and dynamical dark energy.
Using QCD sum rules, this paper investigates the masses and decay properties of the tensor hadronic molecules () and (), determining their instability against dissociation and calculating their full widths through dominant and subleading decay channels.
This paper demonstrates that post-inflationary axion theories with a domain wall number greater than one can be phenomenologically viable if additional PQ-violating operators destroy axion strings before the QCD potential becomes relevant, thereby resolving the domain wall problem while predicting a QCD axion mass in the – eV range and a neutron electric dipole moment within reach of upcoming experiments.
This paper proposes using time-domain astrometric weak lensing of multiply imaged quasars to detect substellar dark matter halos by measuring their induced angular acceleration, a method capable of probing dark matter structures 12 to 14 orders of magnitude smaller than previously detected and significantly constraining dark matter particle properties.
This paper computes high-frequency gravitational wave radiation from collisions between light and heavy compact objects using shockwave propagators, revealing a perturbative expansion of tidal response functions that connect Lipatov vertex amplitudes to Weinberg's soft radiation limits while highlighting double-copy structures with Yang-Mills gluon radiation.
This white paper summarizes scientific challenges and AI/ML opportunities identified through the FAIRS Japan 2024 unconference, highlighting common technical themes such as high-dimensional reconstruction, fast simulation, and anomaly detection across accelerator physics, cosmology, and neutrino physics.
This paper demonstrates that incorporating precision timing information from a future HL-LHC detector layer into vector-boson-fusion Higgs decay searches significantly enhances sensitivity to long-lived particles by converting displaced vertices into four-dimensional objects, thereby suppressing prompt heavy-flavor backgrounds and extending reach into regimes where traditional spatial-only searches lose acceptance.
This paper presents a general quantum circuit framework that utilizes specialized vertex and propagator gates to efficiently compute partonic Drell-Yan scattering cross sections and isolate interference effects through basis rotation, marking a significant step toward simulating arbitrary scattering processes on quantum devices.
This study systematically compares classical and quantum machine learning architectures for regression on simulated high-energy physics collision data, finding that while classical models like CNNs and LSTMs currently offer marginally better performance, quantum counterparts achieve competitive accuracy with significantly fewer trainable parameters, highlighting a distinct parameter-efficiency advantage for near-term quantum devices.