Speaker
Description
Knowledge of the shape and duration of ultrashort laser pulses plays an important role, e.g., in the optimization of high-harmonic generation (HHG), pump-probe spectroscopy, generation of Terahertz radiation [1], and can be an ideal diagnostic tool for lasers systems, be they conventional or novel. Among the current laser pulse characterization methods, the dispersion-scan (d-scan) technique emerges as a robust, inline technique, that has single-shot data acquisition variants. This data comprises of a 2D plot (i.e. d-scan trace) from which numerical routines can be use to extract/retrieve the laser pulse (composed of spectrum and spectral phase). A fast pulse retrieval is desirable, hence in this work we implement and extend the DenseNet [2] neural networks to retrieve d-scan traces (including spectrum and spectral phase) and compare their inference speed with the execution time of optimized conventional retrieval algorithms.
[1] M Kling et al., J. Opt. 27, 013002 (2025)
[2] S Kleinert et al., Opt. Lett. 44, 979-982 (2019)
| Broad physics domain | Optics (Ultrafast Optics) |
|---|---|
| AI/ML technique(s) to be presented | DenseNet |
