Research within the domain of physics has profited from the rise of artificial neural networks and deep learning. In the past, we've seen them being applied to study dark matter and massive galaxies. Continuing this pattern, we now have artificial neural networks being used in the study of exotic particles.
At the Compact Muon Solenoid (CMS), which is a particle detector built on the Large Hadron Collider (LHC) at CERN, researchers are using neural networks to identify atypical experimental signatures resulting from proton–proton collisions inside the LHC.
These experimental signatures are hard to track for traditional algorithms as most of the 'debris' generated by a collision is short-lived. But neural networks can prove to be potent in this situation. This is due to the fact that they can be trained on real-world data.
CMS' neural network has been trained with such data and will soon be in a position to detect the experimental signatures automatically. For training, the researchers have used domain adaptation by backward propagation to improve the simulation modeling of the jet class probability distributions observed in collision data.
The neural network has been trained (with supervision) to distinguish sprays of particles known as “jets” produced by the decays of long-lived particles from jets produced by far more common physical processes.
The model has shown promising results thus far. During the analysis of a particle track where the probability of correctly identifying a jet from a long-lived particle was 50%, the model misidentified only one regular jet in every thousand and demonstrated a low count of false negatives and false positives.
CERN believes that the new system will help advance the firm's quest for finding ephemeral and exotic particles. For more information, you may study the paper published on arXiv.