<i>Colloquium</i> : Machine learning in nuclear physics

Parameterized Machine Learning for High-Energy Physics

We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individu...

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

Machine learning for event selection in high energy physics

The field of high energy physics aims to discover the underlying structure of matter by searching for and studying exotic particles, such as the top quark and Higgs boson, produced in collisions at modern accelerators. Since such accelerators are extraordinarily expensive, extracting maximal information from the resulting data is essential. However, most accelerator events do not produce partic...

Physics in Nuclear Medicine

Frontiers in Nuclear Physics

These are the proceedings of the symposium on “Frontiers in Nuclear Physics” held at the Physikzentrum Bad Honnef from June 18 to 20, 2009. The workshop concentrated on recent advances in the understanding of the nuclear forces, fewnucleon systems and related topics. Included are a short contribution per talk. ∗ This symposium was supported in parts by funds provided from the Helmholtz Associat...