Year: 1996
Type: article
Abstract: An accurate impact parameter determination in a heavy ion collision is crucial for almost all further analysis. The capabilities of an artificial neural network are investigated in that respect. A novel input generation for the network is proposed, namely, the transverse and longitudinal momentum distributions of all outgoing (or actually detectable) particles. The neural network approach yields an improvement in performance of a factor of 2 as compared to classical techniques. To achieve this improvement simple network architectures and a 5\ifmmode\times\else\texttimes\fi{}5 input grid in (${\mathit{p}}_{\mathit{t}}$,${\mathit{p}}_{\mathit{z}}$) space are sufficient. \textcopyright{} 1996 The American Physical Society.
Source: Physical Review C
Institution: Goethe University Frankfurt
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2951716910