TY - JOUR
T1 - A genetic search in frequency space for stabilizing atoms by high-intensity laser fields
AU - Vemuri, V Rao
AU - Barash, Danny
AU - Orel, Ann E
N1 - Funding Information:
The authors would like to acknowledge helpful discussions with K.C. Kulander. One of the authors DB thanks W. Hugh Woodin for the fine hospitality at the UC Berkeley Mathematics department. The genetic algorithm front-end driver code, written by D.L. Carroll, was used in this work. Computing resources were supported by NPACI National Partnership for Advanced Computational Infrastructure . Work was performed on the SDSC San-Diego Super Computer Center Cray T3E and the UC Berkeley NOW Network Of Workstations . AEO and DB acknowledge support provided by the National Science Foundation under grant PHY-93-22067.
PY - 2000
Y1 - 2000
N2 - The goal of this paper is to explore the power of stochastic search methods, in particular genetic algorithms, to solve a challenging problem in experimental physics. The problem is to find an optimum frequency to stabilize atoms by high-intensity laser fields. The standard approach to search for optimal laser parameters has been by trial and error. This is the first known application of a genetic algorithm technique to model atomic stabilization. Genetic algorithms worked well for this problem as a way to automate the search in a time efficient manner. A parallel platform is used to perform the genetic search efficiently. Locating the best frequency to achieve a suppression of ionization, which is predicted to occur at high intensities, can help design a laboratory experiment and tune to that frequency in order to identify a stabilization effect. The genetic algorithms did successfully identify this optimum frequency. It is indeed possible to extend the number of unknown tunable laser parameters, beyond searching merely over frequency space. For instance, optimal pulse shape and pulse duration can also be included. While conducting such a search in multi-dimensional parameter space, parallel genetic algorithms can offer an advantage to the tedious trial and error procedures.
AB - The goal of this paper is to explore the power of stochastic search methods, in particular genetic algorithms, to solve a challenging problem in experimental physics. The problem is to find an optimum frequency to stabilize atoms by high-intensity laser fields. The standard approach to search for optimal laser parameters has been by trial and error. This is the first known application of a genetic algorithm technique to model atomic stabilization. Genetic algorithms worked well for this problem as a way to automate the search in a time efficient manner. A parallel platform is used to perform the genetic search efficiently. Locating the best frequency to achieve a suppression of ionization, which is predicted to occur at high intensities, can help design a laboratory experiment and tune to that frequency in order to identify a stabilization effect. The genetic algorithms did successfully identify this optimum frequency. It is indeed possible to extend the number of unknown tunable laser parameters, beyond searching merely over frequency space. For instance, optimal pulse shape and pulse duration can also be included. While conducting such a search in multi-dimensional parameter space, parallel genetic algorithms can offer an advantage to the tedious trial and error procedures.
UR - http://www.scopus.com/inward/record.url?scp=33747272546&partnerID=8YFLogxK
U2 - 10.2498/cit.2000.02.02
DO - 10.2498/cit.2000.02.02
M3 - מאמר
SN - 1330-1136
VL - 8
SP - 103
EP - 113
JO - Journal of Computing and Information Technology
JF - Journal of Computing and Information Technology
IS - 2
ER -