AI and Machine Learning in Dark Matter Searches: From Anomaly Detection to Detector Optimization
A special issue of Particles (ISSN 2571-712X). This special issue belongs to the section "Computational and Mathematical Physics, AI and Machine Learning".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 61
Special Issue Editors
Interests: data analysis; computational techniques; physics beyond the standard model
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in the search for dark matter, revolutionizing our ability to analyze vast datasets, enhance signal sensitivity, and optimize experimental design. As dark matter remains one of the most profound mysteries in fundamental physics, cutting-edge AI-driven approaches are playing an increasingly vital role in exploring new parameter spaces and refining detection strategies.
This special issue focuses on the latest innovations at the intersection of AI, ML, and searches for dark matter. Key topics include advanced anomaly detection techniques for identifying rare events within high-dimensional datasets, deep learning architectures for improving signal-to-background discrimination, and AI-driven optimization of detector development and calibration. These methods are being actively integrated into both direct and indirect detection experiments, as well as collider-based research, providing new avenues to uncover potential dark matter signatures.
Furthermore, recent developments in generative models, normalizing flows, and unsupervised learning have enabled novel approaches to anomaly detection, reducing dependence on predefined signal hypotheses and allowing for more agnostic searches. Reinforcement learning is also being explored for real-time data acquisition strategies and autonomous experiment tuning, ensuring maximum sensitivity in next-generation dark matter detectors.
Dr. Ankush Reddy Kanuganti
Dr. Ashik Ikbal Sheikh
Guest Editors
Manuscript Submission Information
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Keywords
- dark matter searches
- anomaly detection
- deep learning (GNNs, transformers)
- detector optimization
- silicon detectors for future colliders
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