Brief

Physicists are broadening their search for dark matter as current experiments, like those using liquid xenon detectors deep underground, are increasingly detecting neutrinos instead of the elusive particles. This "neutrino fog" is making it difficult to find weakly interacting massive particles (WIMPs) where they were previously expected. The lack of direct detection at facilities like the Large Hadron Collider and in underground experiments has spurred a wide array of new search strategies, including quantum sensors and axion detection experiments, reflecting a shift from a narrow focus to a more diverse and exploratory approach in understanding the universe's composition. AI

Researchers have developed a machine learning approach to enhance the detection of dark matter candidates at the Large Hadron Collider (LHC). This method specifically targets WIMP dark matter within the Next-to-Minimal Supersymmetric Standard Model (NMSSM), focusing on scenarios where direct detection signals are suppressed. The ML analysis improves sensitivity to subtle signals from radiatively decaying neutralinos, which present a distinctive collider signature with multiple photons. With 100 fb^{-1} of data at 14 TeV, the ML approach can achieve a 5σ discovery reach for higgsino masses up to 225 GeV. AI