The principal component analysis is applied to a data sample, with the aim of retaining the smallest number of variables necessary to adequately describe the data. We find that in a particular example, of the initial 20 variables necessary to fully describe the data, only 10 principal components suffice to determine their structure. The application of these ideas to obtain a fast Monte Carlo event generator is discussed in detail for particle physics.
A fast Monte Carlo event generator for particle physics / Iaselli, Giuseppe. - In: NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH. SECTION A, ACCELERATORS, SPECTROMETERS, DETECTORS AND ASSOCIATED EQUIPMENT. - ISSN 0168-9002. - STAMPA. - 248:2-3(1986), pp. 488-490. [10.1016/0168-9002(86)91037-5]
A fast Monte Carlo event generator for particle physics
Giuseppe Iaselli
1986-01-01
Abstract
The principal component analysis is applied to a data sample, with the aim of retaining the smallest number of variables necessary to adequately describe the data. We find that in a particular example, of the initial 20 variables necessary to fully describe the data, only 10 principal components suffice to determine their structure. The application of these ideas to obtain a fast Monte Carlo event generator is discussed in detail for particle physics.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
