2092 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 applies first-principles many-body ab initio nuclear modeling with chiral effective field theory to calculate light dark matter mediator production rates at electron fixed-target experiments, revealing that a quasi-elastic treatment can increase predicted signal yields by up to two orders of magnitude compared to standard phenomenological parameterizations.
This paper reviews the theoretical sensitivity of paleo-detectors—mineral samples that record dark matter-induced nuclear recoils over geological timescales—to various WIMP-nucleus interactions within a Non-Relativistic Effective Field Theory framework, demonstrating that they can surpass or match the reach of conventional direct-detection experiments across a wide range of dark matter masses and interaction operators.
This paper introduces RABBIT, a Python framework leveraging TensorFlow 2's differentiable programming and just-in-time compilation to enable fast, robust, and scalable binned profile likelihood minimization for precision LHC measurements, outperforming existing tools in high-dimensional regimes.
This paper proposes a new mechanism called chemical-potential-matched dark matter (DM), where the dark matter mass is comparable to the baryon chemical potential, offering a novel explanation for the baryon-dark matter coincidence that typically predicts dark matter in the eV–keV mass range, or up to the MeV scale if entropy dilution occurs.
This paper presents a comprehensive NLO+PS study of Higgs-boson production with charm and bottom quarks, comparing massive and massless flavour schemes to quantify uncertainties and provide the first practical simulation recommendations for production at the LHC.
Using 140 fb of 13 TeV proton-proton collision data, the ATLAS experiment performed a search for high-mass resonances in photon-jet final states, finding no significant deviations from the Standard Model and setting stringent exclusion limits on excited quark masses up to 6.0 TeV and quantum black hole thresholds up to 7.5 TeV.
This paper investigates third-generation flavor-violating transitions mediated by bosons within the SMEFT framework, utilizing the optimal observable technique to project the Future Circular Collider's (FCC-ee) sensitivity across various energy stages and demonstrating its potential to provide complementary and direct probes of flavor physics alongside low-energy constraints.
This paper serves as an opening keynote lecture for the 4th International Symposium on the History of Particle Physics, surveying the field's development from the prehistory of the discovery through the early 1980s to provide context for the symposium's subsequent presentations.
This paper presents the first successful implementation and validation of GPU-accelerated optical photon simulation using Opticks for the 10-kiloton DUNE far-detector, demonstrating a speedup of up to 313 times over CPU-based GEANT4 while maintaining full Monte Carlo fidelity to enable high-statistics studies and machine learning dataset generation.
This paper introduces Masked Particle Modeling (MPM), a self-supervised pre-training framework that learns permutation-invariant representations of unordered particle sets in high energy physics by reconstructing masked particles via vector quantization, demonstrating its effectiveness as a foundation model for diverse downstream tasks like jet classification and domain transfer.