The quest for understanding the fundamental laws of physics has led scientists far in probing nature at high energies and indeed in exploring the origins of the universe as we observe it. Elusive hints to the Standard Model and new physics are hidden amongst large and complex background processes. The next generations of experiments in particle physics and astroparticles will take an unprecedented large volume of data to achieve their scientific objectives.
Physicists are therefore faced with challenges of data selection, acquisition, distribution, archiving, and analysis. New hardware and software will play a significant role in achieving these goals, at all levels, and in particular hardware-optimized software and software-oriented hardware will be game changers in the field. As Moore’s Law is coming to a limitation for expanding in conventional computation resource, the community will have to tap on accelerators and new architectures to fulfil its mission. New computation paradigm will require new algorithms and approaches to physics problems, and in particular, artificial intelligence techniques might supplement other more expensive solutions.
The Big Data and AI in High Energy Physics section is particularly interested in publishing work on topics related to solving the challenge of processing big data from science and improving the reach using artificial intelligence. This includes but is not limited to:
We are particularly interested in articles on the above using machine learning. Beyond these topics, we will also consider high-quality papers on related topics. The section publishes high-quality papers and supports authors with a streamlined interactive peer-review system.
As an umbrella journal, Frontiers in Big Data can significantly increase the visibility and readership base of articles and authors. Research Topics / Article Collections can be cross-featured in other relevant sections and even journals, increasing readership and interdisciplinarity for both individual articles and the topic as a whole.
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