A Living Review of Machine Learning for Particle Physics
- URL: http://arxiv.org/abs/2102.02770v1
- Date: Tue, 2 Feb 2021 04:39:40 GMT
- Title: A Living Review of Machine Learning for Particle Physics
- Authors: Matthew Feickert and Benjamin Nachman
- Abstract summary: Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics.
Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations.
As a living document, it will be updated as often as possible to incorporate the latest developments.
- Score: 0.5076419064097732
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Modern machine learning techniques, including deep learning, are rapidly
being applied, adapted, and developed for high energy physics. Given the fast
pace of this research, we have created a living review with the goal of
providing a nearly comprehensive list of citations for those developing and
applying these approaches to experimental, phenomenological, or theoretical
analyses. As a living document, it will be updated as often as possible to
incorporate the latest developments. A list of proper (unchanging) reviews can
be found within. Papers are grouped into a small set of topics to be as useful
as possible. Suggestions and contributions are most welcome, and we provide
instructions for participating.
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