Minimization of dispersion in an ultrafast chirped pulse amplifier using adaptive learning

Abstract.

Minimizing residual frequency dispersion that accompanies pulse stretching, amplification, and recompression is an important consideration in ultrashort chirped-pulse amplifiers. Here we show how an adaptive learning algorithm can be used in conjunction with a pulse shaper to compensate for higher-order and nonlinear dispersion in a chirped-pulse amplifier. Using spectral blueshifting as a sensitive diagnostic for pulse shape, we implement a ‘learning loop’ comprised of the pulse shaper, strong field laser ionization, and a genetic algorithm to minimize dispersion through the amplifier. We verify our optimization results using frequency-resolved optical gating (FROG) measurements and also show theoretically and experimentally that spectral blueshifting is indeed a sensitive diagnostic for pulse shape, and specifically, for higher-order dispersion.