Lagrangian-based model of the process applied to the data
This paper derives a Lagrangian-based Breit-Wigner formula for the process and validates its parameters by fitting 2022 BESIII experimental data on production.
5243 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 derives a Lagrangian-based Breit-Wigner formula for the process and validates its parameters by fitting 2022 BESIII experimental data on production.
This paper proposes a novel method for detecting low-frequency gravitational waves using a three-satellite constellation equipped with atom interferometers to measure the gravito-magnetic component of the waves, thereby distinguishing the signal from asteroid-induced gravity gradient noise that mimics the gravito-electric signal.
This paper demonstrates that extending the standard cosmological model to include running spectral index, extra relativistic species, and neutrino masses relaxes constraints on the primordial power spectrum, thereby reviving previously excluded inflationary models, while forecasting that future CMB experiments will significantly tighten measurements of spectral parameters and probe smaller scales than currently possible.
This paper analyzes the process using Higgs pseudo-observables to demonstrate that deviations from the Standard Model due to heavy new physics are encoded in on-shell and couplings, which can be fully characterized by measuring the process at different FCC-ee energies to resolve sign ambiguities left by decay constraints.
This paper investigates how enhanced primordial density fluctuations facilitate the early formation of stars and black holes at high redshifts, leading to a significantly increased abundance and density of dark matter subhalos and stellar clusters in the Milky Way that vary sensitively with the specific shape of the power spectrum enhancement.
This paper presents an AI-assisted framework for local infrared subtraction that combines EFT matching with neural network or analytic constructions to enable efficient, slicing-parameter-free higher-order QCD calculations, successfully reconstructing NLO results for multi-jet production and demonstrating feasibility on modest hardware.
This paper utilizes unique scaling function properties and an improved chiral order parameter to directly estimate the chiral phase transition temperature and universal critical parameters from (2+1)-flavor QCD simulations, while quantitatively analyzing finite-volume effects and deviations from universal scaling as a function of the light-to-strange quark mass ratio.
This paper utilizes the Standard Model Effective Field Theory (SMEFT) to demonstrate how current and future electric dipole moment measurements, particularly of the proton, provide highly sensitive and complementary constraints on CP-violating top-quark interactions by accounting for complex multi-loop effects and renormalization-group evolution.
This paper demonstrates that the breakdown of fixed-order perturbation theory in high-energy LHC processes, caused by large logarithms of , is resolved by resumming these logarithms, thereby providing accurate predictions for +dijet production that match recent ATLAS measurements.
This paper introduces two hybrid techniques, particularly recommending the "Latent Categories" approach, that significantly reduce the computational cost of neural simulation-based inference while maintaining robustness and reliability guarantees for applications ranging from offline analyses to future trigger-level scenarios.