Artificial Intelligence for High Energy Physics, by Paolo Calafiura, David Rousseau, Kazuhiro Terao, World Scientific
The appearance of the word “for” rather than “in” in the title of this collection raises the bar from an academic description to a primer. It is neither the book’s length (more than 800 pages), nor the fact that the author list resembles a who’s who in artificial intelligence (AI) research carried out in high-energy physics that makes this book live up to its premise; it is the careful crafting of its content and structure.
Artificial intelligence is not new to our field. On the contrary, some of the concepts and algorithms have been pioneered in high-energy physics. Artificial Intelligence for High Energy Physics credits this as well as reaching into very recent AI research. It covers topics ranging from unsupervised machine-learning techniques in clustering to workhorse tools such as boosted decision trees in analyses, and from recent applications of AI in event reconstruction to simulations at the boundary where AI can help us to understand physics.
Each chapter follows a similar structure: after setting the broader context, a short theoretical introduction into the tools (and, where possible, the available software) is given, which is then applied and adapted to a high-energy physics problem. The ratio of in-depth theoretical background to AI concepts and the focus on applications is well balanced, and underlines the work of the editors, who avoided duplication and cross-reference individual chapters and topics. The editors and authors have not only created a selection of high-quality review articles, but a coherent and remarkably good read. Takeaway messages in the chapter for distributed training and optimisation stand out, and one might wish that this concept found more resonance throughout the book.
Sometimes, the book can be used as a glossary, which helps to bridge the gaps that seem to exist simply because high-energy physicists and data scientists use different names for similar or even identical things. While the book can certainly be used as a guide for a physicist in AI, an AI researcher with the necessary physics knowledge may not be served quite so well.
In an ideal world, each chapter would have a reference dataset to allow the reader to follow the stated problems and learn through building and exercising the described pipelines. This, however, would turn the book from a primer into a textbook for AI in high-energy physics. To be fair, wherever possible the authors of the chapters have used and referred to publicly available datasets, and one chapter is devoted to the issue of arranging a community data competition, such as the TrackML challenge in 2018.
As for the most important question – have I learned something new? – the answer is a resounding “yes”. While none of the broad topics and their application to high-energy physics will come as a surprise to those who have been following the field in recent years, there are neat projects and detailed applications showcased in this book. Furthermore, reading about a familiar topic in someone else’s words can be a powerful eye opener.