Physicists at the Large Hadron Collider (LHC) have long struggled with interpreting data from the massive experiment, which requires complex statistical modeling. A new approach using deep neural networks has shown promise in tackling one of the biggest challenges: interference, a fundamental aspect of quantum mechanics.
Interference allows two possible events to cancel each other out, reducing the likelihood of detecting either result. This limits the power and increases the uncertainty of LHC data. However, researchers have now found a way to overcome this hurdle using Neural Simulation-Based Inference (NSBI), a machine learning technique developed by a young researcher.
The ATLAS collaboration, one of two groups studying proton collisions at the LHC, recently released papers outlining NSBI’s potential and its effectiveness in analyzing data from their detector. The breakthrough demonstrates that NSBI can significantly improve analysis results compared to traditional methods.
This success has already influenced the experiment’s plans for future work, marking a significant step forward in understanding the quantum world through cutting-edge technology.
Source: https://arstechnica.com/science/2025/06/how-a-grad-student-got-lhc-data-to-play-nice-with-quantum-interference