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

Minimizing residual frequency dispersion that accompanies pulse stretching, amplification, and recompression is an important consideration in ultrasho rt chirped-pulse amplifiers.. Here we show how an adaptive learning algorit hm 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 'le arning loop' comprised of the pulse shaper, strong field laser ionization, and a genetic algorithm to minimize dispersion through the amplifier. We ve rify our optimization results using frequency-resolved optical Sating (FROG ) measurements and also show theoretically and experimentally that spectral blueshifting is indeed a sensitive diagnostic for pulse shape, and specifi cally, for higher-order dispersion.