Simultaneous Referencing in Attosecond Transient Spectroscopy via Application of Predictive Neural Networks
Attosecond Transient Spectroscopy (ATS) enables uncovering ultrafast dynamics in gases, liquids, and solids. This work demonstrates an easily applicable, neural-network based approach that achieves a more than three-fold noise reduction, without relying on suppressing correlated noise nor requiring a modified setup [1].
Authors
- L. Gallmann (ORCID: https://orcid.org/0000-0003-3167-8271)
- Sergej Neb (ORCID: https://orcid.org/0000-0002-8684-7442)
- Florence Burri (ORCID: https://orcid.org/0000-0001-9701-6145)
- Marko Hollm (ORCID: https://orcid.org/0009-0002-2714-3223)
Institutions
- ETH Zurich (CH)
Publication Details
- Published
- 2025-06-23
- DOI
- https://doi.org/10.1109/cleo/europe-eqec65582.2025.11110458
- Primary Topic
- Mass Spectrometry Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00