Advanced Detector Technologies in Astroparticle Physics: Low-Background Sensors and Artificial Intelligence-Assisted Rare-Event Searches

Advanced detector technologies have become essential tools for probing rare and low-energy phenomena in astroparticle and nuclear physics, with applications spanning dark matter searches, coherent neutrino scattering, neutrinoless double-beta decay, and other rare-event searches. Modern experiments employ a diverse range of detector concepts optimized for different signal channels, interaction mechanisms, target materials, and energy thresholds.In direct dark matter detection, this landscape includes Cryogenic Semiconductor Detectors, Pure Ionization Detectors, Bubble Chambers, Noble-Liquid Detectors, Scintillating Crystals, CCD/Imaging Detectors, and Directional Gas Detectors. These technologies provide complementary capabilities in terms of energy threshold, particle identification, background discrimination, event localization, directionality, and scalability. Within this wider landscape, cryogenic technologies based on Transition Edge Sensors (TESs), Metallic Magnetic Calorimeters (MMCs), Kinetic Inductance Detectors (KIDs), low-temperature calorimeters, and related quantum and thermal sensors offer particularly powerful capabilities for resolving minute energy depositions, making them especially important for searches for low-mass dark matter and coherent neutrino interactions.As next-generation experiments pursue increasingly weak and rare signals, demands on detector performance, readout systems, calibration, event reconstruction, and background rejection continue to rise. The growing complexity of detector systems, together with larger datasets and increasingly stringent background requirements, is driving the development of advanced computational approaches for detector characterization, event reconstruction, signal discrimination, and experimental optimization.Artificial intelligence (AI) and machine learning (ML) offer new opportunities across the full rare-event detection chain. These methods can extract information from complex detector waveforms and images, improve pulse-shape and topology-based discrimination, identify subtle signal features, classify interaction types, reject environmental and radioactive backgrounds, reconstruct event positions and energies, and enable efficient real-time event triggering. Furthermore, AI-based methods may help address unexplained populations of events close to detector thresholds, including the low-energy excesses observed in several low-threshold rare-event experiments.We welcome Original Research, Reviews, Mini Reviews, and Perspective articles addressing, but not limited to, the following themes:-Cryogenic and Thermal Sensors: Phonon and ionization detection in silicon, germanium, and related targets; TESs, MMCs, KIDs, low-threshold operation, Neganov–Trofimov–Luke amplification, and sensor integration.-Direct Detection Platforms: Pure ionization detectors (skipper-type charge readout, single-electron sensitivity), superheated-fluid bubble chambers, noble-liquid TPCs (liquid xenon/argon), scintillating crystals, high-resistivity CCDs, and directional gaseous TPCs.-Machine Learning for Rare-Event Detection: Pulse-shape discrimination, event classification, particle identification, nuclear- versus electronic-recoil discrimination, background rejection, and rare-signal extraction.-Deep Learning and Anomaly Detection: Identification of rare or previously unknown event populations, unsupervised and semi-supervised learning, outlier detection, and addressing near-threshold low-energy excesses.-AI-Enabled Detector Optimization and Control: Hardware–software co-design, detector calibration, characterization, noise mitigation, operating-point optimization, automated control, and data-quality monitoring.-Statistical Rigor, Uncertainty, and Interpretability: Comparative evaluations of classical (likelihood, optimal-filter) versus ML approaches, systematic uncertainties, explainable AI, domain adaptation, and simulation-to-data validation.-Background Modeling and Mitigation: Radioactive, cosmogenic, surface, neutron, and instrumental backgrounds; active/passive shielding; underground operations; and AI-assisted background identification.-Experimental Demonstrations and Physical Applications: Detector performance and results from ongoing and planned experiments targeting dark matter, coherent neutrino scattering, neutrinoless double-beta decay, and dark-sector searches. Topic editors: Muad Ghaith, Ziqing Hong Source: https://www.frontiersin.org/research-topics/83264/advanced-detector-technologies-in-astroparticle-physics-low-background-sensors-and-artificial-intelligence-assisted-rare-event-searches