Session

DescriptionWith the deluge of data and the increased complexity of detectors at the horizon of the high luminosity large hadron collider, processing data will become even more challenging. Physicists will have to outsmart nature and come up with improved algorithms. Machine learning is very attractive in this respect that one can derive algorithms learned from data. Interpretability, or the lack of thereof, is a limiting factor, but progress is being made. Deep learning applications to high energy physics challenges have met great success in recent years in particle identification, event classification, signal extraction, object reconstruction, and anomaly detection. Even though it seems possible to learn physics solely form data, models still perform better when they are infused with domain knowledge. We propose several topical reviews of proofs of concept in applying deep learning to high energy physics problems. Beyond the proofs of concept, we propose to discuss challenges in implementing these methods within the experiments' data pipeline and computing infrastructure.