Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC
arXiv:2605.28296v1 Announce Type: new Abstract: In modern nuclear physics experiments, identifying events of interest is challenging for nuclear reaction studies with the active target Time Projection Chamber (TPC). In this work, machine learning techniques are employed to analyze the complex data of the 12C + 12C fusion reaction from a TPC named MATE (multi-purpose active-target time projection chamber for nuclear experiments). Specifically, we successfully applied Residual Neural Network (ResNet-50, ResNet-34 and ResNet-18) and Visual Geometry Group (VGG-19) to classify elastic scattering an
The increasing complexity and volume of data from modern scientific experiments, particularly in nuclear physics, necessitate advanced computational methods like machine learning for analysis and interpretation.
This development demonstrates the growing applicability of machine learning in fundamental science, streamlining data analysis in complex experimental setups and potentially accelerating discoveries in fields like nuclear physics.
The efficiency and accuracy of event classification and data reconstruction in nuclear physics experiments can be significantly improved by integrating advanced neural networks, reducing manual effort and processing time.
- · Nuclear physicists
- · Particle accelerator facilities
- · AI/ML research in scientific domains
- · Computational physics
- · Traditional manual data analysis methods
- · Experimental setups without ML integration
Machine learning becomes a standard tool for event classification and data reconstruction across various high-energy physics and nuclear physics experiments.
The improved data analysis capabilities lead to faster identification and validation of new physical phenomena or more precise measurements of known ones.
The success in nuclear physics encourages broader adoption of similar AI techniques in other complex scientific domains facing data analysis bottlenecks, potentially accelerating scientific discovery across the board.
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