5447 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 employs the Laplace transform method to derive compact analytical expressions for both integrated and transverse momentum dependent gluon densities in a proton at very low , demonstrating that these simplified formulas accurately capture the essential features of more complex calculations while closely matching results from other analytical and numerical approaches.
This paper investigates how non-minimal kinetic mixing between the Higgs field and the scalaron in Higgs- inflation generates distinct primordial perturbation regimes, producing localized features in the curvature power spectrum for moderate couplings or residual isocurvature modes for weak couplings, with significant implications for CMB observations.
Motivated by recent BESIII data, this study investigates the decay by incorporating contributions from the dynamically generated and other resonances, successfully reproducing existing mass distributions and predicting a distinct cusp structure around 1.43 GeV that could confirm the existence of this low-lying excited baryon.
This paper employs a coupled-channel chiral unitary approach to analyze the decay, demonstrating that dynamically generated and resonances explain recent experimental discrepancies and highlighting this channel as a crucial tool for understanding low-energy baryon spectroscopy and isospin dynamics.
This paper investigates the kinematic dependencies and polarization effects on the differential cross-section and double spin asymmetry of prompt photon production via bremsstrahlung in proton-proton collisions at NICA energies of GeV, noting that while this process constitutes a small fraction (0.03%) of the total prompt photon yield, it exhibits significant sensitivity to proton polarization at high transverse momenta.
This paper presents a non-perturbative renormalization program within the mass gap approach to QCD that dynamically generates massive gluons, analyzes their full propagator to resolve canonical gauge inconsistencies, and demonstrates how their off-shell nature ensures confinement by preventing them from appearing in the physical spectrum.
This paper proposes a Bayesian model selection framework utilizing genetic algorithms to navigate the SMEFT landscape as a space of competing operator hypotheses, demonstrating that this approach yields more robust characterizations of Wilson coefficients and clearer identification of model correlations than traditional global fits when applied to LEP and LHC data.
This paper demonstrates that coupling Ostrogradsky ghosts to dissipative baths within a non-equilibrium Keldysh-Lindblad framework can stabilize these unstable modes through a dissipative phase transition, effectively suppressing ghost excitations via either dynamically generated effective masses or strong overdamping.
This paper re-evaluates the proposed Higgs-top-Z mass coincidence relation using 2025 PDG data and NNLO matching, finding that while the pole-level geometric relation remains a viable test, the corresponding exact running-coupling boundary condition is incompatible with measurements, thereby necessitating a finite matching factor or new symmetry to explain the observed mass spectrum.
This paper demonstrates that deep neural networks significantly enhance the sensitivity of LHC searches for heavy lepton-flavor-violating Higgs decays () by improving signal-background discrimination through kinematic classification and correcting systematic mass prediction biases inherent in standard collinear approximations.