Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is the largest liquid scintillator detector in the world, aiming to determine the neutrino mass ordering with an energy resolution of $3\%/\sqrt{E~[\mathrm{MeV}]}$. Accurate analysis of photomultiplier tube (PMT) waveforms is essential for energy resolution. We present a deep learning framework for PMT waveform denoising and reconstruction. Our framework deploys a Transformer-UNet denoising network followed by a Transformer encoder for parameter estimation, to address the challenges of single photoelectron response (SER) calibration under real noise conditions and multi-photoelectron (multi-PE) pile-up. We introduce a simulation-based supervised learning framework which incorporates physics-based pulse models and data-driven noise. A function-space distribution estimation framework calibrates SER characteristics across different PMT individuals. Experiments on the Pan-Asia ContainerD dataset demonstrate that our method achieves the Residual Sum of Squares (RSS) of 2.68~mV$^2$ and Wasserstein distance of 0.61~ns on multi-PE reconstruction, which demonstrates better performance than other existing methods. The framework handles hardware-dependent non-Gaussian electronic noise and baseline drift, outputs physically interpretable parameters with uncertainty estimates, and enables direct integration into maximum likelihood reconstruction pipelines.
| Contribution types | Standard talk (20min + 5min Q/A) |
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