Characterization of Deconvolution-Based PMT Waveform Reconstruction Under Large Charge Dynamic Range and Varying Scintillation Time Profiles

Abstract

Photomultiplier tubes (PMTs) are widely used as photon sensors for neutrino and dark matter detection. Accurate charge and time information extracted from PMT waveforms is crucial for event reconstruction. An algorithm based on deconvolution technology was proposed and applied to the reconstruction of PMT waveforms. This study further investigated the reliability of the deconvolution algorithm when handling a large charge dynamic range (0-200 photoelectrons), varying scintillation time profiles, and muon-induced large signals. Monte Carlo data confirmed that the deconvolution algorithm exhibits relatively stable reconstruction performance: the residual non-linearity of charge reconstruction is controlled to approximately 1\% over the range of 0 to 200 photoelectrons for various configurations of undershoots and scintillation time profiles, and the algorithm is capable of handling muon-induced large signals.

Figures (11)

• Figure 1: (a) A typical SPE waveform template. It consists of two components: the main peak and the undershoot. (b) SPE waveforms with different configurations of undershoots. To provide a clearer illustration of the waveform characteristics, only the segment from 0 to 300 ns is shown in the figure, while the full waveform length is 1000 ns. For all three undershoot configurations, the baseline of the SPE waveforms is fully restored by the end of the 1000 ns time window.
• Figure 2: The workflow for PMT waveform simulation (left block diagram) and deconvolution-based reconstruction (right block diagram). In the simulation, true photoelectron hits $u(t)$ are convolved with the single photoelectron (SPE) response $r(t)$ and combined with Gaussian noise $n(t)$ to generate the measured waveform $m(t) = u(t) \otimes r(t) + n(t)$. The measured signal is transformed to the frequency domain via FFT. In the reconstruction, deconvolution is performed in the frequency domain: $U(f) = M(f) \times \mathit{filter}(f) / R_{\rm calib}(f)$, where $\mathit{filter}(f)$ suppresses noise and $R_{\rm calib}(f)$ is the calibrated SPE response. An inverse FFT transforms the result back to the time domain, yielding the reconstructed hits $u_{\text{rec}}(t)$, followed by charge calculation. The short-time Fourier transform may optionally be employed to perform time-frequency analysis, thereby enhancing the identification of pile-up hits.
• Figure 3: The scintillation time profiles for several representative liquid scintillator formulations listed in Table \ref{['table:time properties']}.
• Figure 4: Waveform reconstruction results of point-like events with different undershoot configurations. "Ratio" refers to the ratio of the reconstructed charge to the true charge. The three panels from top to bottom correspond to the undershoot configurations of 1.3%, 6.5%, and 13% respectively. The reconstruction results obtained with the additional use of the short-time Fourier transform in the deconvolution-based reconstruction (denoted as "Rec. FFT+STFT") are consistent with those obtained without it (denoted as "Rec. FFT").
• Figure 5: Waveform reconstruction results of point-like events with different scintillation time profiles. "Ratio" refers to the ratio of the reconstructed charge to the true charge. The deconvolution algorithm demonstrates stable reconstruction performance, and the residual non-linearity of the reconstructed charge can be controlled within 1%.