21-25 August 2017
University of Washington, Seattle
US/Pacific timezone

Deep Learning usage by large experiments

21 Aug 2017, 11:00
30m
Auditorium (Alder Hall)

Auditorium

Alder Hall

Oral Plenary

Speaker

Dr Ben Nachman (Lawrence Berkeley National Lab. (US))

Description

Modern machine learning (ML) has introduced a new and powerful toolkit to High Energy Physics. While only a small number of these techniques are currently used in practice, research and development centered around modern ML has exploded over the last year(s). I will highlight recent advances with a focus on jet physics to be concrete. Themselves defined by unsupervised learning algorithms, jets are a prime benchmark for state-of-the-art ML applications and innovations. For example, I will show how deep learning has been applied to jets for classification, regression, and generation. These tools hold immense potential, but incorporating domain-specific knowledge is necessary for optimal performance. In addition, studying what the machines are learning is critical for robustness and may even help us learn new physics!

Presentation materials

Peer reviewing

Paper