Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • 2000 International Quantum Electronics Conference
  • Technical Digest Series (Optica Publishing Group, 2000),
  • paper QThD89

Monte Carlo simulation of resonance fluorescence under non-Poissonian excitation

Not Accessible

Your library or personal account may give you access

Abstract

The development of laser sources with sub-Poissonian photodetection statistics raises the problem of how such statistical property of a laser could influence an atomic system, and how it could be used in novel applications. In this work we present semi-classical Monte Carlo simulations of the resonance fluorescence experiment in which we consider a variable statistics for the energy exchange between atoms and excitation laser. The laser photon statistics is taken into account by considering bunching and antibunching effects of the absorption and stimulated emission processes while spontaneous emission is still treated as a memoryless process. Our study is motivated both by the interesting statistical properties of the atom-field interaction itself and by the possibility of employing sub-Poissonian lasers on the construction of a magneto-optical trap.

© 2000 IEEE

PDF Article
More Like This
Monte Carlo simulations of time-resolved fluorescence excited in a layered turbid medium

Adam Liebert, Heidrun Wabnitz, Rainer Macdonald, and Herbert Rinneberg
ME7 Biomedical Topical Meeting (BIOMED) 2006

Monte-Carlo Simulation of Photocarrier Dynamics; THz-pulse and Second-Harmonic Generation from Semiconductor Surface

V.L. Malevich
QThD102 International Quantum Electronics Conference (IQEC) 2000

Path-based preprocess method for accelerating decoupled fluorescence Monte Carlo simulation

Xu Jiang, Yong Deng, and Qingming Luo
W3A.47 International Conference on Photonics and Imaging in Biology and Medicine (PIBM) 2017

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.