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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.29391 |
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Table of Contents:
- Photomultiplier tubes (PMTs) are widely used in particle and nuclear physics experiments. The reconstruction of PMT waveforms is a fundamental task in these experiments, where accurate extraction of photoelectron (PE) multiplicities and time from the waveform is required for downstream event reconstruction and analysis. In realistic detector environments, PMT waveform reconstruction is complicated by electronic effects such as pileup, charge fluctuations, noise etc., which make precise recovery of physical observables challenging. To address these challenges, we present \phast{}, a machine-learning-based method that reconstructs PE count and time profile simultaneously. The model consists of a shared wave-transformer encoder followed by two dedicated branches: a counting branch for the total PE number prediction, and a time branch employing a count-conditioned query decoder with dynamic query activation. To study the reconstruction performance under controlled conditions, we construct several toy Monte Carlo PMT waveform datasets, including both uniform and mixed fast-slow double-temporal-components configurations. The proposed method demonstrates stable and accurate reconstruction performance across various waveform conditions, achieving high consistency in both PE counting and time reconstruction. These results indicate that architectures combining convolutional feature extraction with query-based transformer decoders provide an effective approach for complex PMT waveform reconstruction tasks.