| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 09022 | |
| Number of page(s) | 9 | |
| Section | Artificial Intelligence and Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202429509022 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429509022
Energy Reconstruction with Semi-Supervised Autoencoders for Dual-Phase Time Projection Chambers
Department of Physics and Astronomy, William Marsh Rice University, Houston, Texas, USA.
* e-mail: il11@rice.edu
Published online: 6 May 2024
This paper presents a proof-of-concept semi-supervised autoencoder for the energy reconstruction of scattering particle interactions inside dualphase time projection chambers (TPCs), such as XENONnT. This autoencoder model is trained on simulated XENONnT data and is able to simultaneously reconstruct photosensor array hit patterns and infer the number of electrons in the gas gap, which is proportional to the energy of ionization signals in the TPC. Development plans for this autoencoder model are discussed, including future work in developing a faster simulation technique for dual-phase TPCs.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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