Femtosecond pulse parameter estimation from photoelectron momenta using machine learning

• URL: http://arxiv.org/abs/2303.13940v2
• Date: Wed, 18 Oct 2023 15:40:39 GMT
• Title: Femtosecond pulse parameter estimation from photoelectron momenta using machine learning
• Authors: Tomasz Szo{\l}dra, Marcelo F. Ciappina, Nicholas Werby, Philip H. Bucksbaum, Maciej Lewenstein, Jakub Zakrzewski, and Andrew S. Maxwell
• Abstract summary: convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we test CNNs on strong-field ionization photoelectron spectra, training on theoretical data sets to invert' experimental data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we test CNNs on strong-field ionization photoelectron spectra, training on theoretical data sets to `invert' experimental data. Pulse characterization is used as a `testing ground', specifically we retrieve the laser intensity, where `traditional' measurements typically lead to 20% uncertainty. We report on crucial data augmentation techniques required to successfully train on theoretical data and return consistent results from experiments, including accounting for detector saturation. The same procedure can be repeated to apply CNNs in a range of scenarios for strong-field ionization. Using a predictive uncertainty estimation, reliable laser intensity uncertainties of a few percent can be extracted, which are consistently lower than those given by traditional techniques. Using interpretability methods can reveal parts of the distribution that are most sensitive to laser intensity, which can be directly associated with holographic interferences. The CNNs employed provide an accurate and convenient ways to extract parameters, and represent a novel interpretational tool for strong-field ionization spectra.

Related papers

• Improving the Precision of CNNs for Magnetic Resonance Spectral Modeling [4.638569496003459]
  Using machine learning to predict MRS-related quantities offers avenues around this problem, but deep learning models bring their own challenges. This work highlights why more comprehensive error characterization is important and how to improve the precision of CNNs for spectral modeling.
  arXiv  Detail & Related papers  (2024-09-10T16:02:12Z)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning [4.633997895806144]
  We consider quantitative analyses of spectral data using laser-induced breakdown spectroscopy. We address the small size of training data available, and the validation of the predictions during inference on unknown data.
  arXiv  Detail & Related papers  (2022-10-07T18:00:09Z)
• Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra [4.698576003197588]
  We use normalizing flows on structured spectral latent spaces to estimate probability densities. We evaluate a method for uncertainty quantification when predicting unobserved state vectors. We demonstrate the capability of this approach on laser-induced breakdown spectroscopy data collected by the Mars rover Curiosity.
  arXiv  Detail & Related papers  (2021-08-17T01:10:29Z)
• Real-time gravitational-wave science with neural posterior estimation [64.67121167063696]
  We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. We analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog. We find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.
  arXiv  Detail & Related papers  (2021-06-23T18:00:05Z)
• Deep Spectral CNN for Laser Induced Breakdown Spectroscopy [11.978306421559587]
  This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to disentangle spectral signals from the sources of sensor uncertainty. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, 'Curiosity'
  arXiv  Detail & Related papers  (2020-12-03T02:23:48Z)
• Spectral Analysis Network for Deep Representation Learning and Image Clustering [53.415803942270685]
  This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis. It can identify the local similarities among images in patch level and thus more robust against occlusion. It can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples.
  arXiv  Detail & Related papers  (2020-09-11T05:07:15Z)
• Deep learning for gravitational-wave data analysis: A resampling white-box approach [62.997667081978825]
  We apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. CNNs were quite precise to detect noise but not sensitive enough to recall GW signals, meaning that CNNs are better for noise reduction than generation of GW triggers.
  arXiv  Detail & Related papers  (2020-09-09T03:28:57Z)
• Complete parameter inference for GW150914 using deep learning [0.0]
  LIGO and Virgo gravitational-wave observatories have detected many exciting events over the past five years. As the rate of detections grows with detector sensitivity, this poses a growing computational challenge for data analysis. We apply deep learning techniques to perform fast likelihood-free Bayesian inference for gravitational waves.
  arXiv  Detail & Related papers  (2020-08-07T18:00:02Z)
• Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
  Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
  arXiv  Detail & Related papers  (2020-07-31T17:25:12Z)
• RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr [60.07531696857743]
  Fine-tuning the deep convolution neural network(CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task. We propose RIFLE - a strategy that deepens backpropagation in transfer learning settings. RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning.
  arXiv  Detail & Related papers  (2020-07-07T11:27:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.