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In the realm of nuclear physics, artificial intelligence (AI) is poised to transform the way particle paths are reconstructed in accelerators, promising advancements in the search for new physics. Scientists at the Institute of Nuclear Physics of the Polish Academy of Sciences (IFJ PAN) in Cracow, Poland, are at the forefront of developing AI algorithms that could soon enhance the analysis of particle collisions, a critical step in identifying phenomena that extend beyond our current understanding of physics.
Traditionally, the analysis of particle collisions has relied on rapid data processing by electronics to determine the significance of events for further investigation. However, the sheer volume of data generated by accelerators like the Large Hadron Collider (LHC) poses a challenge, necessitating the development of more efficient methods for reconstructing particle tracks.
The IFJ PAN team proposes the use of AI, specifically deep neural networks, to tackle this challenge. Their research, featured in the journal Computer Science, demonstrates the potential of AI to offer an effective alternative to conventional track reconstruction techniques. With the upcoming MUonE experiment, which aims to explore discrepancies in the Standard Model of particle physics, these AI tools could soon be put to the test.
Prof. Marcin Kucharczyk and Dr. Milosz Zdybal, leading researchers at IFJ PAN, explain that the complexity of detecting particle paths is exacerbated by the detector's environment, including the presence of magnetic fields and high occupancy rates, which complicate the accurate reconstruction of tracks. AI excels in environments where it must quickly recognize patterns amidst noise and high data volumes, making it an ideal candidate for this task.
The team's deep neural network, trained on simulated particle collisions, has shown promising results in accurately reconstructing particle tracks, matching the performance of traditional algorithms. This advancement is significant not only for the MUonE experiment but also for the future of particle physics research, where increasing the precision of experimental data is paramount.
The implications of AI in particle physics extend beyond track reconstruction. By improving the accuracy and efficiency of data analysis, AI has the potential to unlock new discoveries and provide deeper insights into the fundamental nature of the universe. As the MUonE experiment progresses, the AI developed by IFJ PAN could play a crucial role in advancing our understanding of physics, marking a new era in the field.
The research conducted by the IFJ PAN team is supported by the Polish National Science Centre, highlighting the institute's commitment to pushing the boundaries of scientific knowledge. With its rich history of contributions to physics and its status as a leading research institute, IFJ PAN continues to be at the cutting edge of technological and scientific advancements.
Research Report:Machine Learning based Event Reconstruction for the MUonE Experiment
Related Links
Nuclear Physics Polish Academy of Sciences
Understanding Time and Space
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