At the dawn of a new scientific era, where the limits of the possible are being pushed every day, high-energy physics (HEP) is merging with advanced machine learning. This innovative synthesis is opening up the unimaginably tiny world of subatomic particles, promising a revolution in our understanding of the fundamental laws of nature. At the center of this scientific revolution is the Large Hadron Collider (LHC), where particles collide with enormous energy, recreating conditions similar to those just after the Big Bang. These colossal machines are designed to simulate conditions just moments after the Big Bang, colliding subatomic particles with each other at close to the speed of light. This violent collision generates an avalanche of data, recording the fleeting, intricate dance of particle interactions. But with the sheer volume of data comes enormous complexity, and traditional methods of sifting through this cosmic haystack in search of the proverbial needles are proving overwhelming.
In response, the scientific community is turning to deep neural networks (DNNs) – sophisticated algorithms capable of processing these huge data sets. At IFJ PAN in Krakow, Poland, Polish researchers are using DNNs to reconstruct secondary particle tracks with high precision, a step forward for detection techniques.
Research by Poles in “Computer Science”
Over the past decades, there have been significant developments in the field of high-energy physics (HEP) experiments, including computational technologies
– an excerpt from the article “MACHINE LEARNING BASED EVENT RECONSTRUCTION FOR THE MUONE EXPERIMENT” [1].
An excellent example of an innovative approach in particle physics is the MUonE project, which is investigating the anomalous magnetic moment of the muon, the heavier cousin of the electron. This target, with the potential to uncover new physics, challenges our current theoretical models. In this context, the use of machine learning-based methods becomes crucial, especially in precisely tracking the trajectories of muons and their interaction points in three-dimensional space. The experiment uses deep neural networks (DNNs) trained on simulated collision data, reducing the time required for pattern recognition and increasing the precision of analysis.
In our paper, we show that a deep neural network trained on a properly prepared database is able to reconstruct secondary particle tracks as accurately as classical algorithms. This is a result of great importance for the development of detection techniques. For while learning a deep neural network is a lengthy and computationally demanding process, a trained network already responds instantly. So, since it still does it with satisfactory precision, we can think optimistically about its use in real crashes,” Prof. Kucharczyk, in an interview for the Polish Academy of Sciences [2].
This approach has significance beyond the MUonE project. DNNs, demonstrating their prowess in the field of particle physics, are becoming a standard tool in high-energy physics. Future experiments, requiring real-time and high-precision data processing, will likely rely on advanced machine learning techniques. This combination of physics and artificial intelligence opens up new possibilities in understanding the fundamental particles and forces of the universe, and exemplifies the symbiotic relationship between science and technology.
IFJ PAN is at the center of work on an innovative deep neural network that allows precise data analysis thanks to millions of configuration parameters. The network, trained on simulated particle collisions, efficiently recognizes and reconstructs secondary particle tracks, opening new horizons in data processing in nuclear physics.
The results of the DNN-based algorithm are comparable to classical reconstruction, significantly reducing the execution time of the pattern recognition phase
– an excerpt from the article “MACHINE LEARNING BASED EVENT RECONSTRUCTION FOR THE MUONE EXPERIMENT.”
Artificial intelligence will play a key role in the MUonE experiment, helping to increase the precision of measurements and improve theoretical physics models. This, in turn, will enable a deeper understanding of particle physics, especially in the context of the Standard Model. Research conducted at the US Fermilab has shown that the so-called anomalous magnetic moment of muons deviates from the predictions of this model, reaching a significance of 4.2 standard deviations (sigma). In the field of physics, usually only a value above 5 sigma, which corresponds to 99.99995% confidence, is considered sufficient to announce a discovery [2]. Success in the MUonE experiment may not only confirm the effectiveness of applying artificial intelligence to particle collision analysis, but also open new avenues in the search for new physics.
A prototype of a DNN-based algorithm for three-dimensional track reconstruction in the MUonE experiment has proven to be competitive with classical track reconstruction tasks in terms of quality and potential performance advantages, an excerpt from the article “MACHINE LEARNING BASED EVENT RECONSTRUCTION FOR THE MUONE EXPERIMENT.”
The contributions of Krakow scientists to nuclear physics could revolutionize the way we analyze particle collisions on the largest scale. Artificial intelligence, with its ability to process huge amounts of data quickly and efficiently, is becoming a key tool in the search for answers to science’s most complex questions. The MUonE experiment at CERN will be the ultimate test for this cutting-edge technology, and its success could open a new era in particle detection and nuclear physics.
Cover photography: Pixabay
Bibliography:
[1] Zdybal, M., Kucharczyk, M., & Wolter, M. (2024). Machine Learning Based Event Reconstruction for the MUONE Experiment. Computer Science, 25(1). https://doi.org/10.7494/csci.2024.25.1.5690
[2] Artificial intelligence will reconstruct particle paths leading to new physics, https://pan.pl/sztuczna-inteligencja-zrekonstruuje-drogi-czastek-prowadzace-ku-nowejfizyce/
Zuzanna Czernicka
