Semantic Scholar Open Access 2018 421 sitasi

Deep Learning and Its Application to LHC Physics

D. Guest Kyle Cranmer D. Whiteson

Lihat Sumber DOI

Abstrak

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

Topik & Kata Kunci

Physics

Penulis (3)

D. Guest

Kyle Cranmer

D. Whiteson

Format Sitasi

APA MLA BibTeX

Guest, D., Cranmer, K., Whiteson, D. (2018). Deep Learning and Its Application to LHC Physics. https://doi.org/10.1146/annurev-nucl-101917-021019

Akses Cepat

Lihat di Sumber doi.org/10.1146/annurev-nucl-101917-021019

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 421×
Sumber Database: Semantic Scholar
DOI: 10.1146/annurev-nucl-101917-021019
Akses: Open Access ✓