Deep Learning for Physics Research

by Martin Erdmann, Jonas Glombitza, Gregor Kasieczka

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.

340 pages, Hardcover

Published June 28, 2021

Reviews

[Andrej Novák · Rating 5 out of 5]

If you want to dive into the deep learning concepts and various ways in which you can implement it in your physics research, this book is for you. Especially perfect for Timepix/2/3/4 hybrid pixel detector research, image recognition and spectral particle tracking in pixel detectors in general.