Accurate temporal characterization both in intensity and phase distribution is important in the diagnosis of the petawatt (PW) class. We present a single-shot picosecond frequency-resolved optical gating (ps-FROG) setup based on an autocorrelator with ps measurement range that is spectrally resolved through a fine grating. The modified ptychographic-based algorithm with a changing update coefficient was used for the reconstruction of the pulse distribution; it can better adapt to the reconstruction of pulse with a large time–bandwidth product. We calibrated and verified the homemade ps-FROG in a 100-μJ ps laser system and used it to characterize the pulse distribution generated by the PW laser system of the Shen Guang II facility. The system shows good performance and high accuracy in reconstructing the intensity and phase distributions of a ps pulse, which provides reference for accurately adjusting the grating pair to acquire the pulse width as a preset.
The spectrum is a crucial parameter to a petawatt laser which is adopting the chirped pulse amplification technique. In such complex systems with high gain and wide spectrum bandwidth, the shape of the spectrum is crucial to the final output pulse width. In daily operation, the width of the compressed pulse will have some abnormal fluctuation, and the shape of the spectrum before compressed is also changed at the same shot. It will mislead the power and intensity estimation in laser-matter interaction experiments. So far, no theory has been able to analyze the relationship between spectrum and pulse width completely. Because it is hard to describe the fluctuation of the compressed pulse width which the online measure spectral phase in the high power laser system is difficult. In this paper, we first found and analyzed the relation between spectral variation and pulse width in the petawatt laser. With the support of existing data, we establish an end-toend deep learning model to map the petawatt laser’s spectrum before the compressor to the compressed pulse width. The deep learning scheme which based on Bayesian Neural Network (BNN) can provide an estimate of uncertainty as a function of pulse width to improve the accuracy of the model. After 20000 iterations, the Mean Square Error (MSE) is reduced to 0.08 in the validation test. Under the experiment, the model realizes an effective predict of the compressed pulse width. With the help of deep learning, we can get more information on the spectrum rather than the center wavelength and spectrum width to predict the compressed pulse width. It should be emphasized that this method will help to avoid unstable pulse output caused by an abnormal spectrum and to improve the operating efficiency of the petawatt laser system.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.