🤖 AI Summary
This work addresses the challenge of resolving individual photoelectrons in photomultiplier tube waveforms when nanosecond-scale multi-photon overlaps occur. To this end, the authors propose a weakly supervised learning framework based on a bidirectional conditional diffusion model that jointly optimizes waveform simulation and photoelectron sequence reconstruction. The method requires only raw waveforms and coarse photoelectron estimates—without ground-truth labels—and achieves high-fidelity reconstruction through bidirectional iterative training. Within the range of 1 to 5 photoelectrons, the approach attains a normalized photoelectron counting resolution of 99% and achieves 80% of the temporal resolution of fully supervised methods, significantly enhancing waveform reconstruction performance for photomultiplier tubes.
📝 Abstract
Photomultiplier tubes (PMTs) are widely employed in particle and nuclear physics experiments. The accuracy of PMT waveform reconstruction directly impacts the detector's spatial and energy resolution. A key challenge arises when multiple photons arrive within a few nanoseconds, making it difficult to resolve individual photoelectrons (PEs). Although supervised deep learning methods have surpassed traditional methods in performance, their practical applicability is limited by the lack of ground-truth PE labels in real data. To address this issue, we propose an innovative weakly supervised waveform simulation and reconstruction approach based on a bidirectional conditional diffusion network framework. The method is fully data-driven and requires only raw waveforms and coarse estimates of PE information as input. It first employs a PE-conditioned diffusion model to simulate realistic waveforms from PE sequences, thereby learning the features of overlapping waveforms. Subsequently, these simulated waveforms are used to train a waveform-conditioned diffusion model to reconstruct the PE sequences from waveforms, reinforcing the learning of features of overlapping waveforms. Through iterative refinement between the two conditional diffusion processes, the model progressively improves reconstruction accuracy. Experimental results demonstrate that the proposed method achieves 99% of the normalized PE-number resolution averaged over 1-5 p.e. and 80% of the timing resolution attained by fully supervised learning.