This note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. With the aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief...
An ultra-compressed deep neural network on a field-programmable gate array. Credit: Sioni P. Summers Machine learning is everywhere. For example, it's how Spotify gives you suggestions of what to listen to next or how Siri answers your questions. And it's used in particle physics too, fro...
There have been large and sustained developments of deep learning in high-energy physics over the past several years. Supervised machine learning methods are widely used to identify known particles and to design targeted searches for specific theories of new physics. Less-than-supervised machine learni...
Publisher’s note:Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. About this article Cite this article Radovic, A., Williams, M., Rousseau, D.et al.Machine learning at the energy and intensity frontiers of particle physics.Nature...
Welcome to the Ultralytics WAVE repository –the cutting-edge solution for the machine learning driven analysis and interpretation of waveform data in particle physics! 🎉 Here, we introduce WAveform Vector Exploitation (WAVE), a novel approach that uses Deep Learning to readout and reconstruct si...
第2章《数据科学中的物理方面》(Physical Aspects of Machine Learning in Data Science) 主要探讨了物理学原理和概念如何与数据科学及机器学习相结合。以下是该章节内容的详细概述: ### 2.1 引言 (Introduction) - 介绍了数据科学在各个行业中的普及,以及计算、机器学习和数据获取技术的进步如何促进了这一领域的发展...
Accordingly, it is also widely used in the experimental and theoretical research of particle physics. This article discusses several machine learning techniques that are particularly relevant to nuclear physics. Additionally, the application of machine learning in various contexts of nuclear physics ...
Machine learning has been in use in high-energy particle physics for well over a decade, but the rise of deep learning in the early 2010s has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present ...
Therefore, in the present context, the combination of a basic physical model and a machine learning framework has the following favourable properties: (i) the resulting model is not limited by the particle size, as is the case in the asymptotic model - at least to the extent that finite-siz...
Carminati, F. et al. Calorimetry with deep learning: particle classification, energy regression, and simulation for high-energy physics. InNIPS Deep Learning for Physical Sciences Workshop(NIPS, 2017). Louppe, G., Kagan, M. & Cranmer, K. Learning to pivot with adversarial networks.Adv. Neura...