Najbliższe seminaria

11 XIIBogdan Damski (Uniwersytet Jagielloński, Kraków, Poland)
Massive photons and periodic “charge” oscillations
Gdzie: B-1-46 and MS Teams [ZOA-test], 12:15
Seminarium Zakładowe
Online: [link]
11 XIIRyszard Kukulski (IITiS PAN, Gliwice)
Probabilistic exact unitary inversion
Gdzie: F-1-04 and ZOOM
Online: [link], pass: on request!
Chaos i Informacja Kwantowa
Pokaż pozostałe

[Abstrakt, pełna informacja]

Update

23.06.2023 - Server Software Update

Server software has been updated. Please check if your websites still work, in case of any problems, report them.

Konferencje

06-08.09.2023 - Konferencja "Time Crystals"

More details na stronie konferencji.

27.06-2.07.2022 - 6th Workshop on Algebraic Designs, Hadamard Matrices & Quanta

Więcej szczegółów on conference website.

05-11.09.2021 - Quantum Optics X

Więcej szczegółów na stronie konferencji.

Open Data

Current: /2303.13940

Arxiv link

For the code, see https://github.com/tszoldra/attoDNN

Notebooks with examples:

  1. Dataset and explainability readout Open In Colab
  2. Simple training and evaluation Open In Colab
  3. Performance analysis for all models shown in the paper Open In Colab
  4. Focal averaging example Open In Colab

 

This database contains:

  1. 2-dimensional photoelectron momentum distributions resulting from ionization of Argon target by femtosecond laser pulses:
    1. data_raw/ - results of the simulations and experiments that are in their original format. Details about the data are in the README files.
    2. data_preprocessed/ - data from data_raw that has a uniform numpy format (same momentum grid) with labels and can be used directly for the machine learning part.
  2. models/
    1. models.tar.gz - all 400 pretrained deep convolutional neural network regression models trained in this project (very large)
    2. QProp_Ar_small_sample.npz_SL_0.0__BayesianVGG16__1000.h5 - one example of a model trained on a small dataset, used in the jupyter notebook example 02_training.ipynb
  3. analysis_all_model.pkl - python pickle with benchmarks of all trained models. Used in the 03_evaluation_all_models.ipynb notebook.
  4. explainability.npz - numpy archive with numerical results of the explainability part of the paper. It is used by the 01_dataset_readout.ipynb notebook.

 

If you use the repository, please cite the paper:

@misc{szoldra2023femtosecond,
      title={Femtosecond pulse parameter estimation from photoelectron momenta using machine learning},
      author={Tomasz Szołdra and Marcelo F. Ciappina and Nicholas Werby and Philip H. Bucksbaum and Maciej Lewenstein and Jakub Zakrzewski and Andrew S. Maxwell},
      year={2023},
      eprint={2303.13940},
      archivePrefix={arXiv},
      primaryClass={physics.atom-ph}
}

 

[Parent Directory]
data_preprocessedDIR
data_rawDIR
modelsDIR
analysis_all_models.pkl157MB
explainability.npz91MB