Machine learning at the energy and intensity frontiers of particle physics

By A. Radovic, Mike Williams, D. Rousseau, Michael Kagan, D. Bonacorsi, A. Himmel, A. Aurisano, K. Terao, T. Wongjirad. Nature, 2018

451 citations recorded by the source corpus.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/bb0bec2f6836b97af51f280eb77895e7170f47c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Nuclear and High Energy Physics

1. Y. Farzan
2. M. Tórtola
3. I. Moult
4. Benjamin Nachman
5. C. Ternes
6. A. Larkoski
7. F. Krennrich
8. O. Mena

Citations in the corpus

• Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “ented by reviews and texts on QCD relevant for jet substructure calculations [10,11,12,13,14,15,16,17], as well as several standard texts and reviews on deep learning [18,19,20] (for particle physics [21,22]), and the standard machine learning packages [23,24,25,26,27,28,29] (for particle physics [3”
• Graph neural networks in particle physics (2020, background): “[1] Alexander Radovic, Mike Williams, David Rousseau, Michael Kagan, Daniele Bonacorsi, Alexander Himmel, Adam Aurisano, Kazuhiro Terao, and Taritree Wongjirad.”
• Deep Neural Network Inverse Design of Integrated Photonic Power Splitters (2019, background): “The huge success of deep learning in modeling complex input-output relationship has attracted attention from several scientific communities such as material discovery [15], high energy physics [16], single molecule imaging medical diagnosis [17], and particle physics [18].”
• Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, background): “but also a plethora of scientific fields ranging from particle signal detection [54], protein structure prediction [55], medical image analysis [56], to the survey of the universe [57].”
• Machine learning and the physical sciences (2019, unknown): “(Guest et al., 2018; Radovic et al., 2018)).”
• LIA: Latently Invertible Autoencoder with Adversarial Learning (2019, background): “…in cracking challenges in various scientific disciplines: high-quality image generation [15, 47, 17, 18, 4], text-to-speech transformation [41, 43], information retrieval [44], 3D rendering [45, 9], signal-to-image acquisition [46], molecular and material science [5, 34], particle physics [31], etc.”
• Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics (2020, methodology): “Similar techniques have been demonstrated to outperform traditional methods in many aspects of high energy physics [182].”
• ARTIFICIAL INTELLIGENCE AND PERSONALIZED MEDICINE (2019, background): “[23 ] Interestingly, the design of new drugs, for example in the context of refining the structure of a therapeutic protein or molecule, could also be greatly facilitated by AI.”
• Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild (2019, background): “…are increasingly part of interdisciplinary collaborations in which they work closely with domain experts, such as doctors, physicists, geneticists, and artists (Bhardwaj et al., 2017; Radovic et al., 2018; Zou 2019 ICML Workshop on Human in the Loop Learning (HILL 2019) , Long Beach, USA.”