SPIDERweb: a Neural Network approach to spectral phase interferometry

Fuente: arXiv
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Main Authors: Gianani, Ilaria, Walmsley, Ian A., Barbieri, Marco
Format: Preprint
Published: 2024
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author Gianani, Ilaria
Walmsley, Ian A.
Barbieri, Marco
author_facet Gianani, Ilaria
Walmsley, Ian A.
Barbieri, Marco
contents Reliably characterised pulses are the starting point of any application of ultrafast techniques. Unfortunately, experimental constraints do not always allow optimising the characterisation conditions. This dictates the need for refined analysis methods. Here we show that neutral networks can provide a viable characterisation when applied to data from SPIDER. We have adopted a cascade of convolutional networks, addressing the multiparameter structure of the interferogram with a reasonable computing power. In particular, the necessity of precalibration is reduced, thus pointing towards the introduction of neural networks in more generic arrangements.
format Preprint
id arxiv_https___arxiv_org_abs_2407_02976
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle SPIDERweb: a Neural Network approach to spectral phase interferometry
Gianani, Ilaria
Walmsley, Ian A.
Barbieri, Marco
Optics
Reliably characterised pulses are the starting point of any application of ultrafast techniques. Unfortunately, experimental constraints do not always allow optimising the characterisation conditions. This dictates the need for refined analysis methods. Here we show that neutral networks can provide a viable characterisation when applied to data from SPIDER. We have adopted a cascade of convolutional networks, addressing the multiparameter structure of the interferogram with a reasonable computing power. In particular, the necessity of precalibration is reduced, thus pointing towards the introduction of neural networks in more generic arrangements.
title SPIDERweb: a Neural Network approach to spectral phase interferometry
topic Optics
url https://arxiv.org/abs/2407.02976