1387 papers
Hep-Ex explores the fascinating intersection where particle physics meets experimental reality. This field investigates how scientists build massive detectors and accelerate particles to test the fundamental laws of nature, turning abstract theories into measurable data. It is the rigorous process of searching for new particles or forces that could reshape our understanding of the universe, often requiring years of collaboration and engineering.
At Gist.Science, we ensure these discoveries become accessible to everyone. We process every new preprint in this category directly from arXiv, generating both plain-language explanations for curious readers and detailed technical summaries for specialists. Our goal is to bridge the gap between complex experimental results and public understanding without losing scientific nuance.
Below are the latest papers in Hep-Ex, freshly summarized and ready for you to explore.
This paper introduces a novel neural posterior estimation method for IceCube neutrino direction reconstruction that combines a transformer encoder with a spherical normalizing flow, achieving state-of-the-art angular resolution and significantly faster, constant-time all-sky scans for both track and shower events across a wide energy range.
This paper proposes a novel LHC search for GeV-scale particles coupling to light quarks by identifying low-multiplicity jets produced in association with a hard photon, a signature that distinguishes these signals from standard QCD backgrounds and extends the discovery reach into previously inaccessible parameter space.
This paper demonstrates that accounting for hadrons produced via ion electromagnetic dissociation, which break experimental exclusivity vetos, resolves long-standing tensions between theoretical predictions and LHC measurements for exclusive muon pair and coherent production.
This paper demonstrates that upcoming ultra-high-energy tau neutrino experiments like GRAND and POEMMA will provide significantly more stringent constraints on Lorentz invariance violation than current low-energy probes by analyzing deviations in flavor transition probabilities and event rates across single and multiple LIV operator scenarios.
This paper reviews the Regge approach to pomeron-pomeron fusion and investigates the feasibility of measuring exclusive central diffractive production of and mesons at the LHC to determine the pomeron's spin structure.
This paper proposes a -symmetric mirror extension of the Standard Model within a bi-conformal gravity framework where spontaneous scale invariance breaking generates a light scalaron that naturally suppresses fifth-force couplings via symmetry, predicting a specific correlation between the force strength and scalar mass that aligns with next-generation experimental targets without relying on environmental screening mechanisms.
This paper establishes a model-independent sum rule linking the branching fractions of and decays, which remains exact despite numerous Wilson coefficients and shares numerical coefficients with the semileptonic counterpart, thereby providing a powerful tool to discriminate new-physics scenarios involving left-handed neutrinos.
This paper introduces a robust regression model that utilizes high-level physics objects to precisely reconstruct the key parameter in semi-visible jet events, offering a superior alternative to analytical methods and a unified approach for enhancing sensitivity in both -channel and -channel dark sector searches.
This paper presents a detailed comparison of the SuSAv2 and RDWIA theoretical models against experimental measurements of charged-current neutrino-induced single-pion production from T2K, MINERvA, and MiniBooNE across a broad energy range, highlighting their respective approaches to modeling nuclear targets and pion production channels.
This paper proposes a novel Graph Neural Network approach to mitigate the challenges of increased beam background and irregular hadronic interactions in the Belle~II electromagnetic calorimeter by representing crystal energy depositions as sparse graphs to effectively identify and remove unwanted noise before clustering.