Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery
Ultrafast pulse characterisation is crucial for studying processes that occur at femtosecond timescales and below. Because of this, various methods have been developed to recover a pulse’s electric field profile at these durations, with the frequency-resolved optical gating (FROG) technique being th...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ad9f21 |
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| author | Wallace Jaffray Ziheng Guo Andrea Di Falco Marcello Ferrera |
| author_facet | Wallace Jaffray Ziheng Guo Andrea Di Falco Marcello Ferrera |
| author_sort | Wallace Jaffray |
| collection | DOAJ |
| description | Ultrafast pulse characterisation is crucial for studying processes that occur at femtosecond timescales and below. Because of this, various methods have been developed to recover a pulse’s electric field profile at these durations, with the frequency-resolved optical gating (FROG) technique being the most common. However, this approach is computationally expensive and suffers from limitations in terms of robustness and reliability. In this regard, recent publications have demonstrated that applying machine learning towards ultrafast pulse recovery can alleviate these issues, providing more accurate retrievals. Inspired by these works, we propose an encoder–decoder scheme for a FROG system which exploits dual harmonic generation in low-index thin films. Specifically, we demonstrate enhanced reliability and accuracy of ultrafast pulse recovery when compared to machine learning approaches using second or third harmonic signals independently. As the amount of information used to train each neural network is kept constant, this study demonstrates and benchmarks the technological advantages of contextual information analysis involving multiple nonlinear processes. |
| format | Article |
| id | doaj-art-891a3f31851e45beb8e93021260e4e08 |
| institution | DOAJ |
| issn | 2632-2153 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-891a3f31851e45beb8e93021260e4e082025-08-20T02:55:46ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015404507410.1088/2632-2153/ad9f21Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recoveryWallace Jaffray0https://orcid.org/0000-0001-7992-5193Ziheng Guo1https://orcid.org/0009-0002-2266-8637Andrea Di Falco2https://orcid.org/0000-0002-7338-8785Marcello Ferrera3https://orcid.org/0000-0003-4479-5127Institute of Photonics and Quantum Sciences , Heriot-Watt University, SUPA Edinburgh, Midlothian EH14 4AS, United KingdomSchool of Physics and Astronomy, University of St. Andrews , North Haugh, St Andrews, Fife KY16 9SS, United KingdomSchool of Physics and Astronomy, University of St. Andrews , North Haugh, St Andrews, Fife KY16 9SS, United KingdomInstitute of Photonics and Quantum Sciences , Heriot-Watt University, SUPA Edinburgh, Midlothian EH14 4AS, United KingdomUltrafast pulse characterisation is crucial for studying processes that occur at femtosecond timescales and below. Because of this, various methods have been developed to recover a pulse’s electric field profile at these durations, with the frequency-resolved optical gating (FROG) technique being the most common. However, this approach is computationally expensive and suffers from limitations in terms of robustness and reliability. In this regard, recent publications have demonstrated that applying machine learning towards ultrafast pulse recovery can alleviate these issues, providing more accurate retrievals. Inspired by these works, we propose an encoder–decoder scheme for a FROG system which exploits dual harmonic generation in low-index thin films. Specifically, we demonstrate enhanced reliability and accuracy of ultrafast pulse recovery when compared to machine learning approaches using second or third harmonic signals independently. As the amount of information used to train each neural network is kept constant, this study demonstrates and benchmarks the technological advantages of contextual information analysis involving multiple nonlinear processes.https://doi.org/10.1088/2632-2153/ad9f21photonicsultrafast physicsmachine learning |
| spellingShingle | Wallace Jaffray Ziheng Guo Andrea Di Falco Marcello Ferrera Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery Machine Learning: Science and Technology photonics ultrafast physics machine learning |
| title | Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery |
| title_full | Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery |
| title_fullStr | Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery |
| title_full_unstemmed | Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery |
| title_short | Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery |
| title_sort | machine learning assisted dual harmonic generation frog for enhanced ultrafast pulse recovery |
| topic | photonics ultrafast physics machine learning |
| url | https://doi.org/10.1088/2632-2153/ad9f21 |
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