Examples of applications of ML in physics
The large amount of degrees of freedom pertain to both theory and
experiment in physics. With increasingly complicated
experiments that produce large amounts data, automated classification
of events becomes increasingly important. Here, deep learning methods
offer a plethora of interesting research avenues.
- Reconstruction of particle trajectories or classification of events are typical examples where ML methods are being used. However, since these data can often be extremely noisy, the precision necessary for discovery in physics requires algorithmic improvements. Research along such directions, interfacing nuclear physics with AI/ML is expected to play a significant role in physics discoveries related to new facilities. The treatment of corrupted data in imaging and image processing is also a relevant topic.
- Design of detectors represents an important area of applications for ML/AI methods in subatomic physics.