In this section
@article{Hajivassiliou:2023:1367-2630/acf4b8,
author = {Hajivassiliou, G and Kassapis, M and Tisch, JWG},
doi = {1367-2630/acf4b8},
journal = {New Journal of Physics},
title = {Rapid retrieval of femtosecond and attosecond pulses from streaking traces using convolutional neural networks},
url = {http://dx.doi.org/10.1088/1367-2630/acf4b8},
volume = {25},
year = {2023}
}
TY - JOUR
AB - Attosecond streaking is a powerful and versatile technique that allows the full-field characterisation of femtosecond to attosecond optical pulses. It has been instrumental in the verification of attosecond pulse generation and probing of ultrafast dynamics in matter. Recently, machine learning (ML) has been applied to retrieve the fields from streaking data (White and Chang 2019 Opt. Express27 4799; Zhu et al 2020 Sci. Rep.10 5782; Brunner et al 2022 Opt. Express30 15669–84). This offers a number of advantages compared with traditional iterative algorithms, including faster processing and better resilience to noise. Here, we implement a ML approach based on convolutional neural networks and limit the search to physically realistic pulses that can be specified with a small number of parameters. This leads to substantial reductions in both training and retrieval times, enabling near kHz retrieval rates. We examine how the retrieval performance is affected by noise, and for the first time in this context, study the effect of missing data. We show that satisfactory retrievals are still possible with signal to noise ratios as low as 10, and with up to $40\%$ of data missing.
AU - Hajivassiliou,G
AU - Kassapis,M
AU - Tisch,JWG
DO - 1367-2630/acf4b8
PY - 2023///
SN - 1367-2630
TI - Rapid retrieval of femtosecond and attosecond pulses from streaking traces using convolutional neural networks
T2 - New Journal of Physics
UR - http://dx.doi.org/10.1088/1367-2630/acf4b8
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001064288900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR - https://iopscience.iop.org/article/10.1088/1367-2630/acf4b8
UR - http://hdl.handle.net/10044/1/106922
VL - 25
ER -