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The evolution of magnetic spectrometers has enabled significant advances in experimental nuclear physics, but it has also posed increasingly complex technical challenges. A emblematic example is the VAMOS++ system, originally designed with a highly non-linear ion optics that requires advanced computational methods to reconstruct particle trajectories. Traditionally, these reconstructions were based on models that assumed a point-like interaction volume, valid only when thin targets and focused beams are used. However, this approximation becomes insufficient when the beam has a large size or when the target is gaseous and extended, limiting the precision of the magnetic rigidity and path length measurements.
To overcome this restriction, an innovative approach has been developed that incorporates the three-dimensional coordinates of the reaction point within the target. This seven-dimensional reconstruction methodology—which adds the three spatial coordinates to the four traditional variables—uses deep artificial neural networks trained with theoretical data generated by magnetic ray-tracing codes. The result is a substantial improvement in mass resolution, even under complex experimental conditions, such as those encountered with voluminous gaseous targets. This advance demonstrates how artificial intelligence can solve ion optics problems that previously required restrictive simplifications.
From a business and technological perspective, this case illustrates the importance of developing custom applications that address specific scientific needs. At Q2BSTUDIO, we understand that each project requires a unique approach, whether in the field of nuclear physics, industry, or services. Our experience in AI for businesses allows us to design machine learning systems that optimize complex processes, such as training neural networks for trajectory reconstruction or integrating sensors in extreme environments. Furthermore, we combine these capabilities with AWS and Azure cloud services to ensure scalability and performance in processing large volumes of data, something critical in ray-tracing simulations and model validation.
The implementation of specialized AI agents could also automate real-time calibration of the spectrometer, reducing the need for human intervention and minimizing systematic errors. Likewise, integrating Power BI and other business intelligence service tools allows experimental results to be visualized and analyzed interactively, facilitating decision-making for research teams. On the other hand, data and infrastructure security is essential when handling critical experimental configurations; therefore, we offer cybersecurity to protect both acquisition systems and cloud storage.
Ultimately, 7D trajectory reconstruction for VAMOS++ not only represents a scientific achievement, but also an example of how custom software and artificial intelligence can transform established methodologies. At Q2BSTUDIO, we apply these same principles to help companies and institutions solve their most complex challenges, whether through automation solutions, data analysis, or cloud deployment. The lesson is clear: when standard tools fall short, customized innovation is the key to moving forward.
