arXiv:2509.15121v2 Announce Type: replace-cross Abstract: We investigate a WIMP dark matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the $Z_3$-symmetric Next-to-Minimal Supersymmetric Standard Model (NMSSM). This framework gives rise to regions of parameter space where DM is obtained via co-annihilation with nearby higgsino-like electroweakinos and DM direct detection~signals are suppressed, the so-called ``blind spots''. On the other hand, collider signatures remain promising due to enhanced radiative decay modes of higgsinos into the si
The continuous advancements in machine learning, coupled with ongoing high-energy physics experiments at facilities like the LHC, are creating new avenues for analysing complex scientific data.
This research highlights the growing utility of AI in fundamental science, particularly in areas like particle physics, to uncover hard-to-detect phenomena such as dark matter, potentially leading to breakthroughs in our understanding of the universe.
The application of machine learning techniques can significantly enhance the sensitivity and discovery potential of existing and future collider experiments for new physics signatures that were previously inaccessible.
- · High-energy physicists
- · Machine learning researchers
- · Particle accelerator facilities
- · Traditional statistical analysis methods
Machine learning becomes an indispensable tool for analyzing vast datasets in experimental physics, accelerating discovery.
New theoretical models and experimental designs are influenced by ML-driven insights, leading to more targeted research.
Fundamental breakthroughs in physics, aided by AI, could eventually lead to new technologies or a paradigm shift in human understanding of reality.
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