Deep learning in attosecond metrology
Brunner C, Duensing A, Schroeder C, Mittermair M, Golkov V, Pollanka M, Cremers D, Kienberger R (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Book Volume: 30
Pages Range: 15669-15684
Journal Issue: 9
DOI: 10.1364/OE.452108
Abstract
Time-resolved photoelectron spectroscopy provides a versatile tool for investigating electron dynamics in gaseous, liquid, and solid samples on sub-femtosecond time scales. The extraction of information from spectrograms recorded with the attosecond streak camera remains a difficult challenge. Common algorithms are highly specialized and typically computationally heavy. In this work, we apply deep neural networks to map from streaking traces to near-infrared pulses as well as electron wavepackets and extensively benchmark our results on simulated data. Additionally, we illustrate domain-shift to real-world data. We also attempt to quantify the model predictive uncertainty. Our deep neural networks display competitive retrieval quality and superior tolerance against noisy data conditions, while reducing the computational time by orders of magnitude.
Involved external institutions
Germany (DE)How to cite
APA:
Brunner, C., Duensing, A., Schroeder, C., Mittermair, M., Golkov, V., Pollanka, M.,... Kienberger, R. (2022). Deep learning in attosecond metrology. Optics Express, 30(9), 15669-15684. https://doi.org/10.1364/OE.452108
MLA:
Brunner, Christian, et al. "Deep learning in attosecond metrology." Optics Express 30.9 (2022): 15669-15684.
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